1156 lines
62 KiB
BibTeX
1156 lines
62 KiB
BibTeX
@article{10.1093/molbev/msy224,
|
||
author = {Flagel, Lex and Brandvain, Yaniv and Schrider, Daniel R},
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||
title = "{The Unreasonable Effectiveness of Convolutional Neural
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||
Networks in Population Genetic Inference}",
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||
journal = {Molecular Biology and Evolution},
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||
volume = 36,
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||
number = 2,
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||
pages = {220-238},
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||
year = 2018,
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||
month = 12,
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||
abstract = "{Population-scale genomic data sets have given researchers
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||
incredible amounts of information from which to infer
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||
evolutionary histories. Concomitant with this flood of data,
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theoretical and methodological advances have sought to extract
|
||
information from genomic sequences to infer demographic events
|
||
such as population size changes and gene flow among closely
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||
related populations/species, construct recombination maps, and
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||
uncover loci underlying recent adaptation. To date, most
|
||
methods make use of only one or a few summaries of the input
|
||
sequences and therefore ignore potentially useful information
|
||
encoded in the data. The most sophisticated of these
|
||
approaches involve likelihood calculations, which require
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||
theoretical advances for each new problem, and often focus on
|
||
a single aspect of the data (e.g., only allele frequency
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||
information) in the interest of mathematical and computational
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||
tractability. Directly interrogating the entirety of the input
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||
sequence data in a likelihood-free manner would thus offer a
|
||
fruitful alternative. Here, we accomplish this by representing
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||
DNA sequence alignments as images and using a class of deep
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||
learning methods called convolutional neural networks (CNNs)
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||
to make population genetic inferences from these images. We
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||
apply CNNs to a number of evolutionary questions and find that
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||
they frequently match or exceed the accuracy of current
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||
methods. Importantly, we show that CNNs perform accurate
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||
evolutionary model selection and parameter estimation, even on
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problems that have not received detailed theoretical
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||
treatments. Thus, when applied to population genetic
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||
alignments, CNNs are capable of outperforming expert-derived
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statistical methods and offer a new path forward in cases
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||
where no likelihood approach exists.}",
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||
issn = {0737-4038},
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||
doi = {10.1093/molbev/msy224},
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||
url = {https://doi.org/10.1093/molbev/msy224},
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||
eprint = {https://academic.oup.com/mbe/article-pdf/36/2/220/27736968/msy224.pdf},
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||
}
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||
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||
@Article{pmid19706884,
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||
Author = "Robins, H. S. and Campregher, P. V. and Srivastava, S. K.
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||
and Wacher, A. and Turtle, C. J. and Kahsai, O. and Riddell,
|
||
S. R. and Warren, E. H. and Carlson, C. S. ",
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||
Title = "{{C}omprehensive assessment of {T}-cell receptor beta-chain
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||
diversity in alphabeta {T} cells}",
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||
Journal = "Blood",
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||
Year = 2009,
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||
Volume = 114,
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||
Number = 19,
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||
Pages = "4099--4107",
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||
Month = "Nov"
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||
}
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||
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||
@article {Nurk2021.05.26.445798,
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||
author = {Nurk, Sergey and Koren, Sergey and Rhie, Arang and
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||
Rautiainen, Mikko and Bzikadze, Andrey V. and Mikheenko, Alla
|
||
and Vollger, Mitchell R. and Altemose, Nicolas and Uralsky,
|
||
Lev and Gershman, Ariel and Aganezov, Sergey and Hoyt,
|
||
Savannah J. and Diekhans, Mark and Logsdon, Glennis A. and
|
||
Alonge, Michael and Antonarakis, Stylianos E. and Borchers,
|
||
Matthew and Bouffard, Gerard G. and Brooks, Shelise Y. and
|
||
Caldas, Gina V. and Cheng, Haoyu and Chin, Chen-Shan and Chow,
|
||
William and de Lima, Leonardo G. and Dishuck, Philip C. and
|
||
Durbin, Richard and Dvorkina, Tatiana and Fiddes, Ian T. and
|
||
Formenti, Giulio and Fulton, Robert S. and Fungtammasan,
|
||
Arkarachai and Garrison, Erik and Grady, Patrick G.S. and
|
||
Graves-Lindsay, Tina A. and Hall, Ira M. and Hansen, Nancy F.
|
||
and Hartley, Gabrielle A. and Haukness, Marina and Howe,
|
||
Kerstin and Hunkapiller, Michael W. and Jain, Chirag and Jain,
|
||
Miten and Jarvis, Erich D. and Kerpedjiev, Peter and Kirsche,
|
||
Melanie and Kolmogorov, Mikhail and Korlach, Jonas and
|
||
Kremitzki, Milinn and Li, Heng and Maduro, Valerie V. and
|
||
Marschall, Tobias and McCartney, Ann M. and McDaniel, Jennifer
|
||
and Miller, Danny E. and Mullikin, James C. and Myers, Eugene
|
||
W. and Olson, Nathan D. and Paten, Benedict and Peluso, Paul
|
||
and Pevzner, Pavel A. and Porubsky, David and Potapova, Tamara
|
||
and Rogaev, Evgeny I. and Rosenfeld, Jeffrey A. and Salzberg,
|
||
Steven L. and Schneider, Valerie A. and Sedlazeck, Fritz J.
|
||
and Shafin, Kishwar and Shew, Colin J. and Shumate, Alaina and
|
||
Sims, Yumi and Smit, Arian F. A. and Soto, Daniela C. and
|
||
Sovi{\'c}, Ivan and Storer, Jessica M. and Streets, Aaron and
|
||
Sullivan, Beth A. and Thibaud-Nissen, Fran{\c c}oise and
|
||
Torrance, James and Wagner, Justin and Walenz, Brian P. and
|
||
Wenger, Aaron and Wood, Jonathan M. D. and Xiao, Chunlin and
|
||
Yan, Stephanie M. and Young, Alice C. and Zarate, Samantha and
|
||
Surti, Urvashi and McCoy, Rajiv C. and Dennis, Megan Y. and
|
||
Alexandrov, Ivan A. and Gerton, Jennifer L. and
|
||
O{\textquoteright}Neill, Rachel J. and Timp, Winston and Zook,
|
||
Justin M. and Schatz, Michael C. and Eichler, Evan E. and
|
||
Miga, Karen H. and Phillippy, Adam M.},
|
||
title = {The complete sequence of a human genome},
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||
elocation-id = {2021.05.26.445798},
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year = 2021,
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||
doi = {10.1101/2021.05.26.445798},
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publisher = {Cold Spring Harbor Laboratory},
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||
abstract = {In 2001, Celera Genomics and the International Human Genome
|
||
Sequencing Consortium published their initial drafts of the
|
||
human genome, which revolutionized the field of genomics.
