Computer Science

Background Cancer gene expression profiling is an indispensable tool for identifying drivers of tumor progression, identifying subtypes, and predicting clinical outcome. An outstanding challenge faced by cancer gene expression studies is the limited concordance between studies [1], driven in part by lack of statistical power [2]. Part of this lack of statistical power is due to the fact that tumor samples from some solid cancers contain between 30%-70% healthy tissue [3].

Recent mammalian microarray experiments detected widespread transcription and indicated that there may be many undiscovered multiple-exon protein-coding genes. To explore this possibility, we labeled cDNA from unamplified, polyadenylation-selected RNA samples from 37 mouse tissues to microarrays encompassing 1.14 million exon probes. We analyzed these data using GenRate, a Bayesian algorithm that uses a genome-wide scoring function in a factor graph to infer genes.

The ability to collect functional data, like physical and genetic interactions and co-expression, about every gene in the genome is expanding the possibilities of biological research. However, navigating through these data can be a difficult task for those without specialized computational expertise. GeneMANIA uses many large publicly available datasets to analyze gene lists and identify highthroughput data sources in which the query genes are highly associated.

We present a new model and learning algorithm, GenMiR3, which takes into account mRNA sequence features in addition to paired mRNA and miRNA expression profiles when scoring candidate miRNA-mRNA interactions. We evaluate three candidate sequence features for predicting miRNA targets by assessing the expression support for the predictions of each feature and the consistency of Gene Ontology Biological Process annotation of their target sets.

Background The use of biological molecular network information for diagnostic and prognostic purposes and elucidation of molecular disease mechanism is a key objective in systems biomedicine. The network of regulatory miRNA-target and functional protein interactions is a rich source of information to elucidate the function and the prognostic value of miRNAs in cancer. The objective of this study is to identify miRNAs that have high influence on target protein complexes in prostate cancer as a case study.

The design and use of expert systems for medical diagnosis remains an attractive goal. One such system, the Quick Medical Reference, Decision Theoretic (QMR-DT), is based on a Bayesian network. This very large-scale network models the appearance and manifestation of disease and has approximately 600 unobservable nodes and 4000 observable nodes that represent, respectively, the presence and measurable manifestation of disease in a patient.

As an alternative to GenASAP, one can use the available RT-PCR measurements as a training set and use supervised machine learning algorithms, such as nearest neighbor or support vector machines, to learn to predict the exclusion levels of the AS events from the microarray data. here, we compare four widely used methods to GenASAP.

We demonstrate that paired expression profiles of microRNAs (miRNAs) and mRNAs can be used to identify functional miRNA-target relationships with high precision. We used a Bayesian data analysis algorithm, GenMiR++, to identify a network of 1,597 high-confidence target predictions for 104 human miRNAs, which was supported by RNA expression data across 88 tissues and cell types, sequence complementarity and comparative genomics data.

Motivation: Recognition of specific DNA sequences is a central mechanism by which transcription factors (TFs) control gene expression. Many TF-binding preferences, however, are unknown or poorly characterized, in part due to the difficulty associated with determining their specificity experimentally, and an incomplete understanding of the mechanisms governing sequence specificity.

Joint genotyping and large-scale phenotyping of molecular traits are currently available for a number of important patient study cohorts and will soon become feasible in routine medical practice. These data are one component of several that are setting the stage for the development of personalized medicine, promising to yield better disease classification, enabling more specific treatment, and also allowing for improved preventive medical screening.

This paper introduces a probability model, the mixture of trees that can account for sparse, dynamically changing dependence relationships. We present a family of efficient algorithms that use EM and the Minimum Spanning Tree algorithm to find the ML and MAP mixture of trees for a variety of priors, including the Dirichlet and the MDL priors. We also show that the single tree classifier acts like an implicit feature selector, thus making the classification performance insensitive to irrelevant attributes.

Abstract Motivation: Recognition of specific DNA sequences is a central mechanism by which transcription factors (TFs) control gene expression. Many TF-binding preferences, however, are unknown or poorly characterized, in part due to the difficulty associated with determining their specificity experimentally, and an incomplete understanding of the mechanisms governing sequence specificity.

Abstract Background: Several years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help focus limited experimental resources on the most likely hypotheses. Several algorithms using diverse genomic data have been applied to this task in model organisms; however, the performance of such approaches in mammals has not yet been evaluated.

Identifying gene function has many useful applications especially in Gene Therapy. Identifying gene function based on gene expression data is much easier in prokaryotes than eukaryotes due to the relatively simple structure of prokaryotes. That is why tissue-specific expression is the primary tool for identifying gene function in eukaryotes. However, recent studies have shown that there is a strong learnable correlation between gene function and gene expression.

Approximately half of known human miRNAs are located in the introns of protein coding genes. Some of these intronic miRNAs are only expressed when their host gene is and, as such, their steady state expression levels are highly correlated with those of the host gene's mRNA. Recently host gene expression levels have been used to predict the targets of intronic miRNAs by identifying other mRNAs that they have consistent negative correlation with.

Summary the precise mechanism (s) by which most of the rRNA endonucleolytic steps occur. Additional rRNA-process-Predictive analysis using publicly available yeast funcing factors continue to be reported (eg, Bassler et al., tional genomics and proteomics data suggests that 2001; Harnpicharnchai et al., 2001; Dragon et al., 2002), many more proteins may be involved in biogenesis of indicating that the rRNA processing machinery has not ribonucleoproteins than are currently known.

MicroRNAs (miRNAs) regulate a large proportion of mammalian genes by hybridizing to targeted messenger RNAs (mRNAs) and down-regulating their translation into protein. Although much work has been done in the genome-wide computational prediction of miRNA genes and their target mRNAs, an open question is how to efficiently obtain functional miRNA targets from a large number of candidate miRNA targets predicted by existing computational algorithms.

Genome Wide Association (GWA) studies resulted in discovery of genetic variants underlying several complex diseases including Chron's disease and age-related macular degeneration (AMD). Still geneticists find that in majority of studies the size of the effect even if it is significant tends to be very small. There are several factors contributing to this problem such as rare variants, complex relationships among SNPs (epistatic effect), and heterogeneity of the phenotype.

A major objective in post-genome research is to fully understand the transcriptional control of each gene and the targets of each transcription factor. In yeast, large-scale experimental and computational approaches have been applied to identify co-regulated genes, cis regulatory elements, and transcription factor DNA binding sites in vivo. Methods for modeling and predicting system behavior, and for reconciling discrepancies among data types, are being explored.

Background Epigenetic modifications, transcription factor (TF) availability and differences in chromatin folding influence how the genome is interpreted by the transcriptional machinery responsible for gene expression. Enhancers buried in non-coding regions are found to be associated with significant differences in histone marks between different cell types. In contrast, gene promoters show more uniform modifications across cell types.