Paul Horton, Readings in Computational Biology 2021

Papers Presented

Mammalian NUMT insertion is non-random
J Tsuji, MC Frith, K Tomii, P Horton
Nucleic Acids Research, 40(18), 9073–9088, 2012.

A beginners guide to SNP calling from high-throughput DNA-sequencing data
André Altmann, Peter Weber, Daniel Bader, Michael Preuss, Elisabeth B Binder, Bertram Müller-Myhsok
Hum Genet, 131(10):1541-54, 2012.

Identification of 12 cancer types through genome deep learning
Yingshuai Sun, Sitao Zhu, ..., & Wenbin Chen
Scientific Reports, 9:17256, 2019.

STAR: ultrafast universal RNA-seq aligner
Alexander Dobin, Carrie A Davis, Felix Schlesinger, Jorg Drenkow, Chris Zaleski, Sonali Jha, Philippe Batut, Mark Chaisson, Thomas R Gingeras
Bioinformatics, 29(1):15-21, 2013.

Metagenomic study of the oral microbiota by Illumina high-throughput sequencing
Vladimir Lazarevic, Katrine Whiteson, Susan Huse, David Hernandez, Laurent Farinelli, Magne Østerås, Jacques Schrenzel, and Patrice François
Journal of Microbiological Methods, 79(3):266–271, 2009.

Assembly algorithms for next-generation sequencing data
Jason R. Miller, Sergey Koren & Granger Sutton
Genomics, 95(6):315–327, 2010.

Better Prediction of Protein Cellular Localization Sites with the k Nearest Neighbors Classifier
Paul Horton & Kenta Nakai
Proceedings ISMB-97, 147-152, 1997.

A Neural Algorithm of Artistic Style
Leon A. Gatsy, Alexander S. Ecker, Matthias Bethge
arXiv preprint arXiv, 1508.06576, 2015.

Predicting DNA Methylation from word Composition
L Lu, K Lin, Z Qian, H Li, Y Cai, Y Li …
Journal of Biomedical Science, 2010.

CPEM: Accurate cancer type classification bases on somatic alterations
K Lee, H Jeong, S Lee, WK Jeong
Scientific Reports, 2019.

Mutations in importin-β family nucleocytoplasmic transport receptors transportin-SR and importin-13 differentially affect binding to respective cargoes
Makoto Kimura, Kenichiro Imai, Yuriko Morinaka, Yoshiko Hosono-Sakuma, Paul Horton, and Naoko Imamoto
Scientific Reports, 11:15649, 2021.

A universal SNP and small-indel variant caller using deep neural networks
Ryan Poplin, Pi-Chuan Chang,...,Mark A DePristo
Nature Biotechnology, 36, 983–987, 2018.

BioBERT: a pre-trained biomedical language representation model for biomedical text mining.
Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J.
Bioinformatics, 36, 1234–1240, 2019.

An introduction to deep learning on biological sequence data: example and solutions
Vanessa Isabell Jurtz, Alexander Rosenberg Johansen, ..., Søren Kaae Sønderby
Bioinformatics, 33:22, 3685–3690, 2017.

Biphasic regulation of transcriptional surge generated by the gene feedback loop in a two-component system
W Liu, X Li, H Qi, Y Wu, J Qu, Z Yin, X Gao, A Han
Bioinformatics,btab138, 2021.

Convolutional neural networks for classification of alignments of non-coding RNA sequences
Genta Aoki, Yasubumi Sakakibara
Bioinformatics, 34:13, i237–i244, 2018.

Machine learning and bioinformatics models to identify gene expression patterns of ovarian cancer associated with disease progression and mortality
Md Ali Hossain, Sheikh Muhammad Saiful Islam, Julian M W Quinn, Fazlul Huq, Mohammad Ali Moni
J Biomed Inform, 100:103313, 2019.

Gene2vec: distributed representation of genes based on co-expression
Jingcheng Du, Peilin Jia, Yulin Dai, Cui Tao, Zhongming Zhao & Degui Zhi
BMC Genomics, 20(Supl 1):82, 2019

Predicting CTCF-mediated chromatin loops using CTCF-MP
Ruochi Zhang, Yuchuan Wang, Yang Yang, Yang Zhang and Jian Ma
Bioinformatics, 34:13, 2018.

BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides
Phasit Charoenkwan, Chanin Nantasenamat, Md Mehedi Hasan, Balachandran Manavalan, Watshara Shoombuatong
Bioinformatics, 1-7, 2021.

An efficient and scalable analysis framework for variant extraction and refinement from population-scale DNA sequence data
Goo Jun, Mary Kate Wing, Gonçalo R. Abecasis, and Hyun Min Kang
Genome Res., 25(6): 918–925, 2015.

qSNE: quadratic rate t-SNE optimizer with automatic parameter tuning for large datasets
Antti Häkkinen, Juha Koiranen, Julia Casado, ..., Sampsa Hautaniemi
Bioinformatics, 36:20, 5086–5092, 2020.

MicroCellClust: mining rare and highly specific subpopulations from single-cell expression data
Alexander Gerniers, Orian Bricard, Pierre Dupont
Bioinformatics, 2021.

Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics
Juan Jovel, Jordan Patterson, Weiwei Wang, Naomi Hotte, Sandra O’Keefe, Troy Mitchel, Troy Perry, Dina Kao, Andrew L. Mason, Karen L. Madsen and Gane K.-S. Wong
Front Microbiol, 7:459, 2016.

Transfer learning for biomedical named entity recognition with neural networks
John M. Giorgi and Gary D. Bader
Bioinformatics, 34:23, 2018.

GPDBN: deep bilinear network integrating both genomic data and pathological images for breast cancer prognosis prediction
Zhiqin Wang, Ruiqing Li, Minghui Wang, Ao Li
Bioinformatics, 1-8, 2021.

Fast and accurate short read alignment with Burrows–Wheeler transform
Heng Li & Richard Durbin
Bioinformatics, 25(14):1754-1760, 2009.

GASAL2: a GPU accelerated sequence alignment library for high-throughput NGS data
Nauman Ahmed, Jonathan Lévy, Shanshan Ren, Hamid Mushtaq, Koen Bertels & Zaid Al-Ars
BMC Bioinformatics, 20:520, 2019.

A machine learning-based method for prediction of macrocyclization patterns of polyketides and non-ribosomal peptides
Priyesh Agrawal and Debasisa Mohanty
Bioinformatics, 37:5, 2021.

Planned Future Presentations

Entry Format

<LI>TITLE Author, Author, ... Journal, VOL:NUM, YYYY.</LI>