AIMC Topic: Gene Expression Regulation, Neoplastic
Clear Filters Showing 451 to 460 of 589 articles
Identifying microRNAs involved in cancer pathway using support vector machines.
Computational biology and chemistry
Jan 29, 2015
Since Ambros' discovery of small non-protein coding RNAs in the early 1990s, the past two decades have seen an upsurge in the number of reports of predicted microRNAs (miR), which have been implicated in various functions. The correlation of miRs wit...
A fast gene selection method for multi-cancer classification using multiple support vector data description.
Journal of biomedical informatics
Dec 27, 2014
For cancer classification problems based on gene expression, the data usually has only a few dozen sizes but has thousands to tens of thousands of genes which could contain a large number of irrelevant genes. A robust feature selection algorithm is r...
Pattern Recognition, Automated
Oligonucleotide Array Sequence Analysis
Gene Expression Regulation, Leukemic
Leukemia
Software
Algorithms
Neoplasms
Gene Expression Profiling
Humans
Diagnosis, Computer-Assisted
Artificial Intelligence
Gene Expression Regulation, Neoplastic
Models, Statistical
Support Vector Machine
Bayes Theorem
Gene Expression
Colonic Neoplasms
Classification of lung cancer using ensemble-based feature selection and machine learning methods.
Molecular bioSystems
Dec 16, 2014
Lung cancer is one of the leading causes of death worldwide. There are three major types of lung cancers, non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC) and carcinoid. NSCLC is further classified into lung adenocarcinoma (LADC), sq...
Identifying predictive features in drug response using machine learning: opportunities and challenges.
Annual review of pharmacology and toxicology
Dec 12, 2014
This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction ...
Gene Expression Regulation, Neoplastic
Neural Networks, Computer
Cluster Analysis
Artificial Intelligence
Pattern Recognition, Automated
Precision Medicine
Humans
Drug Discovery
Algorithms
Patient Selection
Pharmacology
Risk Factors
Patient Safety
Antineoplastic Agents
Drug-Related Side Effects and Adverse Reactions
Animals
Gene Regulatory Networks
Risk Assessment
Illuminating the Noncoding Genome in Cancer Using Artificial Intelligence.
Cancer research
Jul 2, 2025
Understanding the vast noncoding cancer genome requires cutting-edge, high-resolution, and accessible strategies. Artificial intelligence is revolutionizing cancer research, enabling advanced models to analyze genome regulation. This review examines ...
Global Thyroid Cancer Patterns and Predictive Analytics: Integrating Machine Learning for Advanced Diagnostic Modelling.
Journal of cellular and molecular medicine
Jul 1, 2025
BACKGROUND: The global increase in thyroid cancer prevalence, particularly among female populations, underscores critical gaps in our understanding of molecular pathogenesis and diagnostic capabilities. Our investigation addresses these knowledge def...
Characterization of m6A-Related Genes in Tumor-Associated Macrophages for Prognosis, Immunotherapy, and Drug Prediction in Lung Adenocarcinomas Based on Machine Learning Algorithms.
FASEB journal : official publication of the Federation of American Societies for Experimental Biology
Jun 15, 2025
Tumor-associated macrophages (TAMs) are a vital immune component within the tumor microenvironment (TME) of lung adenocarcinoma (LUAD), exerting significant influence on tumor growth, metastasis, and drug resistance. N6-methyladenosine (m6A) modifica...
Development and experimental verification of a prognosis model for hypoxia- and lactate metabolism-associated genes in HNSCC.
Medicine
Jun 13, 2025
Hypoxia and lactate metabolism are both distinctive characteristics of cancerous cells. Head and neck squamous cell carcinoma (HNSCC) is one of the most prevalent forms of cancer. The objective of this study was to construct a prognostic model of gen...
Machine Learning-Assisted Analysis of the Oral Cancer Immune Microenvironment: From Single-Cell Level to Prognostic Model Construction.
Journal of cellular and molecular medicine
Jun 1, 2025
Oral cancer is among the most prevalent malignant tumours worldwide; prognosis can be affected by several factors, including molecular subtypes, immune microenvironment and clinical characteristics. In this study, we aimed to apply machine learning m...
Autophagy-related gene SQSTM1 predicts the prognosis of hepatocellular carcinoma.
Computers in biology and medicine
Jun 1, 2025
BACKGROUND: The relationship between autophagy and the progression of hepatocellular carcinoma (HCC) is notably substantial, yet the underlying mechanisms remain incompletely elucidated. Our objective is to construct a predictive model, thereby provi...
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