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Full-Text Articles in Physical Sciences and Mathematics
Genetic Variants In Kcnj11, Tcf7l2 And Hnf4a Are Associated With Type 2 Diabetes, Bmi And Dyslipidemia In Families Of Northeastern Mexico: A Pilot Study, Hugo Leonid Gallardo-Blanco, Jesus Zacarias Villarreal-Perez, Ricardo Martin Cerda-Flores, Andres Figueroa
Genetic Variants In Kcnj11, Tcf7l2 And Hnf4a Are Associated With Type 2 Diabetes, Bmi And Dyslipidemia In Families Of Northeastern Mexico: A Pilot Study, Hugo Leonid Gallardo-Blanco, Jesus Zacarias Villarreal-Perez, Ricardo Martin Cerda-Flores, Andres Figueroa
Computer Science Faculty Publications and Presentations
The aim of the present study was to investigate whether genetic markers considered risk factors for metabolic syndromes, including dyslipidemia, obesity and type 2 diabetes mellitus (T2DM), can be applied to a Northeastern Mexican population. A total of 37 families were analyzed for 63 single nucleotide polymorphisms (SNPs), and the age, body mass index (BMI), glucose tolerance values and blood lipid levels, including those of cholesterol, low‑density lipoprotein (LDL), very LDL (VLDL), high‑density lipoprotein (HDL) and triglycerides were evaluated. Three genetic markers previously associated with metabolic syndromes were identified in the sample population, including KCNJ11, TCF7L2 and HNF4A. The KCNJ11 …
Inferring Causal Molecular Networks: Empirical Assessment Through A Community-Based Effort, Steven M. Hill, Laura M. Heiser, Thomas Cokelaer, Michael Unger, Nicole K. Nesser, Daniel E. Carlin, Yang Zhang, Artem Sokolov, Evan O. Paull, Dong-Chul Kim
Inferring Causal Molecular Networks: Empirical Assessment Through A Community-Based Effort, Steven M. Hill, Laura M. Heiser, Thomas Cokelaer, Michael Unger, Nicole K. Nesser, Daniel E. Carlin, Yang Zhang, Artem Sokolov, Evan O. Paull, Dong-Chul Kim
Computer Science Faculty Publications and Presentations
It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology …