- Jesus Aguilar-Ruiz, Pablo de Olavide University
- Jason Moore, Dartmouth College
The optimization and visualization of edge weights in optimal assignment methods is a valuable approach for ligandbased virtual screening experiments and provides a useful tool for drug discovery.
The multivariate analysis of variance method (MANOVA) is superior to other methods for association mapping in dose-response genome-wide association studies when applied to a simulation study.
The Modular Relief Framework (MoRF) abstracts common features of Relief algorithms to create new algorithms with a greater ability to identify interacting genetic variants in synthetic genomic data.
- View more articles
- #bioinformatics RT @CompSciFact @Atabey_Kaygun "Foundations of Computer Science" by Aho,Ullman available online http://t.co/k2vOVsoufv about 4 hours ago
- RT @pickover: The work you think about in the shower, while smiling, may be the work you should be doing for the rest of your life. about 5 hours ago
- Classic: Turing, A.M. (1950). Computing machinery and intelligence. Mind, 59, 433-460 http://t.co/APiZ7pBFYr #artificialintelligence about 5 hours ago
Aims & scope
BioData Mining is an open access, peer reviewed, online journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data.
BioData Mining 2013, 6:6
Dr. Moore directs an NIH-funded research program that is focused on complex systems approaches to understanding the genetic basis for common human diseases. A major focus of the lab is on the development, evaluation and application of machine learning and data mining algorithms for detecting and characterizing nonlinear gene-gene and gene-environment interactions. Recent work is focused on the study of epistasis using network science and visual analytics. More information can be found at www.epistasis.org and compgen.blogspot.com.
Dr. Aguilar leads a research group on Bioinformatics. His group is involved in several projects related to gene association networks, protein structure prediction, disease prognosis and application of data mining or evolutionary techniques to biomedical problems. He is the Dean of the School of Engineering, at Pablo de Olavide University, Seville, Spain, and one of his main concerns is to approach biologists and computer scientists to common research goals.