|
||
While these drafts and the updates that followed effectively
|
||
covered the euchromatic fraction of the genome, the
|
||
heterochromatin and many other complex regions were left
|
||
unfinished or erroneous. Addressing this remaining 8\% of the
|
||
genome, the Telomere-to-Telomere (T2T) Consortium has finished
|
||
the first truly complete 3.055 billion base pair (bp) sequence
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||
of a human genome, representing the largest improvement to the
|
||
human reference genome since its initial release. The new
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||
T2T-CHM13 reference includes gapless assemblies for all 22
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||
autosomes plus Chromosome X, corrects numerous errors, and
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introduces nearly 200 million bp of novel sequence containing
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2,226 paralogous gene copies, 115 of which are predicted to be
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protein coding. The newly completed regions include all
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centromeric satellite arrays and the short arms of all five
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||
acrocentric chromosomes, unlocking these complex regions of
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the genome to variational and functional studies for the first
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time.Competing Interest StatementAF and CSC are employees of
|
||
DNAnexus; IS, JK, MWH, PP, and AW are employees of Pacific
|
||
Biosciences; FJS has received travel funds to speak at events
|
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hosted by Pacific Biosciences; SK and FJS have received travel
|
||
funds to speak at events hosted by Oxford Nanopore
|
||
Technologies. WT has licensed two patents to Oxford Nanopore
|
||
Technologies (US 8748091 and 8394584).},
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URL = {https://www.biorxiv.org/content/early/2021/05/27/2021.05.26.445798},
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eprint = {https://www.biorxiv.org/content/early/2021/05/27/2021.05.26.445798.full.pdf},
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journal = {bioRxiv}
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||
}
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@ARTICLE{10.3389/fgene.2020.00900,
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||
AUTHOR = {Wang, Luotong and Qu, Li and Yang, Longshu and Wang, Yiying
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||
and Zhu, Huaiqiu},
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||
TITLE = {NanoReviser: An Error-Correction Tool for Nanopore
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||
Sequencing Based on a Deep Learning Algorithm},
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JOURNAL = {Frontiers in Genetics},
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VOLUME = 11,
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PAGES = 900,
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YEAR = 2020,
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URL = {https://www.frontiersin.org/article/10.3389/fgene.2020.00900},
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DOI = {10.3389/fgene.2020.00900},
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||
ISSN = {1664-8021},
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||
ABSTRACT = {Nanopore sequencing is regarded as one of the most
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||
promising third-generation sequencing (TGS) technologies.
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||
Since 2014, Oxford Nanopore Technologies (ONT) has developed a
|
||
series of devices based on nanopore sequencing to produce very
|
||
long reads, with an expected impact on genomics. However, the
|
||
nanopore sequencing reads are susceptible to a fairly high
|
||
error rate owing to the difficulty in identifying the DNA
|
||
bases from the complex electrical signals. Although several
|
||
basecalling tools have been developed for nanopore sequencing
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||
over the past years, it is still challenging to correct the
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||
sequences after applying the basecalling procedure. In this
|
||
study, we developed an open-source DNA basecalling reviser,
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||
NanoReviser, based on a deep learning algorithm to correct the
|
||
basecalling errors introduced by current basecallers provided
|
||
by default. In our module, we re-segmented the raw electrical
|
||
signals based on the basecalled sequences provided by the
|
||
default basecallers. By employing convolution neural networks
|
||
(CNNs) and bidirectional long short-term memory (Bi-LSTM)
|
||
networks, we took advantage of the information from the raw
|
||
electrical signals and the basecalled sequences from the
|
||
basecallers. Our results showed NanoReviser, as a
|
||
post-basecalling reviser, significantly improving the
|
||
basecalling quality. After being trained on standard ONT
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||
sequencing reads from public E. coli and human NA12878
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||
datasets, NanoReviser reduced the sequencing error rate by
|
||
over 5% for both the E. coli dataset and the human dataset.
|
||
The performance of NanoReviser was found to be better than
|
||
those of all current basecalling tools. Furthermore, we
|
||
analyzed the modified bases of the E. coli dataset and added
|
||
the methylation information to train our module. With the
|
||
methylation annotation, NanoReviser reduced the error rate by
|
||
7% for the E. coli dataset and specifically reduced the error
|
||
rate by over 10% for the regions of the sequence rich in
|
||
methylated bases. To the best of our knowledge, NanoReviser is
|
||
the first post-processing tool after basecalling to accurately
|
||
correct the nanopore sequences without the time-consuming
|
||
procedure of building the consensus sequence. The NanoReviser
|
||
package is freely available at <ext-link ext-link-type="uri"
|
||
xlink:href="https://github.com/pkubioinformatics/NanoReviser"
|
||
xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/pkubioinformatics/NanoReviser</ext-link>.}
|
||
}
|
||
|
||
@article{HEATHER20161,
|
||
title = {The sequence of sequencers: The history of sequencing DNA},
|
||
journal = {Genomics},
|
||
volume = 107,
|
||
number = 1,
|
||
pages = {1-8},
|
||
year = 2016,
|
||
issn = {0888-7543},
|
||
doi = {https://doi.org/10.1016/j.ygeno.2015.11.003},
|
||
url = {https://www.sciencedirect.com/science/article/pii/S0888754315300410},
|
||
author = {James M. Heather and Benjamin Chain},
|
||
keywords = {DNA, RNA, Sequencing, Sequencer, History},
|
||
abstract = {Determining the order of nucleic acid residues in
|
||
biological samples is an integral component of a wide variety
|
||
of research applications. Over the last fifty years large
|
||
numbers of researchers have applied themselves to the
|
||
production of techniques and technologies to facilitate this
|
||
feat, sequencing DNA and RNA molecules. This time-scale has
|
||
witnessed tremendous changes, moving from sequencing short
|
||
oligonucleotides to millions of bases, from struggling towards
|
||
the deduction of the coding sequence of a single gene to rapid
|
||
and widely available whole genome sequencing. This article
|
||
traverses those years, iterating through the different
|
||
generations of sequencing technology, highlighting some of the
|
||
key discoveries, researchers, and sequences along the way.}
|
||
}
|
||
|
||
|
||
|
||
@Article{vanDijk2014,
|
||
author = {van Dijk, Erwin L. and Auger, H{\'e}l{\`e}ne and
|
||
Jaszczyszyn, Yan and Thermes, Claude},
|
||
title = {Ten years of next-generation sequencing technology},
|
||
journal = {Trends in Genetics},
|
||
year = 2014,
|
||
month = {Sep},
|
||
day = 01,
|
||
publisher = {Elsevier},
|
||
volume = 30,
|
||
number = 9,
|
||
pages = {418-426},
|
||
issn = {0168-9525},
|
||
doi = {10.1016/j.tig.2014.07.001},
|
||
url = {https://doi.org/10.1016/j.tig.2014.07.001}
|
||
}
|
||
|
||
@article {Sanger5463,
|
||
author = {Sanger, F. and Nicklen, S. and Coulson, A. R.},
|
||
title = {DNA sequencing with chain-terminating inhibitors},
|
||
volume = 74,
|
||
number = 12,
|
||
pages = {5463--5467},
|
||
year = 1977,
|
||
doi = {10.1073/pnas.74.12.5463},
|
||
publisher = {National Academy of Sciences},
|
||
abstract = {A new method for determining nucleotide sequences in DNA is
|
||
described. It is similar to the {\textquotedblleft}plus and
|
||
minus{\textquotedblright} method [Sanger, F. \& Coulson,
|
||
A. R. (1975) J. Mol. Biol. 94, 441-448] but makes use of the
|
||
2',3'-dideoxy and arabinonucleoside analogues of the normal
|
||
deoxynucleoside triphosphates, which act as specific
|
||
chain-terminating inhibitors of DNA polymerase. The technique
|
||
has been applied to the DNA of bacteriophage ϕX174 and is more
|
||
rapid and more accurate than either the plus or the minus
|
||
method.},
|
||
issn = {0027-8424},
|
||
URL = {https://www.pnas.org/content/74/12/5463},
|
||
eprint = {https://www.pnas.org/content/74/12/5463.full.pdf},
|
||
journal = {Proceedings of the National Academy of Sciences}
|
||
}
|
||
|
||
|
||
|
||
@Article{InternationalHumanGenomeSequencingConsortium2004,
|
||
author = {Consortium, International Human Genome Sequencing},
|
||
title = {Finishing the euchromatic sequence of the human genome},
|
||
journal = {Nature},
|
||
year = 2004,
|
||
month = {Oct},
|
||
day = 01,
|
||
volume = 431,
|
||
number = 7011,
|
||
pages = {931-945},
|
||
abstract = {The sequence of the human genome encodes the genetic
|
||
instructions for human physiology, as well as rich information
|
||
about human evolution. In 2001, the International Human Genome
|
||
Sequencing Consortium reported a draft sequence of the
|
||
euchromatic portion of the human genome. Since then, the
|
||
international collaboration has worked to convert this draft
|
||
into a genome sequence with high accuracy and nearly complete
|
||
coverage. Here, we report the result of this finishing
|
||
process. The current genome sequence (Build 35) contains 2.85
|
||
billion nucleotides interrupted by only 341 gaps. It covers
|
||
∼99{\%} of the euchromatic genome and is accurate to an error
|
||
rate of ∼1 event per 100,000 bases. Many of the remaining
|
||
euchromatic gaps are associated with segmental duplications
|
||
and will require focused work with new methods. The
|
||
near-complete sequence, the first for a vertebrate, greatly
|
||
improves the precision of biological analyses of the human
|
||
genome including studies of gene number, birth and death.
