AIMC Topic: Gene Expression Profiling

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The feature selection bias problem in relation to high-dimensional gene data.

Artificial intelligence in medicine Nov 14, 2015
OBJECTIVE: Feature selection is a technique widely used in data mining. The aim is to select the best subset of features relevant to the problem being considered. In this paper, we consider feature selection for the classification of gene datasets. G...
Algorithms Decision Support Techniques Reproducibility of Results Biomarkers, Tumor Humans Linear Models Pattern Recognition, Automated Oligonucleotide Array Sequence Analysis Support Vector Machine Gene Expression Profiling Bias Databases, Genetic Computational Biology Gene Expression Regulation, Neoplastic Data Mining
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Microarray expression profiling and gene ontology analysis of long non-coding RNAs in spontaneously hypertensive rats and their potential roles in the pathogenesis of hypertension.

Molecular medicine reports Nov 11, 2015
Long non-coding RNAs (lncRNAs) have been demonstrated to be significant in numerous biological processes. Hypertension is a form of cardiovascular disease with at least one billion cases worldwide. The present study sought to compare the differential...
Down-Regulation Gene Ontology Oligonucleotide Array Sequence Analysis Rats, Inbred SHR Gene Expression Profiling Hypertension Real-Time Polymerase Chain Reaction RNA, Long Noncoding Reproducibility of Results Rats, Inbred WKY Gene Expression Regulation Animals
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Evaluation of data discretization methods to derive platform independent isoform expression signatures for multi-class tumor subtyping.

BMC genomics Nov 10, 2015
BACKGROUND: Many supervised learning algorithms have been applied in deriving gene signatures for patient stratification from gene expression data. However, transferring the multi-gene signatures from one analytical platform to another without loss o...
Glioblastoma RNA Isoforms Gene Expression Profiling Humans Computational Biology Machine Learning Cluster Analysis Algorithms
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Gene and sample selection using T-score with sample selection.

Journal of biomedical informatics Nov 7, 2015
Gene selection from high-dimensional microarray gene-expression data is statistically a challenging problem. Filter approaches to gene selection have been popular because of their simplicity, efficiency, and accuracy. Due to small sample size, all sa...
Computational Biology Logistic Models Algorithms Support Vector Machine Gene Expression Profiling Databases, Genetic
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Use of Artificial Intelligence and Machine Learning Algorithms with Gene Expression Profiling to Predict Recurrent Nonmuscle Invasive Urothelial Carcinoma of the Bladder.

The Journal of urology Oct 13, 2015
PURPOSE: Due to the high recurrence risk of nonmuscle invasive urothelial carcinoma it is crucial to distinguish patients at high risk from those with indolent disease. In this study we used a machine learning algorithm to identify the genes in patie...
Neoplasm Invasiveness Predictive Value of Tests Neoplasm Staging Biopsy Risk Assessment Gene Expression Profiling Urinary Bladder Neoplasms Carcinoma, Transitional Cell Polymerase Chain Reaction Machine Learning Algorithms Prognosis Sensitivity and Specificity Male Female Aged Humans Artificial Intelligence
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Prediction of recombinant protein overexpression in Escherichia coli using a machine learning based model (RPOLP).

Computers in biology and medicine Sep 30, 2015
Recombinant protein overexpression, an important biotechnological process, is ruled by complex biological rules which are mostly unknown, is in need of an intelligent algorithm so as to avoid resource-intensive lab-based trial and error experiments i...
Recombinant Proteins Escherichia coli Databases, Protein Gene Expression Regulation, Bacterial Machine Learning Reproducibility of Results Algorithms Artificial Intelligence Gene Expression Profiling
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Putative synaptic genes defined from a Drosophila whole body developmental transcriptome by a machine learning approach.

BMC genomics Sep 15, 2015
BACKGROUND: Assembly and function of neuronal synapses require the coordinated expression of a yet undetermined set of genes. Although roughly a thousand genes are expected to be important for this function in Drosophila melanogaster, just a few hund...
Machine Learning Algorithms Humans Datasets as Topic Computational Biology Models, Biological Animals Rats Synapses Gene Expression Regulation, Developmental Organ Specificity Gene Expression Profiling Drosophila Transcriptome
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Seq-ing improved gene expression estimates from microarrays using machine learning.

BMC bioinformatics Sep 4, 2015
BACKGROUND: Quantifying gene expression by RNA-Seq has several advantages over microarrays, including greater dynamic range and gene expression estimates on an absolute, rather than a relative scale. Nevertheless, microarrays remain in widespread use...
High-Throughput Nucleotide Sequencing Transcriptome Computational Biology Sequence Analysis, RNA Machine Learning Humans Gene Expression Profiling
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A tree-like Bayesian structure learning algorithm for small-sample datasets from complex biological model systems.

BMC systems biology Aug 28, 2015
BACKGROUND: There are increasing efforts to bring high-throughput systems biology techniques to bear on complex animal model systems, often with a goal of learning about underlying regulatory network structures (e.g., gene regulatory networks). Howev...
Transcription, Genetic Macaca mulatta Gene Expression Profiling Systems Biology Bayes Theorem Machine Learning Malaria Algorithms Animals
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Use of Semisupervised Clustering and Feature-Selection Techniques for Identification of Co-expressed Genes.

IEEE journal of biomedical and health informatics Jul 20, 2015
Studying the patterns hidden in gene-expression data helps to understand the functionality of genes. In general, clustering techniques are widely used for the identification of natural partitionings from the gene expression data. In order to put cons...
Cluster Analysis Supervised Machine Learning Humans Yeasts Gene Expression Profiling Arabidopsis Algorithms Computational Biology Rats Animals Databases, Genetic Fuzzy Logic
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