AIMC Topic: Databases, Genetic

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ChIP-PIT: Enhancing the Analysis of ChIP-Seq Data Using Convex-Relaxed Pair-Wise Interaction Tensor Decomposition.

IEEE/ACM transactions on computational biology and bioinformatics
In recent years, thanks to the efforts of individual scientists and research consortiums, a huge amount of chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) experimental data have been accumulated. Instead of investigati...

KNOWLEDGE-ASSISTED APPROACH TO IDENTIFY PATHWAYS WITH DIFFERENTIAL DEPENDENCIES.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
We have previously developed a statistical method to identify gene sets enriched with condition-specific genetic dependencies. The method constructs gene dependency networks from bootstrapped samples in one condition and computes the divergence betwe...

The Plant Ontology: A Tool for Plant Genomics.

Methods in molecular biology (Clifton, N.J.)
The use of controlled, structured vocabularies (ontologies) has become a critical tool for scientists in the post-genomic era of massive datasets. Adoption and integration of common vocabularies and annotation practices enables cross-species comparat...

Using Semantic Similarities and csbl.go for Analyzing Microarray Data.

Methods in molecular biology (Clifton, N.J.)
Cellular phenotypes result from the combined effect of multiple genes, and high-throughput techniques such as DNA microarrays and deep sequencing allow monitoring this genomic complexity. The large scale of the resulting data, however, creates challe...

A novel random forests-based feature selection method for microarray expression data analysis.

International journal of data mining and bioinformatics
High-dimensional data and a large number of redundancy features in bioinformatics research have created an urgent need for feature selection. In this paper, a novel random forests-based feature selection method is proposed that adopts the idea of str...

Gene function prediction with knowledge from gene ontology.

International journal of data mining and bioinformatics
Gene function prediction is an important problem in bioinformatics. Due to the inherent noise existing in the gene expression data, the attempt to improve the prediction accuracy resorting to new classification techniques is limited. With the emergen...

Ensemble of sparse classifiers for high-dimensional biological data.

International journal of data mining and bioinformatics
Biological data are often high in dimension while the number of samples is small. In such cases, the performance of classification can be improved by reducing the dimension of data, which is referred to as feature selection. Recently, a novel feature...

Employing social network analysis for disease biomarker detection.

International journal of data mining and bioinformatics
Detection of disease biomarkers in general and cancer biomarkers in particular is an important task which has received considerable attention in the area of in silico genomic experiments. We describe a new approach for detecting cancer biomarkers bas...

Regularised extreme learning machine with misclassification cost and rejection cost for gene expression data classification.

International journal of data mining and bioinformatics
The main purpose of traditional classification algorithms on bioinformatics application is to acquire better classification accuracy. However, these algorithms cannot meet the requirement that minimises the average misclassification cost. In this pap...