|
||
Notably, the human genome seems to encode only 20,000--25,000
|
||
protein-coding genes. The genome sequence reported here should
|
||
serve as a firm foundation for biomedical research in the
|
||
decades ahead.},
|
||
issn = {1476-4687},
|
||
doi = {10.1038/nature03001},
|
||
url = {https://doi.org/10.1038/nature03001}
|
||
}
|
||
|
||
|
||
|
||
@Article{Schloss2008,
|
||
author = {Schloss, Jeffery A.},
|
||
title = {How to get genomes at one ten-thousandth the cost},
|
||
journal = {Nature Biotechnology},
|
||
year = 2008,
|
||
month = {Oct},
|
||
day = 01,
|
||
volume = 26,
|
||
number = 10,
|
||
pages = {1113-1115},
|
||
abstract = {The NHGRI's Advanced DNA Sequencing Technology program is
|
||
spearheading the development of platforms that will bring
|
||
routine whole-genome sequencing closer to reality.},
|
||
issn = {1546-1696},
|
||
doi = {10.1038/nbt1008-1113},
|
||
url = {https://doi.org/10.1038/nbt1008-1113}
|
||
}
|
||
|
||
@Article{Shugay2014,
|
||
author = {Shugay, Mikhail and Britanova, Olga V. and Merzlyak,
|
||
Ekaterina M. and Turchaninova, Maria A. and Mamedov, Ilgar Z.
|
||
and Tuganbaev, Timur R. and Bolotin, Dmitriy A. and
|
||
Staroverov, Dmitry B. and Putintseva, Ekaterina V. and
|
||
Plevova, Karla and Linnemann, Carsten and Shagin, Dmitriy and
|
||
Pospisilova, Sarka and Lukyanov, Sergey and Schumacher, Ton N.
|
||
and Chudakov, Dmitriy M.},
|
||
title = {Towards error-free profiling of immune repertoires},
|
||
journal = {Nature Methods},
|
||
year = 2014,
|
||
month = {Jun},
|
||
day = 01,
|
||
volume = 11,
|
||
number = 6,
|
||
pages = {653-655},
|
||
abstract = {A two-step error correction process for high
|
||
throughput--sequenced T- and B-cell receptors allows the
|
||
elimination of most errors while not diminishing the natural
|
||
complexity of the repertoires.},
|
||
issn = {1548-7105},
|
||
doi = {10.1038/nmeth.2960},
|
||
url = {https://doi.org/10.1038/nmeth.2960}
|
||
}
|
||
|
||
@Article{Ma2019,
|
||
author = {Ma, Xiaotu and Shao, Ying and Tian, Liqing and Flasch,
|
||
Diane A. and Mulder, Heather L. and Edmonson, Michael N. and
|
||
Liu, Yu and Chen, Xiang and Newman, Scott and Nakitandwe, Joy
|
||
and Li, Yongjin and Li, Benshang and Shen, Shuhong and Wang,
|
||
Zhaoming and Shurtleff, Sheila and Robison, Leslie L. and
|
||
Levy, Shawn and Easton, John and Zhang, Jinghui},
|
||
title = {Analysis of error profiles in deep next-generation
|
||
sequencing data},
|
||
journal = {Genome Biology},
|
||
year = 2019,
|
||
month = {Mar},
|
||
day = 14,
|
||
volume = 20,
|
||
number = 1,
|
||
pages = 50,
|
||
abstract = {Sequencing errors are key confounding factors for detecting
|
||
low-frequency genetic variants that are important for cancer
|
||
molecular diagnosis, treatment, and surveillance using deep
|
||
next-generation sequencing (NGS). However, there is a lack of
|
||
comprehensive understanding of errors introduced at various
|
||
steps of a conventional NGS workflow, such as sample handling,
|
||
library preparation, PCR enrichment, and sequencing. In this
|
||
study, we use current NGS technology to systematically
|
||
investigate these questions.},
|
||
issn = {1474-760X},
|
||
doi = {10.1186/s13059-019-1659-6},
|
||
}
|
||
|
||
@mastersthesis{BenítezCantos-Master,
|
||
author = "María Soledad Benítez Cantos",
|
||
title = "Análisis de repertorios de receptores de células T a partir de datos de secuenciación masiva",
|
||
school = "Universidad de Granada",
|
||
year = "2019",
|
||
month = "{Jul}",
|
||
}
|
||
|
||
@inbook{abbas_lichtman_pillai_2017,
|
||
place = {Philadelphia, PA},
|
||
edition = {9th},
|
||
booktitle = {Cellular and molecular immunology},
|
||
publisher = {Elsevier},
|
||
author = {Abbas, Abul K. and Lichtman, Andrew H. and Pillai, Shiv},
|
||
year = 2017,
|
||
pages = 204
|
||
}
|
||
|
||
|
||
|
||
@Article{CRICK1970,
|
||
author = {Crick, Francis},
|
||
title = {Central Dogma of Molecular Biology},
|
||
journal = {Nature},
|
||
year = 1970,
|
||
month = {Aug},
|
||
day = 01,
|
||
volume = 227,
|
||
number = 5258,
|
||
pages = {561-563},
|
||
abstract = {The central dogma of molecular biology deals with the
|
||
detailed residue-by-residue transfer of sequential
|
||
information. It states that such information cannot be
|
||
transferred from protein to either protein or nucleic acid.},
|
||
issn = {1476-4687},
|
||
doi = {10.1038/227561a0},
|
||
url = {https://doi.org/10.1038/227561a0}
|
||
}
|
||
|
||
@Article{Salk2018,
|
||
author = {Salk, Jesse J. and Schmitt, Michael W. and Loeb, Lawrence
|
||
A.},
|
||
title = {Enhancing the accuracy of next-generation sequencing for
|
||
detecting rare and subclonal mutations},
|
||
journal = {Nature Reviews Genetics},
|
||
year = 2018,
|
||
month = {May},
|
||
day = 01,
|
||
volume = 19,
|
||
number = 5,
|
||
pages = {269-285},
|
||
abstract = {The ability to identify low-frequency genetic variants
|
||
among heterogeneous populations of cells or DNA molecules is
|
||
important in many fields of basic science, clinical medicine
|
||
and other applications, yet current high-throughput DNA
|
||
sequencing technologies have an error rate between 1 per 100
|
||
and 1 per 1,000 base pairs sequenced, which obscures their
|
||
presence below this level.As next-generation sequencing
|
||
technologies evolved over the decade, throughput has improved
|
||
markedly, but raw accuracy has remained generally unchanged.
