Paul Horton, Readings in Computational Biology 2019

• Roots of Bioinformatics article by David Searls
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• Machine learning analysis of gene expression data reveals novel diagnostic and prognostic biomarkers and identifies therapeutic targets for soft tissue sarcomas.
  DGP van IJzendoorn, K Szuhai, …, & JVMG Bovée
  PLoS Comput Biol., 15:e1006826, 2019.
• Systems Biology Approaches Toward Understanding Primary Mitochondrial Diseases
  Elaina M. Maldonado, Fatma Taha, Joyeeta Rahman & Shamima Rahman
  Front Genet., 10:19, 2019.
• Text mining and its potential applications in systems biology
  Ananiadou, S., Kell, D. B., & Tsujii, J. I.
  Trends in biotechnology, 24(12):571-579, 2006.
• Scalable and accurate deep learning with electronic health records
  Alvin Rajkomar, Eyal Oren, …, & Jeffrey Dean
  npj Digital Medicine, 1:18, 2018.
• Off-target predictions in CRISPR-Cas9 gene editing using deep learning
  Lin, J., & Wong, K. C.
  Bioinformatics, 34:i656-i663, 2018.
• Sequence-specific DNA binding Pyrrole–imidazole polyamides and their applications
  Yusuke Kawamoto, Toshikazu Bando & Hiroshi Sugiyama
  Bioorganic & Medicinal Chemistry, 26:1393-1411, 2018.
• A parallel approximate string matching under Levenshtein distance on graphics processing units using warp-shuffle operations
  ThienLuan Ho, Seung-Rohk Oh & HyunJin Kim
  PLoS ONE, 12(10):e0186251, 2017.
• A general method applicable to the search for similarities in the amino acid sequence of two proteins
  Needleman, S. B., & Wunsch, C. D.
  Journal of molecular biology, 48(3):443-453, 1970.
• Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
  Ryan Poplin, Avinash V. Varadarajan, Katy Blumer, Yun Liu, Michael V. McConnell, Greg S. Corrado, Lily Peng & Dale R. Webster
  Nature Biomedical Engineering, 2, 158–164, 2018.
• A genome-scale analysis of mRNAs targeting to plant mitochondria: upstream AUGs in 5' untranslated regions reduce mitochondrial association
  Vincent T, Vingadassalon A, Ubrig E, Azeredo K, Srour O, Cognat V, Graindorge S, Salinas T, Maréchal-Drouard L, and Duchêne AM
  Plant Journal, 92(6):1132-1142, 2017.
• A review on biological inspired computation in cryptology
  Subariah Ibrahim & Mohd Aizaini Maarof
  Jurnal Teknologi Maklumat, 17:90-98, 2007.
• Machine learning in DNA microarray analysis for cancer classification
  Sung-Bae Cho & Hong-Hee Won
  Proceedings First APBC, 2003.
• Using natural language processing to extract clinically useful information from Chinese electronic medical records
  Liang Chen, Liting Song, Yue Shao, Dewei Li & Keyue Ding
  Int J Med Inform, 124:6-12, 2019.
• Dynamics of Translation of Single mRNA Molecules In Vivo
  Xiaowei Yan, Tim A. Hoek, Ronald D. Vale, and Marvin E. Tanenbaum
  Cell, 165(4):976-989, 2016.
• RNA localization
  Yaron Shav-Tal and Robert H. Singer
  J Cell Sci., 118:4077–4081, 2005.
• Identification of common molecular subsequences
  Smith, T. F., & Waterman, M. S.
  Journal of molecular biology, 147(1), 195–197, 1981.
• A linear space algorithm for computing maximal common subsequences
  Hirschberg, D. S.
  Communications of the ACM, 18(6), 341–343, 1975.
• The Next Generation of Transcription Factor Binding Site Prediction
  Anthony Mathelier & Wyeth W. Wasserman
  PLOS Computational Biology, 13:(9), e1003214, 2013.
• Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography
  Sukrit Narula, Khader Shameer, Alaa Mabrouk Salem Omar, Joel T. Dudley & Partho P. Sengupta
  Journal of the American College of Cardiology, 68, 2016.
