AIMC Topic: Neoplasms

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A Cancer Gene Selection Algorithm Based on the K-S Test and CFS.

BioMed research international
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...

Deep Learning for Automated Extraction of Primary Sites From Cancer Pathology Reports.

IEEE journal of biomedical and health informatics
Pathology reports are a primary source of information for cancer registries which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. In this study, we investigated deep learning...

Optimization of Gene Set Annotations Using Robust Trace-Norm Multitask Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Gene set enrichment (GSE) is a useful tool for analyzing and interpreting large molecular datasets generated by modern biomedical science. The accuracy and reproducibility of GSE analysis are heavily affected by the quality and integrity of gene sets...

Revealing common disease mechanisms shared by tumors of different tissues of origin through semantic representation of genomic alterations and topic modeling.

BMC genomics
BACKGROUND: Cancer is a complex disease driven by somatic genomic alterations (SGAs) that perturb signaling pathways and consequently cellular function. Identifying patterns of pathway perturbations would provide insights into common disease mechanis...

CATTLE (CAncer treatment treasury with linked evidence): An integrated knowledge base for personalized oncology research and practice.

CPT: pharmacometrics & systems pharmacology
Despite the existence of various databases cataloging cancer drugs, there is an emerging need to support the development and application of personalized therapies, where an integrated understanding of the clinical factors and drug mechanism of action...

Relating Complexity and Error Rates of Ontology Concepts. More Complex NCIt Concepts Have More Errors.

Methods of information in medicine
OBJECTIVES: Ontologies are knowledge structures that lend support to many health-information systems. A study is carried out to assess the quality of ontological concepts based on a measure of their complexity. The results show a relation between com...

Analysis of cancer-related lncRNAs using gene ontology and KEGG pathways.

Artificial intelligence in medicine
BACKGROUND: Cancer is a disease that involves abnormal cell growth and can invade or metastasize to other tissues. It is known that several factors are related to its initiation, proliferation, and invasiveness. Recently, it has been reported that lo...

Development of a two-stage gene selection method that incorporates a novel hybrid approach using the cuckoo optimization algorithm and harmony search for cancer classification.

Journal of biomedical informatics
For each cancer type, only a few genes are informative. Due to the so-called 'curse of dimensionality' problem, the gene selection task remains a challenge. To overcome this problem, we propose a two-stage gene selection method called MRMR-COA-HS. In...

Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts.

Genomics
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...