|
||
Researchers with a need for high accuracy developed data
|
||
filtering methods and incremental biochemical improvements
|
||
that modestly improve low-frequency variant detection, but
|
||
background errors remain limiting in many fields.The most
|
||
profoundly impactful means for reducing errors, first
|
||
developed approximately 7 years ago, has been the concept of
|
||
single-molecule consensus sequencing. This entails redundant
|
||
sequencing of multiple copies of a given specific DNA molecule
|
||
and discounting of variants that are not present in all or
|
||
most of the copies as likely errors.Consensus sequencing can
|
||
be achieved by labelling each molecule with a unique molecular
|
||
barcode before generating copies, which allows subsequent
|
||
comparison of these copies or schemes whereby copies are
|
||
physically joined and sequenced together. Because of
|
||
trade-offs in cost, time and accuracy, no single method is
|
||
optimal for every application, and each method should be
|
||
considered on a case-by-case basis.Major applications for
|
||
high-accuracy DNA sequencing include non-invasive cancer
|
||
diagnostics, cancer screening, early detection of cancer
|
||
relapse or impending drug resistance, infectious disease
|
||
applications, prenatal diagnostics, forensics and mutagenesis
|
||
assessment.Future advances in ultra-high-accuracy sequencing
|
||
are likely to be driven by an emerging generation of
|
||
single-molecule sequencers, particularly those that allow
|
||
independent sequence comparison of both strands of native DNA
|
||
duplexes.},
|
||
issn = {1471-0064},
|
||
doi = {10.1038/nrg.2017.117},
|
||
url = {https://doi.org/10.1038/nrg.2017.117}
|
||
}
|
||
|
||
@book{book:lehninger,
|
||
title = {Lehninger-Principles of Biochemistry},
|
||
author = {Albert Lehninger, David L. Nelson, Michael M. Cox},
|
||
publisher = {W. H. Freeman},
|
||
isbn = {9781429224161,1429224169},
|
||
year = 2008,
|
||
edition = {5th Edition},
|
||
pages = 276
|
||
}
|
||
|
||
@inproceedings{crick1958protein,
|
||
title = {On protein synthesis},
|
||
author = {Crick, Francis HC},
|
||
booktitle = {Symp Soc Exp Biol},
|
||
volume = 12,
|
||
number = {138-63},
|
||
pages = 8,
|
||
year = 1958
|
||
}
|
||
|
||
@article{10.1093/bioinformatics/btg109,
|
||
author = {Lee, Christopher},
|
||
title = "{Generating consensus sequences from partial order multiple
|
||
sequence alignment graphs}",
|
||
journal = {Bioinformatics},
|
||
volume = 19,
|
||
number = 8,
|
||
pages = {999-1008},
|
||
year = 2003,
|
||
month = 05,
|
||
abstract = "{Motivation: Consensus sequence generation is important in
|
||
many kinds of sequence analysis ranging from sequence assembly
|
||
to profile-based iterative search methods. However, how can a
|
||
consensus be constructed when its inherent assumption—that the
|
||
aligned sequences form a single linear consensus—is not
|
||
true?Results: Partial Order Alignment (POA) enables
|
||
construction and analysis of multiple sequence alignments as
|
||
directed acyclic graphs containing complex branching
|
||
structure. Here we present a dynamic programming algorithm
|
||
(heaviest\_bundle) for generating multiple consensus sequences
|
||
from such complex alignments. The number and relationships of
|
||
these consensus sequences reveals the degree of structural
|
||
complexity of the source alignment. This is a powerful and
|
||
general approach for analyzing and visualizing complex
|
||
alignment structures, and can be applied to any alignment. We
|
||
illustrate its value for analyzing expressed sequence
|
||
alignments to detect alternative splicing, reconstruct full
|
||
length mRNA isoform sequences from EST fragments, and separate
|
||
paralog mixtures that can cause incorrect SNP
|
||
predictions.Availability: The heaviest\_bundle source code is
|
||
available at http://www.bioinformatics.ucla.edu/poaContact:
|
||
leec@mbi.ucla.edu*To whom correspondence should be
|
||
addressed.}",
|
||
issn = {1367-4803},
|
||
doi = {10.1093/bioinformatics/btg109},
|
||
url = {https://doi.org/10.1093/bioinformatics/btg109},
|
||
eprint = {https://academic.oup.com/bioinformatics/article-pdf/19/8/999/642375/btg109.pdf},
|
||
}
|
||
|
||
@Article{Nagar2013,
|
||
author = {Nagar, Anurag and Hahsler, Michael},
|
||
title = {Fast discovery and visualization of conserved regions in
|
||
DNA sequences using quasi-alignment},
|
||
journal = {BMC Bioinformatics},
|
||
year = 2013,
|
||
month = {Sep},
|
||
day = 13,
|
||
volume = 14,
|
||
number = 11,
|
||
pages = {S2},
|
||
abstract = {Next Generation Sequencing techniques are producing
|
||
enormous amounts of biological sequence data and analysis
|
||
becomes a major computational problem. Currently, most
|
||
analysis, especially the identification of conserved regions,
|
||
relies heavily on Multiple Sequence Alignment and its various
|
||
heuristics such as progressive alignment, whose run time grows
|
||
with the square of the number and the length of the aligned
|
||
sequences and requires significant computational resources. In
|
||
this work, we present a method to efficiently discover regions
|
||
of high similarity across multiple sequences without
|
||
performing expensive sequence alignment. The method is based
|
||
on approximating edit distance between segments of sequences
|
||
using p-mer frequency counts. Then, efficient high-throughput
|
||
data stream clustering is used to group highly similar
|
||
segments into so called quasi-alignments. Quasi-alignments
|
||
have numerous applications such as identifying species and
|
||
their taxonomic class from sequences, comparing sequences for
|
||
similarities, and, as in this paper, discovering conserved
|
||
regions across related sequences.},
|
||
issn = {1471-2105},
|
||
doi = {10.1186/1471-2105-14-S11-S2},
|
||
url = {https://doi.org/10.1186/1471-2105-14-S11-S2}
|
||
}
|
||
|
||
@book{book:771224,
|
||
title = {Artificial Intelligence: A Modern Approach},
|
||
author = {Stuart Russell, Peter Norvig},
|
||
publisher = {Prentice Hall},
|
||
isbn = {0136042597, 9780136042594},
|
||
year = 2010,
|
||
series = {Prentice Hall Series in Artificial Intelligence},
|
||
edition = {3rd},
|
||
pages = {38-45, 48-49, 55-56}
|
||
}
|
||
|
||
@article{McCarthy_Minsky_Rochester_Shannon_2006,
|
||
title = {A Proposal for the Dartmouth Summer Research Project on
|
||
Artificial Intelligence, August 31, 1955},
|
||
volume = 27,
|
||
url = {https://ojs.aaai.org/index.php/aimagazine/article/view/1904},
|
||
DOI = {10.1609/aimag.v27i4.1904},
|
||
abstractNote = {The 1956 Dartmouth summer research project on artificial
|
||
intelligence was initiated by this August 31, 1955 proposal,
|
||
authored by John McCarthy, Marvin Minsky, Nathaniel Rochester,
|
||
and Claude Shannon. The original typescript consisted of 17
|
||
pages plus a title page. Copies of the typescript are housed
|
||
in the archives at Dartmouth College and Stanford University.
