AIMC Topic: Genes, Neoplasm

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Hybrid Method Based on Information Gain and Support Vector Machine for Gene Selection in Cancer Classification.

Genomics, proteomics & bioinformatics Dec 12, 2017
It remains a great challenge to achieve sufficient cancer classification accuracy with the entire set of genes, due to the high dimensions, small sample size, and big noise of gene expression data. We thus proposed a hybrid gene selection method, Inf...
Databases, Genetic Genes, Neoplasm Computational Biology Gene Expression Regulation, Neoplastic Support Vector Machine Humans Neoplasms
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A Cancer Gene Selection Algorithm Based on the K-S Test and CFS.

BioMed research international May 8, 2017
BACKGROUND: To address the challenging problem of selecting distinguished genes from cancer gene expression datasets, this paper presents a gene subset selection algorithm based on the Kolmogorov-Smirnov (K-S) test and correlation-based feature selec...
Support Vector Machine Genes, Neoplasm Humans Algorithms Animals Neoplasms Models, Genetic
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The mutational oncoprint of recurrent cytogenetic abnormalities in adult patients with de novo acute myeloid leukemia.

Leukemia Mar 24, 2017
Recurrent chromosomal abnormalities and gene mutations detected at the time of diagnosis of acute myeloid leukemia (AML) are associated with particular disease features, treatment response and survival of AML patients, and are used to denote specific...
Humans Female Aged Adult DNA, Neoplasm Age Factors Leukemia, Myeloid, Acute Gene Ontology Mutation Male Chromosome Aberrations Genes, Neoplasm DNA Mutational Analysis Middle Aged Karyotyping
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Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts.

Genomics Feb 1, 2017
Gene selection is a demanding task for microarray data analysis. The diverse complexity of different cancers makes this issue still challenging. In this study, a novel evolutionary method based on genetic algorithms and artificial intelligence is pro...
Artificial Intelligence Female Male Humans Neoplasms Oligonucleotide Array Sequence Analysis Genes, Neoplasm
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DeepGene: an advanced cancer type classifier based on deep learning and somatic point mutations.

BMC bioinformatics Dec 23, 2016
BACKGROUND: With the developments of DNA sequencing technology, large amounts of sequencing data have become available in recent years and provide unprecedented opportunities for advanced association studies between somatic point mutations and cancer...
Point Mutation Computational Biology Sequence Analysis, DNA Genes, Neoplasm Humans Neural Networks, Computer Neoplasms
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Identifying Individual-Cancer-Related Genes by Rebalancing the Training Samples.

IEEE transactions on nanobioscience Apr 12, 2016
The identification of individual-cancer-related genes typically is an imbalanced classification issue. The number of known cancer-related genes is far less than the number of all unknown genes, which makes it very hard to detect novel predictions fro...
Humans Machine Learning Algorithms Random Allocation Genes, Neoplasm Neoplasms Logistic Models
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Machine learning-based classification of diffuse large B-cell lymphoma patients by eight gene expression profiles.

Cancer medicine Feb 11, 2016
Gene expression profiling (GEP) had divided the diffuse large B-cell lymphoma (DLBCL) into molecular subgroups: germinal center B-cell like (GCB), activated B-cell like (ABC), and unclassified (UC) subtype. However, this classification with prognosti...
Antineoplastic Combined Chemotherapy Protocols Treatment Outcome Support Vector Machine Survival Analysis Cyclophosphamide Databases, Genetic Doxorubicin Middle Aged Gene Expression Profiling ROC Curve Humans Sensitivity and Specificity Genes, Neoplasm Prognosis Prednisone Antibodies, Monoclonal, Murine-Derived Female Rituximab Germinal Center Aged Vincristine Lymphoma, Large B-Cell, Diffuse Male
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Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection.

IEEE/ACM transactions on computational biology and bioinformatics Sep 14, 2015
Recently, feature selection and dimensionality reduction have become fundamental tools for many data mining tasks, especially for processing high-dimensional data such as gene expression microarray data. Gene expression microarray data comprises up t...
Databases, Genetic Sensitivity and Specificity Neoplasm Proteins Humans Data Mining Genetic Markers Neoplasms Machine Learning Genetic Predisposition to Disease Algorithms Pattern Recognition, Automated Reproducibility of Results Genes, Neoplasm
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Evaluation and integration of cancer gene classifiers: identification and ranking of plausible drivers.

Scientific reports May 11, 2015
The number of mutated genes in cancer cells is far larger than the number of mutations that drive cancer. The difficulty this creates for identifying relevant alterations has stimulated the development of various computational approaches to distingui...
Mutation United States Humans Genes, Neoplasm Databases, Genetic Machine Learning Neoplasm Proteins Animals
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Identifying new cancer genes based on the integration of annotated gene sets via hypergraph neural networks.

Bioinformatics (Oxford, England) Jun 28, 2024
MOTIVATION: Identifying cancer genes remains a significant challenge in cancer genomics research. Annotated gene sets encode functional associations among multiple genes, and cancer genes have been shown to cluster in hallmark signaling pathways and ...
Humans Computational Biology Databases, Genetic Neural Networks, Computer Molecular Sequence Annotation Neoplasms Genes, Neoplasm Genomics
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