• Biology Approaches Toward Understanding Primary Mitochondrial Diseases
  Maldonado, E. M., Taha, F., Rahman, J., & Rahman, S.
  Systems Frontiers in genetics, 10(19), 2019.
• The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments.
  Gorgolewski, K. J., Auer, T., Calhoun, V. D., Craddock, R. C., Das, S., Duff, E. P.,… & Handwerker, D. A.
  Scientific Data, 3:160044, 2016.
• DNA sequencing at 40: past, present and future.
  Shendure, J., Balasubramanian, S., Church, G. M., Gilbert, W., Rogers, J., Schloss, J. A., & Waterston, R. H.
  Nature, 550:(7676), 345. 2017.
• Representational similarity analysis-connecting the branches of systems neuroscience.
  Kriegeskorte, N., Mur, M., & Bandettini, P. A.
  Frontiers in systems neuroscience, 2 4, 2008.
• Biomarker identification for diagnosis of schizophrenia with integrated analysis of fMRI and SNPs.
  Cao, H., Lin, D., Duan, J., Wang, Y. P., & Calhoun, V.
  2012 IEEE International Conference on Bioinformatics and Biomedicine, 1-6. IEEE, 2012.
• A Probabilistic Classification System for Predicting the Cellular Localization Sites of Proteins
  Paul Horton and Kenta Nakai
  Proceeding of the Fourth International Conference on Intelligent Systems for Molecular Biology (ISMB1996), 109–115, 1996.
• PMLPR: A novel method for predicting subcellular localization based on recommender systems.
  Mehrabad, E. M., Hassanzadeh, R., & Eslahchi, C.
  Scientific reports, 8(1), 12006, 2018.
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
  Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun
  arXiv:1506.01497, 2015.
• Using recurrent neural network models for early detection of heart failure onset
  Edward Choi Andy Schuetz Walter F Stewart and Jimeng Sun
  Journal of the American Medical Informatics Association, 24(2), 2017, 361–370, 2016.
• Tackling the Challenges of FASTQ Referential Compression
  Aníbal Guerra, Jaime Lotero, José Édinson Aedo and Sebastián Isaza
  Bioinformatics and Biology Insights, 13: 1–19, 2019.
• Realizing drug repositioning by adapting a recommendation system to handle the process. Ozsoy, M. G., Özyer, T., Polat, F., & Alhajj, R.
  BMC bioinformatics, 19(1), 136, 2018.
• Predicting potential side effects of drugs by recommender methods and ensemble learning.
  Zhang, W., Zou, H., Luo, L., Liu, Q., Wu, W., & Xiao, W.
  Neurocomputing, 173:979-987, 2016.
• Mask R-CNN
  Kaiming He, Georgia Gkioxari, Piotr Dollár & Ross Girshick
  The IEEE International Conference on Computer Vision (ICCV), 2961-2969, 2017.
• The physical size of transcription factors is key to transcriptional regulation in chromatin domains
  Kazuhiro Maeshima, Kazunari Kaizu, Sachiko Tamura, Tadasu Nozaki, Tetsuro Kokubo4 & Koichi Takahashi
  J Phys Condens Matter, 27(6):064116, 2015.
• Karyopherins regulate nuclear pore complex barrier and transport function
  Larisa E. Kapinos, Binlu Huang, Chantal Rencurel, & Roderick Y.H. Lim
  J Cell Biol.216(11):3609–3624, 2017.
• Fast inexact mapping using advanced tree exploration on backward search methods
  José Salavert, Andrés Tomás, Joaquín Tárraga, Ignacio Medina, Joaquín Dopazo & Ignacio Blanquer Torres
  BMC Bioinformatics.16:18, 2015.
• Diffusion of size bidisperse spheres in dense granular shear flow
  Ruihuan Cai, Hongyi Xiao, Jinyang Zheng & Yongzhi Zhao
  Phys Rev E., 99(3-1):032902, 2019.