|
||
The first 5 papers state the proposal, and the remaining pages
|
||
give qualifications and interests of the four who proposed the
|
||
study. In the interest of brevity, this article reproduces
|
||
only the proposal itself, along with the short
|
||
autobiographical statements of the proposers.},
|
||
number = 4,
|
||
journal = {AI Magazine},
|
||
author = {McCarthy, John and Minsky, Marvin L. and Rochester,
|
||
Nathaniel and Shannon, Claude E.},
|
||
year = 2006,
|
||
month = {Dec.},
|
||
pages = 12
|
||
}
|
||
|
||
@book{book:80129,
|
||
title = {Computational Intelligence. An Introduction},
|
||
author = {Andries P. Engelbrecht},
|
||
publisher = {Wiley},
|
||
isbn = {9780470035610,0470035617},
|
||
year = 2007,
|
||
edition = 2,
|
||
pages = {39-40}
|
||
}
|
||
|
||
@Inbook{Zou2009,
|
||
author = "Zou, Jinming and Han, Yi and So, Sung-Sau",
|
||
editor = "Livingstone, David J.",
|
||
title = "Overview of Artificial Neural Networks",
|
||
bookTitle = "Artificial Neural Networks: Methods and Applications",
|
||
year = 2009,
|
||
publisher = "Humana Press",
|
||
address = "Totowa, NJ",
|
||
pages = "14--22",
|
||
abstract = "The artificial neural network (ANN), or simply neural
|
||
network, is a machine learning method evolved from the idea of
|
||
simulating the human brain. The data explosion in modern drug
|
||
discovery research requires sophisticated analysis methods to
|
||
uncover the hidden causal relationships between single or
|
||
multiple responses and a large set of properties. The ANN is
|
||
one of many versatile tools to meet the demand in drug
|
||
discovery modeling. Compared to a traditional regression
|
||
approach, the ANN is capable of modeling complex nonlinear
|
||
relationships. The ANN also has excellent fault tolerance and
|
||
is fast and highly scalable with parallel processing. This
|
||
chapter introduces the background of ANN development and
|
||
outlines the basic concepts crucially important for
|
||
understanding more sophisticated ANN. Several commonly used
|
||
learning methods and network setups are discussed briefly at
|
||
the end of the chapter.",
|
||
isbn = "978-1-60327-101-1",
|
||
doi = "10.1007/978-1-60327-101-1_2",
|
||
url = "https://doi.org/10.1007/978-1-60327-101-1_2"
|
||
}
|
||
|
||
@book{book:2610592,
|
||
title = {Principles of artificial neural networks},
|
||
author = {Graupe, Daniel},
|
||
publisher = {World Scientific Publ},
|
||
isbn = {9789814522731,9814522732},
|
||
year = 2013,
|
||
edition = {3. ed},
|
||
pages = {28-31}
|
||
}
|
||
|
||
@Article{Cireşan2010,
|
||
author = {Cire{\c{s}}an, Dan Claudiu and Meier, Ueli and Gambardella,
|
||
Luca Maria and Schmidhuber, J{\"u}rgen},
|
||
title = {Deep, Big, Simple Neural Nets for Handwritten Digit
|
||
Recognition},
|
||
journal = {Neural Computation},
|
||
year = 2010,
|
||
month = {Dec},
|
||
day = 01,
|
||
volume = 22,
|
||
number = 12,
|
||
pages = {3207-3220},
|
||
abstract = {Good old online backpropagation for plain multilayer
|
||
perceptrons yields a very low 0.35{\%} error rate on the MNIST
|
||
handwritten digits benchmark. All we need to achieve this best
|
||
result so far are many hidden layers, many neurons per layer,
|
||
numerous deformed training images to avoid overfitting, and
|
||
graphics cards to greatly speed up learning.},
|
||
issn = {0899-7667},
|
||
doi = {10.1162/NECO_a_00052},
|
||
url = {https://doi.org/10.1162/NECO_a_00052}
|
||
}
|
||
|
||
|
||
|
||
@Article{Rumelhart1986,
|
||
author = {Rumelhart, David E. and Hinton, Geoffrey E. and Williams,
|
||
Ronald J.},
|
||
title = {Learning representations by back-propagating errors},
|
||
journal = {Nature},
|
||
year = 1986,
|
||
month = {Oct},
|
||
day = 01,
|
||
volume = 323,
|
||
number = 6088,
|
||
pages = {533-536},
|
||
abstract = {We describe a new learning procedure, back-propagation, for
|
||
networks of neurone-like units. The procedure repeatedly
|
||
adjusts the weights of the connections in the network so as to
|
||
minimize a measure of the difference between the actual output
|
||
vector of the net and the desired output vector. As a result
|
||
of the weight adjustments, internal `hidden' units which are
|
||
not part of the input or output come to represent important
|
||
features of the task domain, and the regularities in the task
|
||
are captured by the interactions of these units. The ability
|
||
to create useful new features distinguishes back-propagation
|
||
from earlier, simpler methods such as the
|
||
perceptron-convergence procedure1.},
|
||
issn = {1476-4687},
|
||
doi = {10.1038/323533a0},
|
||
url = {https://doi.org/10.1038/323533a0}
|
||
}
|
||
|
||
@book{book:2530718,
|
||
title = {Machine Learning Refined: Foundations, Algorithms, and
|
||
Applications},
|
||
author = {Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos},
|
||
publisher = {Cambridge University Press},
|
||
isbn = {1108480721,9781108480727},
|
||
year = 2020,
|
||
edition = 2
|
||
}
|
||
|
||
@article{ruder2016overview,
|
||
title = {An overview of gradient descent optimization algorithms},
|
||
author = {Ruder, Sebastian},
|
||
journal = {arXiv preprint arXiv:1609.04747},
|
||
year = 2016
|
||
}
|
||
|
||
@article{DBLP:journals/corr/WangRX17,
|
||
author = {Haohan Wang and Bhiksha Raj and Eric P. Xing},
|
||
title = {On the Origin of Deep Learning},
|
||
journal = {CoRR},
|
||
volume = {abs/1702.07800},
|
||
year = 2017,
|
||
url = {http://arxiv.org/abs/1702.07800},
|
||
archivePrefix = {arXiv},
|
||
eprint = {1702.07800},
|
||
timestamp = {Mon, 13 Aug 2018 16:46:19 +0200},
|
||
biburl = {https://dblp.org/rec/journals/corr/WangRX17.bib},
|
||
bibsource = {dblp computer science bibliography, https://dblp.org}
|
||
}
|
||
|
||
@Inbook{Can2014,
|
||
author = "Can, Tolga",
|
||
editor = "Yousef, Malik and Allmer, Jens",
|
||
title = "Introduction to Bioinformatics",
|
||
bookTitle = "miRNomics: MicroRNA Biology and Computational Analysis",
|
||
year = 2014,
|
||
publisher = "Humana Press",
|
||
address = "Totowa, NJ",
|
||
pages = "51--71",
|
||
abstract = "Bioinformatics is an interdisciplinary field mainly
|
||
involving molecular biology and genetics, computer science,
|
||
mathematics, and statistics. Data intensive, large-scale
|
||
biological problems are addressed from a computational point
|
||
of view. The most common problems are modeling biological
|
||
processes at the molecular level and making inferences from
|
||
