AIMC Topic: Gene Expression Profiling

Clear Filters Showing 1591 to 1600 of 1601 articles

Mining Gene Regulatory Networks by Neural Modeling of Expression Time-Series.

IEEE/ACM transactions on computational biology and bioinformatics
Discovering gene regulatory networks from data is one of the most studied topics in recent years. Neural networks can be successfully used to infer an underlying gene network by modeling expression profiles as times series. This work proposes a novel...

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...

Fuzzy Logic as a Computational Tool for Quantitative Modelling of Biological Systems with Uncertain Kinetic Data.

IEEE/ACM transactions on computational biology and bioinformatics
Quantitative modelling of biological systems has become an indispensable computational approach in the design of novel and analysis of existing biological systems. However, kinetic data that describe the system's dynamics need to be known in order to...

A genetic filter for cancer classification on gene expression data.

Bio-medical materials and engineering
We present a new genetic filter to identify a predictive gene subset for cancer-type classification on gene expression profiles. This approach pursues to not only maximize correlation between selected genes and cancer types but also minimize inter-co...

A label distance maximum-based classifier for multi-label learning.

Bio-medical materials and engineering
Multi-label classification is useful in many bioinformatics tasks such as gene function prediction and protein site localization. This paper presents an improved neural network algorithm, Max Label Distance Back Propagation Algorithm for Multi-Label ...

A hybrid ensemble method based on double disturbance for classifying microarray data.

Bio-medical materials and engineering
Microarray data has small samples and high dimension, and it contains a significant amount of irrelevant and redundant genes. This paper proposes a hybrid ensemble method based on double disturbance to improve classification performance. Firstly, ori...

An effective fuzzy kernel clustering analysis approach for gene expression data.

Bio-medical materials and engineering
Fuzzy clustering is an important tool for analyzing microarray data. A major problem in applying fuzzy clustering method to microarray gene expression data is the choice of parameters with cluster number and centers. This paper proposes a new approac...

Adaptive Fuzzy Consensus Clustering Framework for Clustering Analysis of Cancer Data.

IEEE/ACM transactions on computational biology and bioinformatics
Performing clustering analysis is one of the important research topics in cancer discovery using gene expression profiles, which is crucial in facilitating the successful diagnosis and treatment of cancer. While there are quite a number of research w...

Patient-specific early classification of multivariate observations.

International journal of data mining and bioinformatics
Early classification of time series has been receiving a lot of attention recently. In this paper we present a model, which we call the Early Classification Model (ECM), that allows for early, accurate and patient-specific classification of multivari...

Machine learning from concept to clinic: reliable detection of BRAF V600E DNA mutations in thyroid nodules using high-dimensional RNA expression data.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
The promise of personalized medicine will require rigorously validated molecular diagnostics developed on minimally invasive, clinically relevant samples. Measurement of DNA mutations is increasingly common in clinical settings but only higher-preval...