Biomedical subjects

Oliver D King

Publications and source records attributed to Oliver D King.

6 recordsLinked to original sources

PubMed2004

Intensity-based protein identification by machine learning from a library of tandem mass spectra.

Tandem mass spectrometry (MS/MS) has emerged as a cornerstone of proteomics owing in part to robust spectral interpretation algorithms. Widely used algorithms do not fully exploit the intensity patterns present in mass spectra. Here, we demonstrate that intensity pattern modeling improves peptide and protein identification from MS/MS spectra. We modeled fragment ion intensities using a machine-learning approach that estimates the likelihood of observed intensities given peptide and fragment attributes. From 1,000,000 spectra, we chose 27,000 with high-quality, nonredundant matches as training data. Using the same 27,000 spectra, intensity was similarly modeled with mismatched peptides. We used these two probabilistic models to compute the relative likelihood of an observed spectrum given that a candidate peptide is matched or mismatched. We used a 'decoy' proteome approach to estimate incorrect match frequency, and demonstrated that an intensity-based method reduces peptide identification error by 50-96% without any loss in sensitivity.

Algorithms↗

PubMed2003

Characterizing gene sets with FuncAssociate.

SUMMARY: FuncAssociate is a web-based tool to help researchers use Gene Ontology attributes to characterize large sets of genes derived from experiment. Distinguishing features of FuncAssociate include the ability to handle ranked input lists, and a Monte Carlo simulation approach that is more appropriate to determine significance than other methods, such as Bonferroni or idák p-value correction. FuncAssociate currently supports 10 organisms (Vibrio cholerae, Shewanella oneidensis, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Arabidopsis thaliana, Caenorhaebditis elegans, Drosophila melanogaster, Mus musculus, Rattus norvegicus and Homo sapiens). AVAILABILITY: FuncAssociate is freely accessible at http://llama.med.harvard.edu/Software.html. Source code (in Perl and C) is freely available to academic users 'as is'.

Algorithms↗

PubMed2003

A non-parametric model for transcription factor binding sites.

We introduce a non-parametric representation of transcription factor binding sites which can model arbitrary dependencies between positions. As two parameters are varied, this representation smoothly interpolates between the empirical distribution of binding sites and the standard position-specific scoring matrix (PSSM). In a test of generalization to unseen binding sites using 10-fold cross-validation on known binding sites for 95 TRANSFAC transcription factors, this representation outperforms PSSMs on between 65 and 89 of the 95 transcription factors, depending on the choice of the two adjustable parameters. We also discuss how the non- parametric representation may be incorporated into frameworks for finding binding sites given only a collection of unaligned promoter regions.

Base Sequence↗

PubMed2003

Predicting gene function from patterns of annotation.

The Gene Ontology (GO) Consortium has produced a controlled vocabulary for annotation of gene function that is used in many organism-specific gene annotation databases. This allows the prediction of gene function based on patterns of annotation. For example, if annotations for two attributes tend to occur together in a database, then a gene holding one attribute is likely to hold the other as well. We modeled the relationships among GO attributes with decision trees and Bayesian networks, using the annotations in the Saccharomyces Genome Database (SGD) and in FlyBase as training data. We tested the models using cross-validation, and we manually assessed 100 gene-attribute associations that were predicted by the models but that were not present in the SGD or FlyBase databases. Of the 100 manually assessed associations, 41 were judged to be true, and another 42 were judged to be plausible.

Animals↗

PubMed2003

GoFish finds genes with combinations of Gene Ontology attributes.

SUMMARY: GoFish is a Java application that allows users to search for gene products with particular gene ontology (GO) attributes, or combinations of attributes. GoFish ranks gene products by the degree to which they satisfy a Boolean query. Four organisms are currently supported: Saccaromyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, and M.musculus.

Amino Acid Sequence↗

PubMed2003

Predicting phenotype from patterns of annotation.

MOTIVATION: Predicting the outcome of specific experiments (such as the growth of a particular mutant strain in a particular medium) has the potential to allow researchers to devote resources to experiments with higher expected numbers of 'hits'. RESULTS: We use decision trees to predict phenotypes associated with Saccharomyces cerevisiae genes on the basis of Gene Ontology (GO) functional annotations from the Saccharomyces Genome Database (SGD) and other phenotypic annotations from the Yeast Phenotype Catalog at the Munich Information Center for Protein Sequences (MIPS). We assess the methodology in three ways: (1) we use cross-validation on the phenotypic annotations listed in MIPS, and show ROC curves indicating the tradeoff between true-positive rate and false-positive rate; (2) we do a literature-search for 100 of the predicted gene-phenotype associations that are not listed in MIPS, and find evidence for 43 of them; (3) we use deletion strains to experimentally assess 61 predicted gene-phenotype associations not listed in MIPS; significantly more of these deletion strains show abnormal growth than would be expected by chance.

Algorithms↗