The focus of the Gerstein Lab is interpreting personal genomes, particularly in relation to disorders, such as cancer. This endeavor has a number of related aspects described below. Moreover, the approaches we take have broad connections to a...
University of Calcutta
Department of Biophysics, Molecular Biology & Bioinformatics
Applications are invited for admission to the Ph.D. programme in the Department of Biophysics, Molecular Biology & Bioinformatics, University of Calcutta...
http://phylobabble.org/ - Welcome to phylobabble.org, a discussion forum for phylogenetic theory and applications. The primary goal of this forum is to discuss best practice and new developments in phylogenetics. Although we do have a Troubleshooting category for getting...
https://r-graphics.org/ - R is powerful tool for data analysis, visualization, and machine learning. And it costs $0 to use! Here are six FREE books you can use to learn R...
Biologists estimate that there are about 5 to 100 million species of organisms living on Earth today. Evidence from morphological, biochemical, and gene sequence data suggests that all organisms on Earth are genetically related, and the genealogical...
github.com - Over the years most bioinformatics people amass a collection of small utility scripts which make their lives easier. Too often they are kept either in private repositories or as part of a public collection to which noone else can contribute. Biocode...
www.codeschool.com - Collections of Ruby and BioRuby learning materials.
BioRuby paper link : http://bioinformatics.oxfordjournals.org/content/26/20/2617.abstract
DEPARTMENT OF BIOTECHNOLOGY
(UGC SAP and DST-FIST & PURSE Sponsored Department)
ALAGAPPA UNIVERSITY
(A State University Accredited by NAAC with „A‟ Grade)
Karaikudi - 630 004, India
WALK IN INTERVIEW
A walk-in Interview for the...
www.r2d3.us - In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions.
More at http://www.r2d3.us/visual-intro-to-machine-learning-part-1/