@Article{pmid19706884,
Author = "Robins, H. S. and Campregher, P. V. and Srivastava, S. K.
and Wacher, A. and Turtle, C. J. and Kahsai, O. and Riddell,
S. R. and Warren, E. H. and Carlson, C. S. ",
Title = "{{C}omprehensive assessment of {T}-cell receptor beta-chain
diversity in alphabeta {T} cells}",
Journal = "Blood",
Year = 2009,
Volume = 114,
Number = 19,
Pages = "4099--4107",
Month = "Nov"
}
@article {Nurk2021.05.26.445798,
author = {Nurk, Sergey and Koren, Sergey and Rhie, Arang and
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},
elocation-id = {2021.05.26.445798},
year = 2021,
doi = {10.1101/2021.05.26.445798},
publisher = {Cold Spring Harbor Laboratory},
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
of a human genome, representing the largest improvement to the
human reference genome since its initial release. The new
T2T-CHM13 reference includes gapless assemblies for all 22
autosomes plus Chromosome X, corrects numerous errors, and
introduces nearly 200 million bp of novel sequence containing
2,226 paralogous gene copies, 115 of which are predicted to be
protein coding. The newly completed regions include all
centromeric satellite arrays and the short arms of all five
acrocentric chromosomes, unlocking these complex regions of
the genome to variational and functional studies for the first
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
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).},
URL = {https://www.biorxiv.org/content/early/2021/05/27/2021.05.26.445798},
eprint = {https://www.biorxiv.org/content/early/2021/05/27/2021.05.26.445798.full.pdf},
journal = {bioRxiv}
}
@ARTICLE{10.3389/fgene.2020.00900,
AUTHOR = {Wang, Luotong and Qu, Li and Yang, Longshu and Wang, Yiying
and Zhu, Huaiqiu},
TITLE = {NanoReviser: An Error-Correction Tool for Nanopore
Sequencing Based on a Deep Learning Algorithm},
JOURNAL = {Frontiers in Genetics},
VOLUME = 11,
PAGES = 900,
YEAR = 2020,
URL = {https://www.frontiersin.org/article/10.3389/fgene.2020.00900},
DOI = {10.3389/fgene.2020.00900},
ISSN = {1664-8021},
ABSTRACT = {Nanopore sequencing is regarded as one of the most
promising third-generation sequencing (TGS) technologies.
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
over the past years, it is still challenging to correct the
sequences after applying the basecalling procedure. In this
study, we developed an open-source DNA basecalling reviser,
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
sequencing reads from public E. coli and human NA12878
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},
}
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title = {Bioinformatics: the machine learning approach},
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isbn = {026202506X,9780585444666,9780262025065},
year = 2001,
series = {Adaptive Computation and Machine Learning},
edition = 2,
pages = 12,
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author = {Schneider, Maria V. and Orchard, Sandra},
title = {Omics Technologies, Data and Bioinformatics Principles},
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doi = {10.1007/978-1-61779-027-0_1},
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McHan, Lauren B. and Naron, Alexandra and Ram, Archana and
Murphy, Rebecca L. and Lyons, Eric and Gregory, Brian D. and
Devisetty, Upendra K. and et al.},
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publisher = {Frontiers Media SA}
}
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doi = {10.1093/bfgp/elm025},
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publisher = {Oxford University Press (OUP)}
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title = {Learning structural motif representations for efficient
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year = 2018,
volume = 34,
number = 17,
month = {Sep},
pages = {i773–i780},
issn = {1460-2059},
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url = {http://dx.doi.org/10.1093/bioinformatics/bty585},
publisher = {Oxford University Press (OUP)}
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author = {Salmela, L. and Schroder, J.},
title = {Correcting errors in short reads by multiple alignments},
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issn = {1460-2059},
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L},
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doi = {10.1186/gb-2010-11-11-r116},
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}
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Peng and Wong, Limsoon and Li, Jinyan},
title = {MapReduce for accurate error correction of next-generation
sequencing data},
journal = {Bioinformatics},
year = 2017,
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volume = 33,
number = 23,
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pages = {3844–3851},
issn = {1460-2059},
doi = {10.1093/bioinformatics/btx089},
url = {http://dx.doi.org/10.1093/bioinformatics/btx089},
publisher = {Oxford University Press (OUP)}
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Yves and Hot, David},
title = {Comparison of mapping algorithms used in high-throughput
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B. A. and Cox, C. J. and Dalke, A. and Friedberg, I. and
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issn = {1460-2059},
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url = {http://dx.doi.org/10.1093/bioinformatics/btp163},
publisher = {Oxford University Press (OUP)}
}
@misc{tensorflow2015-whitepaper,
title = { {TensorFlow}: Large-Scale Machine Learning on
Heterogeneous Systems},
url = {https://www.tensorflow.org/},
note = {Software available from tensorflow.org},
author = { Mart\'{\i}n~Abadi and Ashish~Agarwal and Paul~Barham and
Eugene~Brevdo and Zhifeng~Chen and Craig~Citro and
Greg~S.~Corrado and Andy~Davis and Jeffrey~Dean and
Matthieu~Devin and Sanjay~Ghemawat and Ian~Goodfellow and
Andrew~Harp and Geoffrey~Irving and Michael~Isard and Yangqing
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