Predicting anti-cancer drug response by finding optimal subset of drugs.

Motivation: One of the most difficult challenges in precision medicine is determining the best treatment strategy for each patient based on personal information. Since drug response prediction in vitro is extremely expensive, time-consuming and virtually impossible, and because there are so many cell lines and drug data, computational methods are needed.

Results: MinDrug is a method for predicting anti-cancer drug response which try to identify the best subset of drugs that are the most similar to other drugs.

CAMND: Comparative analysis of metabolic network decomposition based on previous and two new criteria, a web based application.

Metabolic networks can model the behavior of metabolism in the cell. Since analyzing the whole metabolic networks is not easy, network modulation is an important issue to be investigated. Decomposing metabolic networks is a strategy to obtain better insight into metabolic functions.

A novel algorithm for parameter estimation of Hidden Markov Model inspired by Ant Colony Optimization.

HMM is a powerful method to model data in various fields. Estimation of Hidden Markov Model parameters is an NP-Hard problem. We propose a heuristic algorithm called "AntMarkov" to improve the efficiency of estimating HMM parameters.