• Cell studio: A platform for interactive, 3D graphical simulation of immunological processes
  Asaf Liberman , Danny Kario, Matan Mussel, Jacob Brill, Kenneth Buetow, Sol Efroni & Uri Nevo
  APL Bioeng., 2(2):026107, 2018.
• Detection of DNA methylated microRNAs in hepatocellular carcinoma
  Wafaa M. Ezzata, Khalda Said Amrb, Yasser A. Elhosarya, Abdelfattah E.Hegazyc, Hoda H. Fahimd, Noha H. Eltaweelb & Refaat R. Kamele
  Gene, 702:153–157, 2019.
• Predicting cancer drug response using a recommender system
  Suphavilai C., Bertrand, D., & Nagarajan, N.
  Bioinformatics, 34(22):3907-3914, 2018.
• Detecting potential adverse drug reactions using a deep neural network model
  Wang, C. S., Lin, P. J., Cheng, C. L., Tai, S. H., Yang, Y. H. K., & Chiang, J. H.
  Journal of medical Internet research, 21(2):e11016, 2019.
• Use of a genetic algorithm in the cryptanalysis of simple substitution ciphers
  Richard Spillman, Mark Janssen, Bob Nelson & Martin Kepner
  Cryptologia, 17(1):31–44, 1993.
• Computational Analysis of mRNA Expression Profiles Identifies MicroRNA-29a/c as Predictor of Colorectal Cancer Early Recurrence
  Tai-Yue Kuo, Edward Hsi, I-Ping Yang, Pei-Chien Tsai, Jaw-Yuan Wang & Suh-Hang Han
  PLoS One., 7(2):e31587, 2012.
• Analysis of miRNA expression using machine learning
  Henry Wirth, Volkan Cakir, Lydia Hopp & Hans Binder
  Methods Mol Biol., 1107:257–278, 2014.
• Circulating Cell-free mRNA in Plasma as a Tumor Marker for Patients with Primary and Recurrent Gastric Cancer
  Nobuyuki Tani, Daisuke Ichikawa, Daito Ikoma, Haruhisa Tomita, Soujin Sai, Hisashi Ikoma, Kazuma Okamoto, Toshiya Ochiai, Yuji Ueda, Eigo Otsuji, Hisakazu Yamagishi, Norimasa Miura & Goshi Shiota
  Anticancer Res., 27(2):1207–12, 2007.
• Computation for ChIP-seq and RNA-seq studies
  Shirley Pepke, Barbara Wold & Ali Mortazavi
  Nat Methods., 6(11 Suppl):S22–32, 2009.
• Automated extraction of information on protein–protein interactions from the biological literature
  Toshihide Ono, Haretsugu Hishigaki, Akira Tanigami & Toshihisa Takagi
  Bioinformatics, 17(2):155-61, 2001.
• Machine Learning Approach for Identification of miRNA-mRNA Regulatory Modules in Ovarian Cancer
  Sushmita Paul & Shubham Talbar
  Proc. PReMI : Pattern Recognition and Machine Intelligence, , 438–447, 2017.
• Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning
  Tanel Pärnamaa and Leopold Parts
  G3: GENES, GENOMES, GENETICS , 7(5):1385–1392, 2017.
• Autoencoder Neural Networks for Outlier Correction in ECG-Based Biometric Identification
  Mikolaj Karpinski, Volodymyr Khoma, Valerii Dudykevych, Yuriy Khoma & Dmytro Sabodashko
  Proc. of The 4th IEEE International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems, Lviv, Ukraine, 2018.
• Basic Local Alignment Search Tool
  Stephen F. Altschul, Warren Gish, Webb Miller, Eugene W. Myers & David J. Lipman
  J. Mol.Biol., 215:403–410 1990.
• Biomedical Event Extraction Using Convolutional Neural Networks and Dependency Parsing
  Jari Björne and Tapio Salakoski
  Proc. BioNLP, Melbourne, Australia, 98–108, 2018.
• Chief Complaint Classification with Recurrent Neural Networks
  Scott Lee, Drew Levin, Pat Finley & Chad Heilig
  J Biomed Inform., 93:103158 2019.