collected data. A bioinformatics solution usually involves the
|
||
following steps:Collect statistics from biological data.Build
|
||
a computational model.Solve a computational modeling
|
||
problem.Test and evaluate a computational algorithm.",
|
||
isbn = "978-1-62703-748-8",
|
||
doi = "10.1007/978-1-62703-748-8_4",
|
||
url = "https://doi.org/10.1007/978-1-62703-748-8_4"
|
||
}
|
||
|
||
|
||
|
||
@Article{Hagen2000,
|
||
author = {Hagen, Joel B.},
|
||
title = {The origins of bioinformatics},
|
||
journal = {Nature Reviews Genetics},
|
||
year = 2000,
|
||
month = {Dec},
|
||
day = 01,
|
||
volume = 1,
|
||
number = 3,
|
||
pages = {231-236},
|
||
abstract = {Bioinformatics is often described as being in its infancy,
|
||
but computers emerged as important tools in molecular biology
|
||
during the early 1960s. A decade before DNA sequencing became
|
||
feasible, computational biologists focused on the rapidly
|
||
accumulating data from protein biochemistry. Without the
|
||
benefits of supercomputers or computer networks, these
|
||
scientists laid important conceptual and technical foundations
|
||
for bioinformatics today.},
|
||
issn = {1471-0064},
|
||
doi = {10.1038/35042090},
|
||
url = {https://doi.org/10.1038/35042090}
|
||
}
|
||
|
||
@article{doi:10.1146/annurev-genom-090413-025358,
|
||
author = {Reinert, Knut and Langmead, Ben and Weese, David and Evers,
|
||
Dirk J.},
|
||
title = {Alignment of Next-Generation Sequencing Reads},
|
||
journal = {Annual Review of Genomics and Human Genetics},
|
||
volume = 16,
|
||
number = 1,
|
||
pages = {133-151},
|
||
year = 2015,
|
||
doi = {10.1146/annurev-genom-090413-025358},
|
||
note = {PMID: 25939052},
|
||
URL = { https://doi.org/10.1146/annurev-genom-090413-025358 },
|
||
eprint = { https://doi.org/10.1146/annurev-genom-090413-025358 }
|
||
,
|
||
abstract = { High-throughput DNA sequencing has considerably changed
|
||
the possibilities for conducting biomedical research by
|
||
measuring billions of short DNA or RNA fragments. A central
|
||
computational problem, and for many applications a first step,
|
||
consists of determining where the fragments came from in the
|
||
original genome. In this article, we review the main
|
||
techniques for generating the fragments, the main
|
||
applications, and the main algorithmic ideas for computing a
|
||
solution to the read alignment problem. In addition, we
|
||
describe pitfalls and difficulties connected to determining
|
||
the correct positions of reads. }
|
||
}
|
||
|
||
@book{book:211898,
|
||
title = {Mark's Basic Medical Biochemistry A Clinical Approach},
|
||
author = {Michael A. Lieberman, Allan Marks},
|
||
publisher = {Lippincott Williams & Wilkins},
|
||
isbn = {9780781770224,078177022X},
|
||
year = 2008,
|
||
series = {Point Lippincott Williams & Wilkins},
|
||
edition = {Third},
|
||
pages = {209, 260},
|
||
}
|
||
|
||
@article{ABIODUN2018e00938,
|
||
title = {State-of-the-art in artificial neural network applications:
|
||
A survey},
|
||
journal = {Heliyon},
|
||
volume = 4,
|
||
number = 11,
|
||
pages = {e00938},
|
||
year = 2018,
|
||
issn = {2405-8440},
|
||
doi = {https://doi.org/10.1016/j.heliyon.2018.e00938},
|
||
url = {https://www.sciencedirect.com/science/article/pii/S2405844018332067},
|
||
author = {Oludare Isaac Abiodun and Aman Jantan and Abiodun Esther
|
||
Omolara and Kemi Victoria Dada and Nachaat AbdElatif Mohamed
|
||
and Humaira Arshad},
|
||
keywords = {Computer science},
|
||
abstract = {This is a survey of neural network applications in the
|
||
real-world scenario. It provides a taxonomy of artificial
|
||
neural networks (ANNs) and furnish the reader with knowledge
|
||
of current and emerging trends in ANN applications research
|
||
and area of focus for researchers. Additionally, the study
|
||
presents ANN application challenges, contributions, compare
|
||
performances and critiques methods. The study covers many
|
||
applications of ANN techniques in various disciplines which
|
||
include computing, science, engineering, medicine,
|
||
environmental, agriculture, mining, technology, climate,
|
||
business, arts, and nanotechnology, etc. The study assesses
|
||
ANN contributions, compare performances and critiques methods.
|
||
The study found that neural-network models such as feedforward
|
||
and feedback propagation artificial neural networks are
|
||
performing better in its application to human problems.
|
||
Therefore, we proposed feedforward and feedback propagation
|
||
ANN models for research focus based on data analysis factors
|
||
like accuracy, processing speed, latency, fault tolerance,
|
||
volume, scalability, convergence, and performance. Moreover,
|
||
we recommend that instead of applying a single method, future
|
||
research can focus on combining ANN models into one
|
||
network-wide application.}
|
||
}
|
||
|
||
@article{LIU201711,
|
||
title = {A survey of deep neural network architectures and their
|
||
applications},
|
||
journal = {Neurocomputing},
|
||
volume = 234,
|
||
pages = {11-26},
|
||
year = 2017,
|
||
issn = {0925-2312},
|
||
doi = {https://doi.org/10.1016/j.neucom.2016.12.038},
|
||
url = {https://www.sciencedirect.com/science/article/pii/S0925231216315533},
|
||
author = {Weibo Liu and Zidong Wang and Xiaohui Liu and Nianyin Zeng
|
||
and Yurong Liu and Fuad E. Alsaadi},
|
||
keywords = {Autoencoder, Convolutional neural network, Deep learning,
|
||
Deep belief network, Restricted Boltzmann machine},
|
||
abstract = {Since the proposal of a fast learning algorithm for deep
|
||
belief networks in 2006, the deep learning techniques have
|
||
drawn ever-increasing research interests because of their
|
||
inherent capability of overcoming the drawback of traditional
|
||
algorithms dependent on hand-designed features. Deep learning
|
||
approaches have also been found to be suitable for big data
|
||
analysis with successful applications to computer vision,
|
||
pattern recognition, speech recognition, natural language
|
||
processing, and recommendation systems. In this paper, we
|
||
discuss some widely-used deep learning architectures and their
|
||
practical applications. An up-to-date overview is provided on
|
||
four deep learning architectures, namely, autoencoder,
|
||
convolutional neural network, deep belief network, and
|
||
restricted Boltzmann machine. Different types of deep neural
|
||
networks are surveyed and recent progresses are summarized.
