Home Up

Paul Horton, Readings in Computational Biology 2022

Course Overview (org format)

Papers Presented

  • Basic local alignment search tool.
    Altschul, S F et al.
    Journal of molecular biology, 215(3):403-10, 1990.
  • Machine learning algorithm as a diagnostic tool for hypoadrenocorticism in dogs
    K.L. Reagan, B.A. Reagan, C. Gilor
    Domestic Animal Endocrinology, 72:106396, 2020.
  • Fast and accurate short read alignment with Burrows–Wheeler transform
    Heng Li & Richard Durbin
    Bioinformatics, 25(14):1754-1760, 2009.
  • 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.
  • GPU accelerated sequence alignment with traceback for GATK haplotypecaller
    S. Ren, N. Ahmed, K. Bertels and Z. Al-Ars
    BMC Genomics, 20:2, 2019.
  • A machine learning approach to unmask novel gene signatures and prediction of Alzheimer’s disease within different brain regions
    Abhibhav Sharma, Pinki Dey
    Genomics, 113(4):1778-1789, 2021.
  • A Proposed Method for Detecting Blood Diseases by Non-Invasive Bio-Impedance Analysis
    Yomna H. Shash, Mohamed A.A. El dosoky and Mohamed T. El-Wakad
    Research Journal of Applied Sciences, Engineering and Technology, 15(7):270-280, 2018.
  • Genome-Wide Association Studies-Based Machine Learning for Prediction of Age-Related Macular Degeneration Risk
    Qi Yan, Yale Jiang, HengHuang, Anand Swaroop, Emily Y. Chew, Daniel E.Weeks, Wei Chen, and Ying Ding
    Translational Vision Science and Technology, 10:2, 2021.
  • Precise uncertain significance prediction using latent space matrix factorization models: genomics variant and heterogeneous clinical data-driven approaches
    Sina Abdollahi, Peng-Chan Lin, Meng-Ru Shen and Jung-Hsien Chiang
    Briefings in Bioinformatics, 22(4):bbaa281, 2021.
  • Pangenome Graphs
    Jordan M. Eizenga, Adam M. Novak, Jonas A. Sibbesen, Simon Heumos, Ali Ghaffaari, Glenn Hickey, Xian Chang, Josiah D. Seaman, Robin Rounthwaite, Jana Ebler, Mikko Rautiainen, Shilpa Garg, Benedict Paten, Tobias Marschall, Jouni Sirén, Erik Garrison
    Annual Review of Genomics and Human Genetics , 21(1):139-162, 2020.
    • Evaluation of supervised machine learning algorithms to distinguish between inflammatory
  • Linear Work Suffix Array Construction
    Juha Karkkainen, Peter Sanders, Stefan Burkhardt
    Journal of the ACM, 53:6, 2006.
  • A machine learning approach for predicting CRISPR-Cas9 cleavage efficiencies and patterns underlying its mechanism of action
    Shiran Abadi, Winston X. Yan, David Amar, Itay Mayrose
    PLoS Computational Biology, 13:(10)e1005807, 2017.
  • Machine learning for analysis of gene expression data in fast- and slow-progressing amyotrophic lateral sclerosis murine models
    Ernesto Iadanza, Rachele Fabbri, Francesco Goretti, Giovanni Nardo, Elena Niccolai, Caterina Bendotti, Amedeo Amedei
    Biocybernetics and Biomedical Engineering 42,Issue1:273-284, 2022.
  • Comparing different supervised machine learning algorithms for disease prediction
    Shahadat Uddin, Arif Khan, Md Ekramul Hossain and Mohammad Ali Moni
    BMC Medical Informatics and Decision Making, 19:281, 2019.
  • Predicting DNA methylation status using word composition
    Lingyi Lu, Kao Lin, Ziliang Qian, Haipeng Li, Yudong Cai, Yixue Li
    Journal of Biomedical Science and Engineering, 03:(07) 672-676, 2010.
  • Evidence for CRHR1 in multiple sclerosis using supervised machine learning and meta-analysis in 12566 individuals
    Farren B.S. Briggs, Selena E. Bartlett, Benjamin A. Goldstein, Joanne Wang, Jacob L. McCauley, Rebecca L. Zuvich, Philip L. De Jager, John D. Rioux, Adrian J. Ivinson, Alastair Compston, David A. Hafler, Stephen L. Hauser, Jorge R. Oksenberg, Stephen J. Sawcer, Margaret A. Pericak-Vance, Jonathan L. Haines, International Multiple Sclerosis Genetics Consortium, Lisa F. Barcellos
    Human Molecular Genetics, 19:21, 2010.
  • DiaDeL: An accurate deep learning-based model with mutational signatures for predicting metastasis stage and cancer types
    Sina Abdollahi, Peng-Chan Lin and Jung-Hsien Chiang
    IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021.
  • Fast and accurate short read alignment with Burrows–Wheeler transform
    Heng Li & Richard Durbin
    Bioinformatics, 25(14):1754-1760, 2009. (encore)
  • Genome-Wide Association Studies-Based Machine Learning for Prediction of Age-Related Macular Degeneration Risk
    Qi Yan, Yale Jiang, HengHuang, Anand Swaroop, Emily Y. Chew, Daniel E.Weeks, Wei Chen, and Ying Ding
    Translational Vision Science and Technology, 10:2, 2021. (encore)

    • Planned Future Presentations

  • Pangenome-based genome inference allows efficient and accurate genotyping across a wide spectrum of variant classes
    Jana Ebler, Peter Ebert, Wayne E. Clarke, Tobias Rausch, Peter A. Audano, Torsten Houwaart, Yafei Mao, Jan O. Korbel, Evan E. Eichler, Michael C., Zody, Alexander T. Dilthey and Tobias Marschall
    Nature Genetics, Vol 54:518-525, 2022.
  • The Neighbor-joining Method: A New Method for Reconstructing Phylogenetic Trees
    Naruya Saitou and Masatoshi Nei
    Molecular biology and evolution, 4(4):406-425, 1987.

    • Entry Format

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