AIMC Topic: Gene Expression Regulation, Neoplastic
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Patterns of Gene Expression Profiles Associated with Colorectal Cancer in Colorectal Mucosa by Using Machine Learning Methods.
Combinatorial chemistry & high throughput screening
Jan 1, 2024
BACKGROUND: Colorectal cancer (CRC) has a very high incidence and lethality rate and is one of the most dangerous cancer types. Timely diagnosis can effectively reduce the incidence of colorectal cancer. Changes in para-cancerous tissues may serve as...
Machine Learning Algorithms Identify Target Genes and the Molecular Mechanism of Matrine against Diffuse Large B-cell Lymphoma.
Current computer-aided drug design
Jan 1, 2024
BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin's lymphoma worldwide. Novel treatment strategies are still needed for this disease.
Machine learning on thyroid disease: a review.
Frontiers in bioscience (Landmark edition)
Mar 16, 2022
This study reviews the recent progress of machine learning for the early diagnosis of thyroid disease. Based on the results of this review, different machine learning methods would be appropriate for different types of data for the early diagnosis of...
Detection of subtype-specific breast cancer surface protein biomarkers via a novel transcriptomics approach.
Bioscience reports
Dec 22, 2021
BACKGROUND: Cell-surface proteins have been widely used as diagnostic and prognostic markers in cancer research and as targets for the development of anticancer agents. So far, very few attempts have been made to characterize the surfaceome of patien...
An artificial neural network model based on DNA damage response genes to predict outcomes of lower-grade glioma patients.
Briefings in bioinformatics
Nov 5, 2021
Although the prognosis of lower-grade glioma (LGG) patients is better than others, outcomes are highly heterogeneous. Isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status can identify patient subsets with different prognosis. However,...
Integration and interplay of machine learning and bioinformatics approach to identify genetic interaction related to ovarian cancer chemoresistance.
Briefings in bioinformatics
Nov 5, 2021
Although chemotherapy is the first-line treatment for ovarian cancer (OCa) patients, chemoresistance (CR) decreases their progression-free survival. This paper investigates the genetic interaction (GI) related to OCa-CR. To decrease the complexity of...
Improving feature selection performance for classification of gene expression data using Harris Hawks optimizer with variable neighborhood learning.
Briefings in bioinformatics
Sep 2, 2021
Gene expression profiling has played a significant role in the identification and classification of tumor molecules. In gene expression data, only a few feature genes are closely related to tumors. It is a challenging task to select highly discrimina...
Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 glioblastoma patients.
Briefings in bioinformatics
Sep 2, 2021
Glioblastoma (GBM) is the most malignant and lethal intracranial tumor, with extremely limited treatment options. Immunotherapy has been widely studied in GBM, but none can significantly prolong the overall survival (OS) of patients without selection...
Survival Analysis
Tumor Microenvironment
Temozolomide
Antineoplastic Agents, Alkylating
Brain Neoplasms
Humans
Atlases as Topic
Male
Machine Learning
Middle Aged
Prognosis
Treatment Outcome
Gene Expression Profiling
Glioblastoma
Female
Neoplastic Stem Cells
Transcriptome
Patient Selection
Aged
Immunotherapy
Programmed Cell Death 1 Receptor
Adult
Gene Expression Regulation, Neoplastic
XGSEA: CROSS-species gene set enrichment analysis via domain adaptation.
Briefings in bioinformatics
Sep 2, 2021
MOTIVATION: Gene set enrichment analysis (GSEA) has been widely used to identify gene sets with statistically significant difference between cases and controls against a large gene set. GSEA needs both phenotype labels and expression of genes. Howeve...
GAERF: predicting lncRNA-disease associations by graph auto-encoder and random forest.
Briefings in bioinformatics
Sep 2, 2021
Predicting disease-related long non-coding RNAs (lncRNAs) is beneficial to finding of new biomarkers for prevention, diagnosis and treatment of complex human diseases. In this paper, we proposed a machine learning techniques-based classification appr...
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