The complexity of gene expression and the elucidation of the mechanisms involved in its regulation constitute an extremely difficult challenge in modern bioinformatics despite the amount of information made recently available by high-throughput biotechnologies and genome-wide investigations. In this contribution we investigated the effectiveness of ensemble systems for gene expression predictio...

BACKGROUND Although several methods have been proposed for predicting the effects of genetic variants and their role in disease, it is still a challenge to identify and prioritize pathogenic variants within sequencing studies. METHODS Here, we compare different variant and gene-specific features as well as existing methods and investigate their best combination to explore potential performanc...

Cancer subtype classification and survival prediction both relate directly to patients' specific treatment plans, making them fundamental medical issues. Although the two factors are interrelated learning problems, most studies tackle each separately. In this paper, expression levels of genes are used for both cancer subtype classification and survival prediction. We considered 350 diffuse larg...

We propose a methodology for constructing an integrated phenotype prediction model that accounts for multiple pathways regulating a targeted phenotype. The method uses multiple prediction models, each expressing a particular pattern of gene-to-gene interrelationship, such as epistasis. We also propose a methodology using Gene Ontology annotations to infer a biological mechanism from the integra...

Genes that are indispensable for survival are essential genes. Many features have been proposed for computational prediction of essential genes. In this paper, the least absolute shrinkage and selection operator method was used to screen key sequence-based features related to gene essentiality. To assess the effects, the selected features were used to predict the essential genes from 31 bacteri...

We show here that gene expression profiling, performed with conventional techniques and focused on a selected group of genes, when used in combination with standard clinical information, provides reliable prognostic prediction of prostate cancer (CaP). We showed previously that changes in the expression of metabolically related genes are involved in CaP progression. We then proceeded to search ...

In this review, we have discussed the class-prediction and discovery methods that are applied to gene expression data, along with the implications of the findings. We attempted to present a unified approach that considers both class-prediction and class-discovery. We devoted a substantial part of this review to an overview of pattern classification/recognition methods and discussed important is...

Gene expression profiling has been widely used to study molecular signatures of many diseases and to develop molecular diagnostics for disease prediction. Gene selection, as an important step for improved diagnostics, screens tens of thousands of genes and identifies a small subset that discriminates between disease types. A two-step gene selection method is proposed to identify informative gen...

BACKGROUND The ability to identify obese individuals who will successfully lose weight in response to dietary intervention will revolutionize disease management. Therefore, we asked whether it is possible to identify subjects who will lose weight during dietary intervention using only a single gene expression snapshot. METHODOLOGY/PRINCIPAL FINDINGS The present study involved 54 female subjec...