Biomedical subjects

T S Furey

Publications and source records attributed to T S Furey.

4 recordsLinked to original sources

PubMed2001

Integration of cytogenetic landmarks into the draft sequence of the human genome.

We have placed 7,600 cytogenetically defined landmarks on the draft sequence of the human genome to help with the characterization of genes altered by gross chromosomal aberrations that cause human disease. The landmarks are large-insert clones mapped to chromosome bands by fluorescence in situ hybridization. Each clone contains a sequence tag that is positioned on the genomic sequence. This genome-wide set of sequence-anchored clones allows structural and functional analyses of the genome. This resource represents the first comprehensive integration of cytogenetic, radiation hybrid, linkage and sequence maps of the human genome; provides an independent validation of the sequence map and framework for contig order and orientation; surveys the genome for large-scale duplications, which are likely to require special attention during sequence assembly; and allows a stringent assessment of sequence differences between the dark and light bands of chromosomes. It also provides insight into large-scale chromatin structure and the evolution of chromosomes and gene families and will accelerate our understanding of the molecular bases of human disease and cancer.

Chromosome Aberrations↗

PubMed2001

Promoter region-based classification of genes.

In this paper we consider the problem of extracting information from the upstream untranslated regions of genes to make predictions about their transcriptional regulation. We present a method for classifying genes based on motif-based hidden Markov models (HMMs) of their promoter regions. Sequence motifs discovered in yeast promoters are used to construct HMMs that include parameters describing the number and relative locations of motifs within each sequence. Each model provides a Fisher kernel for a support vector machine, which can be used to predict the classifications of unannotated promoters. We demonstrate this method on two classes of genes from the budding yeast, S. cerevisiae. Our results suggest that the additional sequence features captured by the HMM assist in correctly classifying promoters.

Algorithms↗

PubMed2000

Knowledge-based analysis of microarray gene expression data by using support vector machines.

We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.

Algorithms↗

PubMed2000

Support vector machine classification and validation of cancer tissue samples using microarray expression data.

MOTIVATION: DNA microarray experiments generating thousands of gene expression measurements, are being used to gather information from tissue and cell samples regarding gene expression differences that will be useful in diagnosing disease. We have developed a new method to analyse this kind of data using support vector machines (SVMs). This analysis consists of both classification of the tissue samples, and an exploration of the data for mis-labeled or questionable tissue results. RESULTS: We demonstrate the method in detail on samples consisting of ovarian cancer tissues, normal ovarian tissues, and other normal tissues. The dataset consists of expression experiment results for 97,802 cDNAs for each tissue. As a result of computational analysis, a tissue sample is discovered and confirmed to be wrongly labeled. Upon correction of this mistake and the removal of an outlier, perfect classification of tissues is achieved, but not with high confidence. We identify and analyse a subset of genes from the ovarian dataset whose expression is highly differentiated between the types of tissues. To show robustness of the SVM method, two previously published datasets from other types of tissues or cells are analysed. The results are comparable to those previously obtained. We show that other machine learning methods also perform comparably to the SVM on many of those datasets. AVAILABILITY: The SVM software is available at http://www.cs. columbia.edu/ approximately bgrundy/svm.

Acute Disease↗