• DeepSignal: detecting DNA methylation state from Nanopore sequencing reads using deep-learning
  Peng Ni, Neng Huan, Zhi Zhang, De-Peng Wang, Fan Liang, Yu Miao, Chuan-Le Xiao, Feng Luo & Jianxin Wang
  Bioinformatics, btz276, 2019.
• Develop machine learning-based regression predictive models for engineering protein solubility
  Xi Han, Xiaonan Wang & Kang Zhou
  Bioinformatics, btz294, 2019.
• Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders
  Gregory P. Way & Casey S. Greene
  Proc. Pac Symp Biocomput., 23:80–91, 2018.
• Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
  Daniel S. Kermany, Michael Goldbaum, Wenjia Cai,…, M. Anthony Lewis, Huimin Xia & Kang Zhang
  Cell, 172(5):1122-1131.e9, 2018.
• Incorporating dictionaries into deep neural networks for the Chinese clinical named entity recognition
  Qi Wang, Yuhang Xia, Yangming Zhou, Tong Ruan, Daqi Gao & Ping He
  Journal of Biomedical Informatics, 92:103133 2019.
• Long short-term memory RNN for biomedical named entity recognition
  Chen Lyu, Bo Chen, Yafeng Ren & Donghong Ji
  BMC Bioinformatics, 18:462, 2017.
• Transfer learning for biomedical named entity recognition with neural networks
  John M. Giorgi & Gary D. Bader
  Bioinformatics, 34(23):4087–4094, 2018.
• A Multimodal Approach to Predict Social Media Popularity
  Mayank Meghawat, Satyendra Yadav, Debanjan Mahata, Yifang Yin, Rajiv Ratn Shah & Roger Zimmermann
  Proc. IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), 2018.
• Profile hidden Markov models
  Sean R. Eddy
  Bioinformatics, 14(9):755–763, 1998.
• The sequence of sequencers: The history of sequencing DNA
  James M. Heather & Benjamin Chain
  Genomics, 107(1):1–8, 2015.
• Continuous base identification for single-molecule nanopore DNA sequencing
  James Clarke, Hai-Chen Wu, Lakmal Jayasinghe, Alpesh Patel1, Stuart Reid & Hagan Bayley
  Nature Nanotechnology, 4(4):265–270, 2009.
• On genomic repeats and reproducibility
  Can Firtina & Can Alkan
  Bioinformatics, 32(15):2243–2247 2016.
• Convolutional neural networks for classification of alignments of non-coding RNA sequences
  Genta Aoki & Yasubumi Sakakibara
  Bioinformatics, 34, i237–i244, 2018.
• Predicting the impact of non-coding variants on DNA methylation
  Haoyang Zeng & David K. Gifford
  Nucleic Acids Res., 45(11):e99 2017.
• Predicting CpG methylation levels by integrating Infinium HumanMethylation450 BeadChip array data
  Shicai Fan, Kang Huang, Rizi Ai, Mengchi Wang & Wei Wang
  Genomics, 107:132–137 2016.
• LeNup: learning nucleosome positioning from DNA sequences with improved convolutional neural networks
  Juhua Zhang, Wenbo Peng & Lei Wang
  Bioinformatics, 34(10):1705–1712, 2018.
• Predicting DNA methylation status using word composition
  Lingyi Lu, Kao Lin, Ziliang Qian, Haipeng Li, Yudong Cai & Yixue Li
  J. Biomedical Science and Engineering, 3:672–676, 2010.
• A Deep Learning Approach for the Classification of Neuronal Cell Types
  Alessio P. Buccino, Torbjørn V. Ness, Gaute T. Einevoll, Gert Cauwenberghs & Philipp D. Häfliger
  Conf Proc IEEE Eng Med Biol Soc., 999–1002, 2018.
• MaGIC: a machine learning tool set and web application for monoallelic gene inference from chromatin
  Svetlana Vinogradova, Sachit D. Saksena, Henry N. Ward, Sébastien Vigneau & Alexander A. Gimelbrant
  BMC Bioinformatics, 20:106 2019.