Education & Certifications


  • Doctor of Philosophy, University of California, Berkeley, Computational Biology (2013)
  • Master of Science, Columbia University, Computer Science (2007)
  • Bachelor of Arts, Hiram College, Political Science and Computer Science (2003)

Stanford Advisors


Research Projects


  • Evaluation of Community Outreach Programs on Adherence to Antiretroviral Drugs in Central Butsotso, Kakamega County, Kenya (MedScholars Project)

    Location

    Kakamega, Kenya

  • Influence of Adherence to Antiretroviral Medications on Mortality at Kakamega County Referral Hospital (MedScholars Project)

    Location

    Kakamega, Kenya

All Publications


  • Community outreach programs and major adherence lapses with antiretroviral therapy in rural Kakamega, Kenya AIDS Care Hodgkinson, L. M., Makori, J., Okwiri, J., Tsisiche, C., Arudo, J., Barry, M. 2017
  • Optimization criteria and biological process enrichment in homologous multiprotein modules PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Hodgkinson, L., Karp, R. M. 2013; 110 (26): 10872-10877

    Abstract

    Biological process enrichment is a widely used metric for evaluating the quality of multiprotein modules. In this study, we examine possible optimization criteria for detecting homologous multiprotein modules and quantify their effects on biological process enrichment. We find that modularity, linear density, and module size are the most important criteria considered, complementary to each other, and that graph theoretical attributes account for 36% of the variance in biological process enrichment. Variations in protein interaction similarity within module pairs have only minor effects on biological process enrichment. As random modules increase in size, both biological process enrichment and modularity tend to improve, although modularity does not show this upward trend in modules with size at most 50 proteins. To adjust for these trends, we recommend a size correction based on random sampling of modules when using biological process enrichment or other attributes to evaluate module boundaries. Characteristics of homologous multiprotein modules optimized for each of the optimization criteria are examined.

    View details for DOI 10.1073/pnas.1308621110

    View details for Web of Science ID 000321503700088

    View details for PubMedID 23757502

    View details for PubMedCentralID PMC3696829

  • Algorithms to Detect Multiprotein Modularity Conserved during Evolution IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS Hodgkinson, L., Karp, R. M. 2012; 9 (4): 1046-1058

    Abstract

    Detecting essential multiprotein modules that change infrequently during evolution is a challenging algorithmic task that is important for understanding the structure, function, and evolution of the biological cell. In this paper, we define a measure of modularity for interactomes and present a linear-time algorithm, Produles, for detecting multiprotein modularity conserved during evolution that improves on the running time of previous algorithms for related problems and offers desirable theoretical guarantees. We present a biologically motivated graph theoretic set of evaluation measures complementary to previous evaluation measures, demonstrate that Produles exhibits good performance by all measures, and describe certain recurrent anomalies in the performance of previous algorithms that are not detected by previous measures. Consideration of the newly defined measures and algorithm performance on these measures leads to useful insights on the nature of interactomics data and the goals of previous and current algorithms. Through randomization experiments, we demonstrate that conserved modularity is a defining characteristic of interactomes. Computational experiments on current experimentally derived interactomes for Homo sapiens and Drosophila melanogaster, combining results across algorithms, show that nearly 10 percent of current interactome proteins participate in multiprotein modules with good evidence in the protein interaction data of being conserved between human and Drosophila.

    View details for DOI 10.1109/TCBB.2011.125

    View details for Web of Science ID 000304147000013

    View details for PubMedID 21968956

  • Parallel software architecture for experimental workflows in computational biology on clouds Lecture Notes in Computer Science Hodgkinson, L., Rosa, J., Brewer, E. A. 2012
  • Algorithms to detect multiprotein modularity conserved during evolution Lecture Notes in Bioinformatics Hodgkinson, L., Karp, R. M. 2011
  • An expert system for credit evaluation and explanation Journal of Computing Sciences in Colleges Hodgkinson, L., Walker, E. 2003