Computational Biology Group

Center for Computational Learning Systems |
Center for Computational Biology and Bioinformatics |
Department of Computer Science

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The Computational Biology Group at Columbia develops and applies computational techniques for modeling and understanding biological processes at the molecular level. Our research emphasizes the application of statistical and machine learning techniques, such as hidden Markov models and support vector machines. We apply these techniques to various types of biological data, including DNA and protein sequence data, as well as gene expression data from microarray experiments. We are currently developing methods for the identification of gene transcripts and regulatory motifs in DNA, detection of remote protein homologies, and gene and tissue classification from microarray and other types of data.

From left to right: (standing) Rui Kuang, Marta Arias, Anshul Kundaje, Xuejing Li, Christina Leslie, David Quigley, Ben Neuwirth; (seated) Anil Raj, Chris Wiggins.

Group members

  • Christina Leslie. Research Scientist, Center for Computational Learning Systems.
  • Rui Kuang. Ph.D. student, Computer Science. String kernels for protein sequence data, local protein structure prediction.
  • Anshul Kundaje. Ph.D. student, Computer Science. Predictive models of gene regulatory networks.
  • David Quigley. Masters student, Biomedical Informatics. Learning sequence determinants of alternative splicing.
  • Steve Lianoglou. Masters student, Computer Science. Inferring signaling pathways.
  • Girish Rao. Masters student, Computer Science. Protein domain segmentation.
  • Vishakh. Masters student, Computer Science. Transcriptional gene regulation.
  • Ben Neuwirth. Undergraduate student, Computer Science. Transcriptional gene regulation.

Collaborators

  • Jason Weston. Research Scientist, NEC Labs.
  • Chris Wiggins. Assistant Professor, Applied Mathemetics, Columbia University.
  • William Stafford Noble. Assistant Professor, Department of Genome Sciences, University of Washington.
  • Larry Chasin. Professor, Department of Biological Sciences, Columbia University.
  • Li Zhang. Professor, Mailman School of Public Health, Columbia University.
  • Marta Arias. Associate Research Scientist, Center for Computational Learning Systems, Columbia University.
  • Manuel Middendorf.i Ph.D., Physics Department, Columbia University.
  • Eugene Ie. Ph.D. student, Computer Science Department, UC San Diego.
  • Xuejing Li. Ph.D. student, Physics Department, Columbia University.
  • Xiang Zhang. Ph.D. student, Department of Biology, Columbia University.
  • An-Suei Yang. Genomics Research Center, Academica Sinica, Taiwan.
  • Yoav Freund. Professor, Computer Science, UCSD.
  • Andre Elisseeff. Research Scientist, IBM Zurich.
  • Eleazar Eskin. Assistant Professor, Computer Science, UCSD.
  • Paul Pavlidis. Assistant Professor, Biomedical Informatics, Columbia University.

Publications

Software Tools

  • String kernel package: implementation of mismatch and profile string kernel code for SVM classification of protein sequences.
  • GeneClass: predictive modeling for gene regulation using boosted alternating decision trees.
  • MEDUSA: Motif discovery for cis regulatory elements using predictive modeling of gene regulation.
  • Gist: software for support vector machine classification and for kernel principal components analysis.

    New Research Projects for Students

    A number of research opportunities for graduate and undergraduate students are available in the Computational Biology Group. Contact us to learn more about current projects in our group.
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