Luqman Mushila Hodgkinson, PhD, MS, is from Kakamega, Kenya, in the former Western Province of Kenya, a medically underserved area where in 2018 there were 193 medical doctors registered to serve around 5 million people. He is a founding member of the School of Medicine at Masinde Muliro University of Science and Technology in Kakamega, Kenya, which now has three classes of medical students, where he is on the faculty. He conducted and published the first study of 10-year survival on antiretroviral medications for HIV in Kenya.
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 (2003)
Michele Barry, Med Scholar Project Advisor
Adjunct Lecturer, Instructor, and Coordinator in the School of Medicine, Masinde Muliro University of Science and Technology (July 21, 2016 - Present)
Founding member of the School of Medicine, now with three classes of medical students, in the former Western Province of Kenya where in 2018 there were 193 medical doctors registered for around 5 million people
Ten-year survival with analysis of gender difference, risk factors, and causes of death during 13 years of public antiretroviral therapy in rural Kenya
2020; 99 (21)
View details for DOI 10.1097/MD.0000000000020328
- Shoshin beriberi in a patient with oral and cutaneous graft-versus-host disease. JAAD case reports 2020; 6 (5): 420–21
Genetic mutations underlying phenotypic plasticity in basosquamous carcinoma
Journal of Investigative Dermatology
View details for DOI 10.1016/j.jid.2019.03.1163
Community outreach programs and major adherence lapses with antiretroviral therapy in rural Kakamega, Kenya.
2018; 30 (6): 696–700
We investigated features of major adherence lapses in antiretroviral therapy (ART) at public Emusanda Health Centre in rural Kakamega County, Kenya using medical records from 2008 to 2015 for all 306 eligible patients receiving ART. Data were modelled using survival analysis. Patients were more likely to lapse if they received stavudine (hazard ratio (HR) 2.54, 95% confidence interval (95%CI):1.44-4.47) or zidovudine (HR 1.64, 95%CI:1.02-2.63) relative to tenofovir. Each day a patient slept hungry per month increased risk of major adherence lapse by 3% (95%CI:0-7%). Isolated home visits by community health workers (CHWs) were more effective to assist patients to return to the health centre than isolated phone calls (HR 2.52, 95%CI:1.02-6.20).
View details for PubMedID 29058457
Optimization criteria and biological process enrichment in homologous multiprotein modules
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2013; 110 (26): 10872-10877
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
2012; 9 (4): 1046-1058
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
View details for DOI 10.1007/978-3-642-31500-8_29
Algorithms to detect multiprotein modularity conserved during evolution
Lecture Notes in Bioinformatics
View details for DOI 10.1007/978-3-642-21260-4_14
- An expert system for credit evaluation and explanation Journal of Computing Sciences in Colleges 2003