Biomedical subjects

Xinglai Ji

Publications and source records attributed to Xinglai Ji.

4 recordsLinked to original sources

PubMed2005

libSRES: a C library for stochastic ranking evolution strategy for parameter estimation.

SUMMARY: Estimation of kinetic parameters in a biochemical pathway or network represents a common problem in systems studies of biological processes. We have implemented a C library, named libSRES, to facilitate a fast implementation of computer software for study of non-linear biochemical pathways. This library implements a (mu, lambda)-ES evolutionary optimization algorithm that uses stochastic ranking as the constraint handling technique. Considering the amount of computing time it might require to solve a parameter-estimation problem, an MPI version of libSRES is provided for parallel implementation, as well as a simple user interface. libSRES is freely available and could be used directly in any C program as a library function. We have extensively tested the performance of libSRES on various pathway parameter-estimation problems and found its performance to be satisfactory. AVAILABILITY: The source code (in C) is free for academic users at http://csbl.bmb.uga.edu/~jix/science/libSRES/

Algorithms↗

PubMed2004

HMMGEP: clustering gene expression data using hidden Markov models.

SUMMARY: The package HMMGEP performs cluster analysis on gene expression data using hidden Markov models. AVAILABILITY: HMMGEP, including the source code, documentation and sample data files, is available at http://www.bioinfo.tsinghua.edu.cn:8080/~rich/hmmgep_download/index.html.

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PubMed2004

DBSubLoc: database of protein subcellular localization.

We have built a protein subcellular localization annotation database, the DBSubLoc database, which is available at http://www.bioinfo.tsinghua. edu.cn/dbsubloc.html. Annotations were taken from primary protein databases, model organism genome projects and literature texts, and then were analyzed to dig out the subcellular localization features of the proteins. The proteins are also classified into different categories. Based on sequence alignment, non-redundant subsets of the database have been built, which may provide useful information for subcellular localization prediction. The database now contains >60,000 protein sequences including approximately 30,000 protein sequences in the non-redundant data sets. Online download, search and Blast tools are also available.

Animals↗

PubMed2003

Mining gene expression data using a novel approach based on hidden Markov models.

In this work we have developed a new framework for microarray gene expression data analysis. This framework is based on hidden Markov models. We have benchmarked the performance of this probability model-based clustering algorithm on several gene expression datasets for which external evaluation criteria were available. The results showed that this approach could produce clusters of quality comparable to two prevalent clustering algorithms, but with the major advantage of determining the number of clusters. We have also applied this algorithm to analyze published data of yeast cell cycle gene expression and found it able to successfully dig out biologically meaningful gene groups. In addition, this algorithm can also find correlation between different functional groups and distinguish between function genes and regulation genes, which is helpful to construct a network describing particular biological associations. Currently, this method is limited to time series data. Supplementary materials are available at http://www.bioinfo.tsinghua.edu.cn/~rich/hmmgep_supp/.

Algorithms↗