Biomedical subjects

Paul Horton

Publications and source records attributed to Paul Horton.

3 recordsLinked to original sources

PubMed2006

Comparative genomic analysis of transcription regulation elements involved in human map kinase G-protein coupling pathway.

The identification of cis-elements (motifs) in the regulatory regions of higher eukaryotes is an important and challenging problem in computational biology. Eukaryotic transcriptional regulatory mechanisms pose several difficulties for promoter analysis: including a high variance in the motif locations, frequently large divergence from motif consensus patterns, and a large amount of repetitive elements (confusing to many motif finding procedures). One promising approach to this difficult problem involves cross-species comparison. In this work we analyzed the full-length regulatory regions of genes involved in the G-protein coupling MAP kinase pathway and compared the results with ribosomal genes using human, mouse and rat genomic data. We found 19 high likely transcription factors (TFs) candidates for MAPK and 12 TFs for the ribosomal dataset. In the case of the MAPK dataset, regulatory regions of genes functionally grouped as receptors and MAP-core genes were found mostly highly conserved across the three species.

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PubMed2006

Inference of scale-free networks from gene expression time series.

Quantitative time-series observation of gene expression is becoming possible, for example by cell array technology. However, there are no practical methods with which to infer network structures using only observed time-series data. As most computational models of biological networks for continuous time-series data have a high degree of freedom, it is almost impossible to infer the correct structures. On the other hand, it has been reported that some kinds of biological networks, such as gene networks and metabolic pathways, may have scale-free properties. We hypothesize that the architecture of inferred biological network models can be restricted to scale-free networks. We developed an inference algorithm for biological networks using only time-series data by introducing such a restriction. We adopt the S-system as the network model, and a distributed genetic algorithm to optimize models to fit its simulated results to observed time series data. We have tested our algorithm on a case study (simulated data). We compared optimization under no restriction, which allows for a fully connected network, and under the restriction that the total number of links must equal that expected from a scale free network. The restriction reduced both false positive and false negative estimation of the links and also the differences between model simulation and the given time-series data.

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PubMed2005

Discrimination of outer membrane proteins using support vector machines.

MOTIVATION: Discriminating outer membrane proteins from other folding types of globular and membrane proteins is an important task both for dissecting outer membrane proteins (OMPs) from genomic sequences and for the successful prediction of their secondary and tertiary structures. RESULTS: We have developed a method based on support vector machines using amino acid composition and residue pair information. Our approach with amino acid composition has correctly predicted the OMPs with a cross-validated accuracy of 94% in a set of 208 proteins. Further, this method has successfully excluded 633 of 673 globular proteins and 191 of 206 alpha-helical membrane proteins. We obtained an overall accuracy of 92% for correctly picking up the OMPs from a dataset of 1087 proteins belonging to all different types of globular and membrane proteins. Furthermore, residue pair information improved the accuracy from 92 to 94%. This accuracy of discriminating OMPs is higher than that of other methods in the literature, which could be used for dissecting OMPs from genomic sequences. AVAILABILITY: Discrimination results are available at http://tmbeta-svm.cbrc.jp.

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