Biomedical subjects

Li M Fu

Publications and source records attributed to Li M Fu.

4 recordsLinked to original sources

PubMed2004

Multi-class cancer subtype classification based on gene expression signatures with reliability analysis.

Differential diagnosis among a group of histologically similar cancers poses a challenging problem in clinical medicine. Constructing a classifier based on gene expression signatures comprising multiple discriminatory molecular markers derived from microarray data analysis is an emerging trend for cancer diagnosis. To identify the best genes for classification using a small number of samples relative to the genome size remains the bottleneck of this approach, despite its promise. We have devised a new method of gene selection with reliability analysis, and demonstrated that this method can identify a more compact set of genes than other methods for constructing a classifier with optimum predictive performance for both small round blue cell tumors and leukemia. High consensus between our result and the results produced by methods based on artificial neural networks and statistical techniques confers additional evidence of the validity of our method. This study suggests a way for implementing a reliable molecular cancer classifier based on gene expression signatures.

Artificial Intelligence↗

PubMed2003

TSGDB: a database system for tumor suppressor genes.

UNLABELLED: A Web-based database system was constructed and implemented that contains 174 tumor suppressor genes. The database homepage was created to accommodate these genes in a pull-down window so that each gene can be viewed individually in a separate Web page. Information displayed on each page includes gene name, aliases, source organism, chromosome location, expression cells/tissues, gene structure, protein size, gene functions and major reference sources. Queries to the database can be conducted through a user-friendly interface, and query results are returned in the HTML format on dynamically generated web pages. AVAILABILITY: The database is available at http://www.cise.ufl.edu/~yy1/HTML-TSGDB/Homepage.html (data files also at http://www.patcar.org/Databases/Tumor_Suppressor_Genes)

Abstracting and Indexing↗

PubMed2003

Improving reliability of gene selection from microarray functional genomics data.

Constructing a classifier based on microarray gene expression data has recently emerged as an important problem for cancer classification. Recent results have suggested the feasibility of constructing such a classifier with reasonable predictive accuracy under the circumstance where only a small number of cancer tissue samples of known type are available. Difficulty arises from the fact that each sample contains the expression data of a vast number of genes and these genes may interact with one another. Selection of a small number of critical genes is fundamental to correctly analyze the otherwise overwhelming data. It is essential to use a multivariate approach for capturing the correlated structure in the data. However, the curse of dimensionality leads to the concern about the reliability of selected genes. Here, we present a new gene selection method in which error and repeatability of selected genes are assessed within the context of M-fold cross-validation. In particular, we show that the method is able to identify source variables underlying data generation.

Algorithms↗

PubMed2002

Genome comparison of Mycobacterium tuberculosis and other bacteria.

The availability of the complete genome sequence of Mycobacterium tuberculosis allows its phylogenetic analysis based on the whole genome rather than single genes. As a genome-based tree is more representative of whole organisms and less inconsistent than single-gene trees, it could provide a better index for interpretation and inference about the origin and nature of species. The standard bacterial phylogeny based on 16S ribosomal RNA sequence comparison shows that M. tuberculosis is more related to Gram-positive than to Gram-negative bacteria. Our results based on genome comparison in terms of shared orthologous genes challenge this implication. We demonstrate that M. tuberculosis is more related to Gram-negative than to Gram-positive bacteria by a quantitative analysis on the genome tree. The numerical distance data derived from genome comparison and those from 16S rRNA comparison show high significant correlation, implying that conserved gene content carries a strong phylogenetic signature in evolution.

Animals↗