|
||
Applications of deep learning techniques on some selected
|
||
areas (speech recognition, pattern recognition and computer
|
||
vision) are highlighted. A list of future research topics are
|
||
finally given with clear justifications.}
|
||
}
|
||
|
||
@misc{chervinskii_2015,
|
||
title = {Autoencoder structure},
|
||
url = {https://commons.wikimedia.org/wiki/File:Autoencoder_structure.png},
|
||
journal = {Wikimedia},
|
||
author = {Chervinskii},
|
||
year = 2015,
|
||
month = {Dec}
|
||
}
|
||
|
||
@book{Goodfellow-et-al-2016,
|
||
title = {Deep Learning},
|
||
author = {Ian Goodfellow and Yoshua Bengio and Aaron Courville},
|
||
publisher = {MIT Press},
|
||
note = {\url{http://www.deeplearningbook.org}},
|
||
year = 2016
|
||
}
|
||
|
||
@Article{Lewis_2020,
|
||
author = {Lewis, Mike and Liu, Yinhan and Goyal, Naman and
|
||
Ghazvininejad, Marjan and Mohamed, Abdelrahman and Levy, Omer
|
||
and Stoyanov, Veselin and Zettlemoyer, Luke},
|
||
title = {BART: Denoising Sequence-to-Sequence Pre-training for
|
||
Natural Language Generation, Translation, and Comprehension},
|
||
journal = {Proceedings of the 58th Annual Meeting of the Association
|
||
for Computational Linguistics},
|
||
year = 2020,
|
||
doi = {10.18653/v1/2020.acl-main.703},
|
||
url = {http://dx.doi.org/10.18653/v1/2020.acl-main.703},
|
||
publisher = {Association for Computational Linguistics}
|
||
}
|
||
|
||
@article{bigdeli17_image_restor_using_autoen_prior,
|
||
author = {Bigdeli, Siavash Arjomand and Zwicker, Matthias},
|
||
title = {Image Restoration Using Autoencoding Priors},
|
||
journal = {CoRR},
|
||
year = 2017,
|
||
url = {http://arxiv.org/abs/1703.09964v1},
|
||
abstract = {We propose to leverage denoising autoencoder networks as
|
||
priors to address image restoration problems. We build on the
|
||
key observation that the output of an optimal denoising
|
||
autoencoder is a local mean of the true data density, and the
|
||
autoencoder error (the difference between the output and input
|
||
of the trained autoencoder) is a mean shift vector. We use the
|
||
magnitude of this mean shift vector, that is, the distance to
|
||
the local mean, as the negative log likelihood of our natural
|
||
image prior. For image restoration, we maximize the likelihood
|
||
using gradient descent by backpropagating the autoencoder
|
||
error. A key advantage of our approach is that we do not need
|
||
to train separate networks for different image restoration
|
||
tasks, such as non-blind deconvolution with different kernels,
|
||
or super-resolution at different magnification factors. We
|
||
demonstrate state of the art results for non-blind
|
||
deconvolution and super-resolution using the same autoencoding
|
||
prior.},
|
||
archivePrefix = {arXiv},
|
||
eprint = {1703.09964},
|
||
primaryClass = {cs.CV},
|
||
}
|
||
|
||
@article{makhzani15_adver_autoen,
|
||
author = {Makhzani, Alireza and Shlens, Jonathon and Jaitly, Navdeep
|
||
and Goodfellow, Ian and Frey, Brendan},
|
||
title = {Adversarial Autoencoders},
|
||
journal = {CoRR},
|
||
year = 2015,
|
||
url = {http://arxiv.org/abs/1511.05644v2},
|
||
abstract = {In this paper, we propose the "adversarial autoencoder"
|
||
(AAE), which is a probabilistic autoencoder that uses the
|
||
recently proposed generative adversarial networks (GAN) to
|
||
perform variational inference by matching the aggregated
|
||
posterior of the hidden code vector of the autoencoder with an
|
||
arbitrary prior distribution. Matching the aggregated
|
||
posterior to the prior ensures that generating from any part
|
||
of prior space results in meaningful samples. As a result, the
|
||
decoder of the adversarial autoencoder learns a deep
|
||
generative model that maps the imposed prior to the data
|
||
distribution. We show how the adversarial autoencoder can be
|
||
used in applications such as semi-supervised classification,
|
||
disentangling style and content of images, unsupervised
|
||
clustering, dimensionality reduction and data visualization.
