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The identification of new therapeutic uses of existing drugs, or drug repositioning, offers the possibility of faster drug development, reduced risk, lesser cost and shorter paths to approval. Several computational methods have been proposed in the literature that makes use of publicly available transcriptional data to reposition drugs against diseases. In this work, we carry out a data mining process...
Discovering functional gene clusters based on gene expression data has been a widely-used method that offers a tremendous opportunity for understanding the functional genomics of a specific disease. Due to its strong power of comprehending and interpreting mass of genes, plenty of studies have been done on detecting and analyzing the gene clusters for various diseases. However, more and more evidence...
For prognostic and diagnostic purposes, it is crucial to be able to separate the group of “driver” genes and their first-degree neighbours, (i.e. “core module”) from the general “disease module”. To facilitate this task, we developed a novel computational framework COMBINER: COre Module Biomarker Identification with Network ExploRation. We applied COMBINER to three benchmark breast cancer datasets...
This study is based on a simple hypothesis - “ideal” drugs for a patient can cure the patient's disease by modulating the gene expression profile of the patient to a similar level with those in healthy people, on the pathway level. To verify this hypothesis, we present a computational framework to evaluate drug effects on gene expression profiles in breast cancer. First, a breast cancer pathway model...
The availability of genome-wide biological network data opens up new possibilities to discover novel biomarkers and elucidate cancer-related complex mechanisms at network level. In this paper, we propose a novel module-based feature selection framework, which integrates biological network information and gene expression data to identify biomarkers, not as individual genes but as functional modules...
We investigate the potential to enhance breast cancer event predictors by exploiting subtype information. We do this with a two-stage approach that first determines a sample's subtype using a recent module-driven approach, and secondly constructs a subtype-specific predictor to predict a metastasis event within five years. Our methodology is validated on a large compendium of microarray breast cancer...
Partial AUC (pAUC) represents the area with a restricted range of specificity (e.g. low false positive rate). It may identify important regional differentiated genes missed by full-range analysis. Unlike the popular t-test, which is based on the mean difference and the standard deviation between the disease and health groups, pAUC based test statistic relies on the rank of a gene in different samples...
Due to the inherent measurement noise in microarray experiments, heterogeneity across samples, and limited sample size, it is often hard to find reliable gene markers for classification. For this reason, several studies proposed to analyze the expression data at the level of groups of functionally related genes such as pathways. One practical problem of these pathway-based approaches is the limited...
The relationship between alcohol consumption and onsets of breast cancer (BC) keeps obscure. A local gene network (LGN) was constructed using 24 breast cancer susceptibility genes that participate in known signal transduction pathways. It was found that the LGN was able to directly link several genes (ALDH, ADH1B and HDAC1) that have approved functions in alcohol metabolism. Further analysis indicated...
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