Open Access. Powered by Scholars. Published by Universities.®

Life Sciences Commons™

Open Access. Powered by Scholars. Published by Universities.®

Bioinformatics

PDF

Selected Works

2006

Articles 1 - 5 of 5

Full-Text Articles in Life Sciences

The Plant Structure Ontology, A Unified Vocabulary Of Anatomy And Morphology Of A Flowering Plant, Katica Ilic, Elizabeth A. Kellogg, Pankaj Jaiswal, Felipe Zapata, Peter F. Stevens, Leszek P. Vincent, Shulamit Avraham, Leonore Reiser, Anuradha Pujar, Martin M. Sachs, Noah T. Whitman, Susan R. Mccouch, Mary L. Schaeffer, Doreen H. Ware, Lincoln D. Stein, Seung Y. Rhee Dec 2006

The Plant Structure Ontology, A Unified Vocabulary Of Anatomy And Morphology Of A Flowering Plant, Katica Ilic, Elizabeth A. Kellogg, Pankaj Jaiswal, Felipe Zapata, Peter F. Stevens, Leszek P. Vincent, Shulamit Avraham, Leonore Reiser, Anuradha Pujar, Martin M. Sachs, Noah T. Whitman, Susan R. Mccouch, Mary L. Schaeffer, Doreen H. Ware, Lincoln D. Stein, Seung Y. Rhee

Peter Stevens

Formal description of plant phenotypes and standardized annotation of gene expression and protein localization data require uniform terminology that accurately describes plant anatomy and morphology. This facilitates cross species comparative studies and quantitative comparison of phenotypes and expression patterns. A major drawback is variable terminology that is used to describe plant anatomy and morphology in publications and genomic databases for different species. The same terms are sometimes applied to different plant structures in different taxonomic groups. Conversely, similar structures are named by their species-specific terms. To address this problem, we created the Plant Structure Ontology (PSO), the first generic ontological …

Go to article

Nonparametric Pathway-Based Regression Models For Analysis Of Genomic Data, Zhi Wei, Hongzhe Li Oct 2006

Nonparametric Pathway-Based Regression Models For Analysis Of Genomic Data, Zhi Wei, Hongzhe Li

Hongzhe Li

High-throughout genomic data provide an opportunity for identifying pathways and genes that are related to various clinical phenotypes. Besides these genomic data, another valuable source of data is the biological knowledge about genes and pathways that might be related to the phenotypes of many complex diseases. Databases of such knowledge are often called the metadata. In microarray data analysis, such metadata are currently explored in post hoc ways by gene set enrichment analysis but have hardly been utilized in the modeling step. We propose to develop and evaluate a pathway-based gradient descent boosting procedure for nonparametric pathways-based regression(NPR) analysis to …

Go to article

Group Additive Regression Models For Genomic Data Analysis, Yihui Luan, Hongzhe Li Oct 2006

Group Additive Regression Models For Genomic Data Analysis, Yihui Luan, Hongzhe Li

Hongzhe Li

One important problem in genomic research is to identify genomic features such as gene expression data or DNA single nucleotide polymorphisms (SNPs) that are related to clinical phenotypes. Often these genomic data can be naturally divided into biologically meaningful groups such as genes belonging to the same pathways or SNPs within genes. In this paper, we propose group additive regression models and a group gradient descent boosting procedure for identifying groups of genomic features that are related to clinical phenotypes. Our simulation results show that by dividing the variables into appropriate groups, we can obtain better identification of the group …

Go to article

Bi-Level Clustering Of Mixed Categorical And Numerical Biomedical Data, Bill Andreopoulos, Aijun An, Xiaogang Wang Jun 2006

Bi-Level Clustering Of Mixed Categorical And Numerical Biomedical Data, Bill Andreopoulos, Aijun An, Xiaogang Wang

William B. Andreopoulos

Biomedical data sets often have mixed categorical and numerical types, where the former represent semantic information on the objects and the latter represent experimental results. We present the BILCOM algorithm for |Bi-Level Clustering of Mixed categorical and numerical data types|. BILCOM performs a pseudo-Bayesian process, where the prior is categorical clustering. BILCOM partitions biomedical data sets of mixed types, such as hepatitis, thyroid disease and yeast gene expression data with Gene Ontology annotations, more accurately than if using one type alone.

Go to article

Multiple Sequence Alignment Accuracy And Phylogenetic Inference, T. Heath Ogden Dec 2005

Multiple Sequence Alignment Accuracy And Phylogenetic Inference, T. Heath Ogden

T. Heath Ogden

Phylogenies are often thought to be more dependent upon the specifics of the sequence alignment rather than on the method of reconstruction. Simulation of sequences containing insertion and deletion events was performed in order to determine the role that alignment accuracy plays during phylogenetic inference. Data sets were simulated for pectinate, balanced, and random tree shapes under different conditions (ultrametric equal branch length, ultrametric random branch length, nonultrametric random branch length). Comparisons between hypothesized alignments and true alignments enabled determination of two measures of alignment accuracy, that of the total data set and that of individual branches. In general, our …

Go to article