Bio

Professional Education


  • Doctor of Philosophy, Columbia University (2011)
  • Master of Philosophy, Columbia University (2009)
  • Bachelor of Technology, Indian Institute of Technology, Madras (2005)
  • Master of Science, Columbia University (2006)

Stanford Advisors


Research & Scholarship

Lab Affiliations


Publications

Journal Articles


  • Identification of Genetic Variants That Affect Histone Modifications in Human Cells SCIENCE McVicker, G., van de Geijn, B., Degner, J. F., Cain, C. E., Banovich, N. E., Raj, A., Lewellen, N., Myrthil, M., Gilad, Y., Pritchard, J. K. 2013; 342 (6159): 747-749

    Abstract

    Histone modifications are important markers of function and chromatin state, yet the DNA sequence elements that direct them to specific genomic locations are poorly understood. Here, we identify hundreds of quantitative trait loci, genome-wide, that affect histone modification or RNA polymerase II (Pol II) occupancy in Yoruba lymphoblastoid cell lines (LCLs). In many cases, the same variant is associated with quantitative changes in multiple histone marks and Pol II, as well as in deoxyribonuclease I sensitivity and nucleosome positioning. Transcription factor binding site polymorphisms are correlated overall with differences in local histone modification, and we identify specific transcription factors whose binding leads to histone modification in LCLs. Furthermore, variants that affect chromatin at distal regulatory sites frequently also direct changes in chromatin and gene expression at associated promoters.

    View details for DOI 10.1126/science.1242429

    View details for Web of Science ID 000326647600046

    View details for PubMedID 24136359

  • Identifying Hosts of Families of Viruses: A Machine Learning Approach PLOS ONE Raj, A., Dewar, M., Palacios, G., Rabadan, R., Wiggins, C. H. 2011; 6 (12)

    Abstract

    Identifying emerging viral pathogens and characterizing their transmission is essential to developing effective public health measures in response to an epidemic. Phylogenetics, though currently the most popular tool used to characterize the likely host of a virus, can be ambiguous when studying species very distant to known species and when there is very little reliable sequence information available in the early stages of the outbreak of disease. Motivated by an existing framework for representing biological sequence information, we learn sparse, tree-structured models, built from decision rules based on subsequences, to predict viral hosts from protein sequence data using popular discriminative machine learning tools. Furthermore, the predictive motifs robustly selected by the learning algorithm are found to show strong host-specificity and occur in highly conserved regions of the viral proteome.

    View details for DOI 10.1371/journal.pone.0027631

    View details for Web of Science ID 000298365700006

    View details for PubMedID 22174744

  • An Information-Theoretic Derivation of Min-Cut-Based Clustering IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Raj, A., Wiggins, C. H. 2010; 32 (6): 988-995

    Abstract

    Min-cut clustering, based on minimizing one of two heuristic cost functions proposed by Shi and Malik nearly a decade ago, has spawned tremendous research, both analytic and algorithmic, in the graph partitioning and image segmentation communities over the last decade. It is, however, unclear if these heuristics can be derived from a more general principle, facilitating generalization to new problem settings. Motivated by an existing graph partitioning framework, we derive relationships between optimizing relevance information, as defined in the Information Bottleneck method, and the regularized cut in a K-partitioned graph. For fast-mixing graphs, we show that the cost functions introduced by Shi and Malik can be well approximated as the rate of loss of predictive information about the location of random walkers on the graph. For graphs drawn from a generative model designed to describe community structure, the optimal information-theoretic partition and the optimal min-cut partition are shown to be the same with high probability.

    View details for DOI 10.1109/TPAMI.2009.124

    View details for Web of Science ID 000276671900003

    View details for PubMedID 20431126

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