Design of novel medicine by bioinformaticsipharmacogenomicsj
For the development of novel and innovative medicines, most of researchers carefully screen and focus on the candidates and inspect their medicinal properties for the targeted disease by using huge number of molecular biology experiments. In usual, these procedures are time consuming and it takes long time for developing novel medicines. Recent advances in technology of throughput biological experiment have enabled the time for development of novel medicine to extremely be short. Famous examples for the high throughput biological experiments are DNA microarray, DNA chip, mass spectroheliogram and robot system for biological experiment. With these high throughput biological experiments, over thousand or ten thousand data can be collected within short time. In this study, a systematic approach for processing and handling massive data was employed by applying efficient statistical method and by using suitable techniques of information science. The developed approach is applied to actual biological data.
Clustering for gene expression data using DNA microarray or protein data.For more information can get for the googles.Fuzzy ART (adaptive resonance theory), Fuzzy k-means clustering, Hierarchical clusteringRational decision method for the number of clustering
Classification and recognition of biological and medical data for DNA microarray or proteins For more informationSupport vector machine (SVM), Artificial neural network (ANN), Fuzzy neural network (FNN), Nonlinear model, Statistical method Selection of important genes or proteins for classification or recognition (Feature selection)Integration analysis for plural database collected in plural medical institution (meta-analysis)
Estimation of interactive network for genes or proteinsFor more informationModification of Boolean or Bayesian model, identification of nonlinear model of S-system
System analysis and simulation of cancer or cell differentiation For more informationSystem analysis for mathematical simulation model for cell cycle and cell differentiation