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Conclude State of the Art with bioinformatics

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@@ -1,49 +1,3 @@
@article{10.1093/molbev/msy224,
author = {Flagel, Lex and Brandvain, Yaniv and Schrider, Daniel R},
title = "{The Unreasonable Effectiveness of Convolutional Neural
Networks in Population Genetic Inference}",
journal = {Molecular Biology and Evolution},
volume = 36,
number = 2,
pages = {220-238},
year = 2018,
month = 12,
abstract = "{Population-scale genomic data sets have given researchers
incredible amounts of information from which to infer
evolutionary histories. Concomitant with this flood of data,
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
related populations/species, construct recombination maps, and
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
theoretical advances for each new problem, and often focus on
a single aspect of the data (e.g., only allele frequency
information) in the interest of mathematical and computational
tractability. Directly interrogating the entirety of the input
sequence data in a likelihood-free manner would thus offer a
fruitful alternative. Here, we accomplish this by representing
DNA sequence alignments as images and using a class of deep
learning methods called convolutional neural networks (CNNs)
to make population genetic inferences from these images. We
apply CNNs to a number of evolutionary questions and find that
they frequently match or exceed the accuracy of current
methods. Importantly, we show that CNNs perform accurate
evolutionary model selection and parameter estimation, even on
problems that have not received detailed theoretical
treatments. Thus, when applied to population genetic
alignments, CNNs are capable of outperforming expert-derived
statistical methods and offer a new path forward in cases
where no likelihood approach exists.}",
issn = {0737-4038},
doi = {10.1093/molbev/msy224},
url = {https://doi.org/10.1093/molbev/msy224},
eprint = {https://academic.oup.com/mbe/article-pdf/36/2/220/27736968/msy224.pdf},
}
@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,
@@ -1025,6 +979,7 @@
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
@@ -1053,6 +1008,7 @@
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
@@ -1083,6 +1039,7 @@
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},
@@ -1119,6 +1076,7 @@
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},
@@ -1153,3 +1111,157 @@
eprint = {2102.11651},
primaryClass = {cs.CL},
}
@book{book:930,
title = {Bioinformatics: the machine learning approach},
author = {Pierre Baldi, Søren Brunak},
publisher = {The MIT Press},
isbn = {026202506X,9780585444666,9780262025065},
year = 2001,
series = {Adaptive Computation and Machine Learning},
edition = 2,
pages = 12,
}
@Article{Schneider_2011,
author = {Schneider, Maria V. and Orchard, Sandra},
title = {Omics Technologies, Data and Bioinformatics Principles},
journal = {Bioinformatics for Omics Data},
year = 2011,
pages = {3–30},
issn = {1940-6029},
doi = {10.1007/978-1-61779-027-0_1},
url = {http://dx.doi.org/10.1007/978-1-61779-027-0_1},
ISBN = 9781617790270,
publisher = {Humana Press},
}
@Article{Peri_2020,
author = {Peri, Sateesh and Roberts, Sarah and Kreko, Isabella R. and
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.},
title = {Read Mapping and Transcript Assembly: A Scalable and
High-Throughput Workflow for the Processing and Analysis of
Ribonucleic Acid Sequencing Data},
journal = {Frontiers in Genetics},
year = 2020,
volume = 10,
month = {Jan},
issn = {1664-8021},
doi = {10.3389/fgene.2019.01361},
url = {http://dx.doi.org/10.3389/fgene.2019.01361},
publisher = {Frontiers Media SA}
}
@Article{Zerbino_2008,
author = {Zerbino, D. R. and Birney, E.},
title = {Velvet: Algorithms for de novo short read assembly using de
Bruijn graphs},
journal = {Genome Research},
year = 2008,
volume = 18,
number = 5,
month = {Feb},
pages = {821–829},
issn = {1088-9051},
doi = {10.1101/gr.074492.107},
url = {http://dx.doi.org/10.1101/gr.074492.107},
publisher = {Cold Spring Harbor Laboratory}
}
@Article{Spudich_2007,
author = {Spudich, G. and Fernandez-Suarez, X. M. and Birney, E.},
title = {Genome browsing with Ensembl: a practical overview},
journal = {Briefings in Functional Genomics and Proteomics},
year = 2007,
volume = 6,
number = 3,
month = {Aug},
pages = {202–219},
issn = {1477-4062},
doi = {10.1093/bfgp/elm025},
url = {http://dx.doi.org/10.1093/bfgp/elm025},
publisher = {Oxford University Press (OUP)}
}
@Article{Liu_2018,
author = {Liu, Yang and Ye, Qing and Wang, Liwei and Peng, Jian},
title = {Learning structural motif representations for efficient
protein structure search},
journal = {Bioinformatics},
year = 2018,
volume = 34,
number = 17,
month = {Sep},
pages = {i773–i780},
issn = {1460-2059},
doi = {10.1093/bioinformatics/bty585},
url = {http://dx.doi.org/10.1093/bioinformatics/bty585},
publisher = {Oxford University Press (OUP)}
}
@Article{Salmela_2011,
author = {Salmela, L. and Schroder, J.},
title = {Correcting errors in short reads by multiple alignments},
journal = {Bioinformatics},
year = 2011,
volume = 27,
number = 11,
month = {Apr},
pages = {1455–1461},
issn = {1460-2059},
doi = {10.1093/bioinformatics/btr170},
url = {http://dx.doi.org/10.1093/bioinformatics/btr170},
publisher = {Oxford University Press (OUP)}
}
@Article{Yang_2012,
author = {Yang, X. and Chockalingam, S. P. and Aluru, S.},
title = {A survey of error-correction methods for next-generation
sequencing},
journal = {Briefings in Bioinformatics},
year = 2012,
volume = 14,
number = 1,
month = {Apr},
pages = {56–66},
issn = {1477-4054},
doi = {10.1093/bib/bbs015},
url = {http://dx.doi.org/10.1093/bib/bbs015},
publisher = {Oxford University Press (OUP)}
}
@Article{Kelley_2010,
author = {Kelley, David R and Schatz, Michael C and Salzberg, Steven
L},
title = {Quake: quality-aware detection and correction of sequencing
errors},
journal = {Genome Biology},
year = 2010,
volume = 11,
number = 11,
pages = {R116},
issn = {1465-6906},
doi = {10.1186/gb-2010-11-11-r116},
url = {http://dx.doi.org/10.1186/gb-2010-11-11-r116},
publisher = {Springer Science and Business Media LLC}
}
@Article{Zhao_2017,
author = {Zhao, Liang and Chen, Qingfeng and Li, Wencui and Jiang,
Peng and Wong, Limsoon and Li, Jinyan},
title = {MapReduce for accurate error correction of next-generation
sequencing data},
journal = {Bioinformatics},
year = 2017,
editor = {Sahinalp, CenkEditor},
volume = 33,
number = 23,
month = {Feb},
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)}
}