|
||
We performed experiments on MNIST, Street View House Numbers
|
||
and Toronto Face datasets and show that adversarial
|
||
autoencoders achieve competitive results in generative
|
||
modeling and semi-supervised classification tasks.},
|
||
archivePrefix = {arXiv},
|
||
eprint = {1511.05644v2},
|
||
primaryClass = {cs.LG},
|
||
}
|
||
|
||
@Article{Yoo_2020,
|
||
author = {Yoo, Jaeyoung and Lee, Hojun and Kwak, Nojun},
|
||
title = {Unpriortized Autoencoder For Image Generation},
|
||
journal = {2020 IEEE International Conference on Image Processing
|
||
(ICIP)},
|
||
year = 2020,
|
||
month = {Oct},
|
||
doi = {10.1109/icip40778.2020.9191173},
|
||
url = {http://dx.doi.org/10.1109/ICIP40778.2020.9191173},
|
||
ISBN = 9781728163956,
|
||
publisher = {IEEE}
|
||
}
|
||
|
||
@article{kaiser18_discr_autoen_sequen_model,
|
||
author = {Kaiser, Łukasz and Bengio, Samy},
|
||
title = {Discrete Autoencoders for Sequence Models},
|
||
journal = {CoRR},
|
||
year = 2018,
|
||
url = {http://arxiv.org/abs/1801.09797v1},
|
||
abstract = {Recurrent models for sequences have been recently
|
||
successful at many tasks, especially for language modeling and
|
||
machine translation. Nevertheless, it remains challenging to
|
||
extract good representations from these models. For instance,
|
||
even though language has a clear hierarchical structure going
|
||
from characters through words to sentences, it is not apparent
|
||
in current language models. We propose to improve the
|
||
representation in sequence models by augmenting current
|
||
approaches with an autoencoder that is forced to compress the
|
||
sequence through an intermediate discrete latent space. In
|
||
order to propagate gradients though this discrete
|
||
representation we introduce an improved semantic hashing
|
||
technique. We show that this technique performs well on a
|
||
newly proposed quantitative efficiency measure. We also
|
||
analyze latent codes produced by the model showing how they
|
||
correspond to words and phrases. Finally, we present an
|
||
application of the autoencoder-augmented model to generating
|
||
diverse translations.},
|
||
archivePrefix = {arXiv},
|
||
eprint = {1801.09797v1},
|
||
primaryClass = {cs.LG},
|
||
}
|
||
|
||
@misc{brownlee_2020,
|
||
title = {How Do Convolutional Layers Work in Deep Learning Neural
|
||
Networks?},
|
||
url = {https://machinelearningmastery.com/convolutional-layers-for-deep-learning-neural-networks/},
|
||
journal = {Machine Learning Mastery},
|
||
author = {Brownlee, Jason},
|
||
year = 2020,
|
||
month = {Apr}
|
||
}
|
||
@article{howard17_mobil,
|
||
author = {Howard, Andrew G. and Zhu, Menglong and Chen, Bo and
|
||
Kalenichenko, Dmitry and Wang, Weijun and Weyand, Tobias and
|
||
Andreetto, Marco and Adam, Hartwig},
|
||
title = {Mobilenets: Efficient Convolutional Neural Networks for
|
||
Mobile Vision Applications},
|
||
journal = {CoRR},
|
||
year = 2017,
|
||
url = {http://arxiv.org/abs/1704.04861v1},
|
||
abstract = {We present a class of efficient models called MobileNets
|
||
for mobile and embedded vision applications. MobileNets are
|
||
based on a streamlined architecture that uses depth-wise
|
||
separable convolutions to build light weight deep neural
|
||
networks. We introduce two simple global hyper-parameters that
|
||
efficiently trade off between latency and accuracy. These
|
||
hyper-parameters allow the model builder to choose the right
|
||
sized model for their application based on the constraints of
|
||
the problem. We present extensive experiments on resource and
|
||
accuracy tradeoffs and show strong performance compared to
|
||
other popular models on ImageNet classification. We then
|
||
demonstrate the effectiveness of MobileNets across a wide
|
||
range of applications and use cases including object
|
||
detection, finegrain classification, face attributes and large
|
||
scale geo-localization.},
|
||
archivePrefix = {arXiv},
|
||
eprint = {1704.04861v1},
|
||
primaryClass = {cs.CV},
|
||
}
|
||
@article{ronneberger15_u_net,
|
||
author = {Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
|
||
title = {U-Net: Convolutional Networks for Biomedical Image
|
||
Segmentation},
|
||
journal = {CoRR},
|
||
year = 2015,
|
||
url = {http://arxiv.org/abs/1505.04597v1},
|
||
abstract = {There is large consent that successful training of deep
|
||
networks requires many thousand annotated training samples. In
|
||
this paper, we present a network and training strategy that
|
||
relies on the strong use of data augmentation to use the
|
||
available annotated samples more efficiently. The architecture
|
||
consists of a contracting path to capture context and a
|
||
symmetric expanding path that enables precise localization. We
|
||
show that such a network can be trained end-to-end from very
|
||
few images and outperforms the prior best method (a
|
||
sliding-window convolutional network) on the ISBI challenge
|
||
for segmentation of neuronal structures in electron
|
||
microscopic stacks. Using the same network trained on
|
||
transmitted light microscopy images (phase contrast and DIC)
|
||
we won the ISBI cell tracking challenge 2015 in these
|
||
categories by a large margin. Moreover, the network is fast.
|
||
Segmentation of a 512x512 image takes less than a second on a
|
||
recent GPU. The full implementation (based on Caffe) and the
|
||
trained networks are available at
|
||
http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .},
|
||
archivePrefix = {arXiv},
|
||
eprint = {1505.04597v1},
|
||
primaryClass = {cs.CV},
|
||
}
|
||
@article{yuan18_simpl_convol_gener_networ_next_item_recom,
|
||
author = {Yuan, Fajie and Karatzoglou, Alexandros and Arapakis,
|
||
Ioannis and Jose, Joemon M and He, Xiangnan},
|
||
title = {A Simple Convolutional Generative Network for Next Item
|
||
Recommendation},
|
||
journal = {CoRR},
|
||
year = 2018,
|
||
url = {http://arxiv.org/abs/1808.05163v4},
|
||
abstract = {Convolutional Neural Networks (CNNs) have been recently
|
||
introduced in the domain of session-based next item
|
||
recommendation. An ordered collection of past items the user
|
||
has interacted with in a session (or sequence) are embedded
|
||
into a 2-dimensional latent matrix, and treated as an image.
|
||
The convolution and pooling operations are then applied to the
|
||
mapped item embeddings. In this paper, we first examine the
|
||
typical session-based CNN recommender and show that both the
|
||
generative model and network architecture are suboptimal when
|
||
modeling long-range dependencies in the item sequence. To
|
||
address the issues, we introduce a simple, but very effective
|
||
generative model that is capable of learning high-level
|
||
representation from both short- and long-range item
|
||
dependencies. The network architecture of the proposed model
|
||
is formed of a stack of \emph{holed} convolutional layers,
|
||
which can efficiently increase the receptive fields without
|
||
relying on the pooling operation. Another contribution is the
|
||
effective use of residual block structure in recommender
|
||
systems, which can ease the optimization for much deeper
|
||
networks. The proposed generative model attains
|
||
state-of-the-art accuracy with less training time in the next
|
||
item recommendation task. It accordingly can be used as a
|
||
powerful recommendation baseline to beat in future, especially
|
||
when there are long sequences of user feedback.},
|
||
archivePrefix = {arXiv},
|
||
eprint = {1808.05163v4},
|
||
primaryClass = {cs.IR},
|
||
}
|
||
@article{sadr21_novel_deep_learn_method_textual_sentim_analy,
|
||
author = {Sadr, Hossein and Solimandarabi, Mozhdeh Nazari and Pedram,
|
||
Mir Mohsen and Teshnehlab, Mohammad},
|
||
title = {A Novel Deep Learning Method for Textual Sentiment
|
||
Analysis},
|
||
journal = {CoRR},
|
||
year = 2021,
|
||
url = {http://arxiv.org/abs/2102.11651v1},
|
||
abstract = {Sentiment analysis is known as one of the most crucial
|
||
tasks in the field of natural language processing and
|
||
Convolutional Neural Network (CNN) is one of those prominent
|
||
models that is commonly used for this aim. Although
|
||
convolutional neural networks have obtained remarkable results
|
||
in recent years, they are still confronted with some
|
||
limitations. Firstly, they consider that all words in a
|
||
sentence have equal contributions in the sentence meaning
|
||
representation and are not able to extract informative words.
|
||
Secondly, they require a large number of training data to
|
||
obtain considerable results while they have many parameters
|
||
that must be accurately adjusted. To this end, a convolutional
|
||
neural network integrated with a hierarchical attention layer
|
||
is proposed which is able to extract informative words and
|
||
assign them higher weight. Moreover, the effect of transfer
|
||
learning that transfers knowledge learned in the source domain
|
||
to the target domain with the aim of improving the performance
|
||
is also explored. Based on the empirical results, the proposed
|
||
model not only has higher classification accuracy and can
|
||
extract informative words but also applying incremental
|
||
transfer learning can significantly enhance the classification
|
||
performance.},
|
||
archivePrefix = {arXiv},
|
||
eprint = {2102.11651},
|
||
primaryClass = {cs.CL},
|
||
}
|