Sponsorised links
October 2008
Conditional Random Fields
by ogrisel
(via)
Conditional random fields (CRFs) are a probabilistic framework for labeling and segmenting structured data, such as sequences, trees and lattices. The underlying idea is that of defining a conditional probability distribution over label sequences given a particular observation sequence, rather than a joint distribution over both label and observation sequences. The primary advantage of CRFs over hidden Markov models is their conditional nature, resulting in the relaxation of the independence assumptions required by HMMs in order to ensure tractable inference. Additionally, CRFs avoid the label bias problem, a weakness exhibited by maximum entropy Markov models (MEMMs) and other conditional Markov models based on directed graphical models. CRFs outperform both MEMMs and HMMs on a number of real-world tasks in many fields, including bioinformatics, computational linguistics and speech recognition.
March 2008
Dynamic programming and sequence alignment
by BlueVoodoo
Molecular biology is increasingly dependent on computer science algorithms as research tools. This article introduces you to bioinformatics — the use of computers to solve biological problems. Learn the basics of dynamic programming, an advanced algorithmic technique you may find useful in many of your programming projects.
January 2008
Sponsorised links
February 2007
January 2007
Tutorial in Bioinformatics
by serkan7
(via)
Bioinformatics and Homology Modeling:
A Student-Tested Tutorial for Beginners
