AIMC Topic: Gene Expression Profiling

Clear Filters Showing 461 to 470 of 1308 articles

A variational autoencoder trained with priors from canonical pathways increases the interpretability of transcriptome data.

PLoS computational biology Jul 3, 2024
Interpreting transcriptome data is an important yet challenging aspect of bioinformatic analysis. While gene set enrichment analysis is a standard tool for interpreting regulatory changes, we utilize deep learning techniques, specifically autoencoder...
Gene Expression Profiling Transcriptome Deep Learning Computational Biology Humans Algorithms
View on PubMed DOI

Comprehensive Bioinformatics Analysis and Machine Learning of TTK as a Transhepatic Arterial Chemoembolization Resistance Target in Hepatocellular Carcinoma.

Molecular biotechnology Jul 2, 2024
Transhepatic arterial chemoembolization (TACE) is the standard treatment for intermediate-stage hepatocellular carcinoma (HCC). However, a significant proportion of patients are non-responders or poor responders to TACE. Therefore, our aim is to iden...
Gene Expression Profiling Liver Neoplasms Carcinoma, Hepatocellular Protein Interaction Maps Machine Learning Prognosis Humans Biomarkers, Tumor Drug Resistance, Neoplasm Computational Biology Chemoembolization, Therapeutic Kaplan-Meier Estimate Gene Expression Regulation, Neoplastic Cell Cycle Proteins Databases, Genetic
View on PubMed DOI

Exploration and verification a 13-gene diagnostic framework for ulcerative colitis across multiple platforms via machine learning algorithms.

Scientific reports Jul 1, 2024
Ulcerative colitis (UC) is a chronic inflammatory bowel disease with intricate pathogenesis and varied presentation. Accurate diagnostic tools are imperative to detect and manage UC. This study sought to construct a robust diagnostic model using gene...
Gene Expression Profiling Transcriptome Case-Control Studies Interleukin-1 Receptor-Associated Kinases Colitis, Ulcerative Lipocalin-2 Biomarkers Humans Female Adult Machine Learning Algorithms Male
View on PubMed DOI

Integrating machine learning algorithms and multiple immunohistochemistry validation to unveil novel diagnostic markers based on costimulatory molecules for predicting immune microenvironment status in triple-negative breast cancer.

Frontiers in immunology Jun 28, 2024
INTRODUCTION: Costimulatory molecules are putative novel targets or potential additions to current available immunotherapy, but their expression patterns and clinical value in triple-negative breast cancer (TNBC) are to be clarified.
Gene Expression Profiling Transcriptome Humans Immunotherapy Immunohistochemistry Triple Negative Breast Neoplasms Female Machine Learning Lymphocytes, Tumor-Infiltrating Tumor Microenvironment Algorithms Biomarkers, Tumor
View on PubMed DOI

Four Genes with Seven Targeted Drugs might be Treatment for Diabetic Nephropathy and Acute Coronary Syndrome.

The Tohoku journal of experimental medicine Jun 27, 2024
Diabetes nephropathy (DN) is a main risk factor for acute coronary syndrome (ACS), but the molecular mechanism is unknown. This research used bioinformatics approaches to uncover potential molecular mechanisms and drugs for DN and ACS. GSE142153 and ...
Databases, Genetic Acute Coronary Syndrome Gene Expression Regulation Molecular Targeted Therapy Gene Ontology Protein Interaction Maps Gene Regulatory Networks Diabetic Nephropathies Computational Biology Humans Gene Expression Profiling
View on PubMed DOI

ST-CellSeg: Cell segmentation for imaging-based spatial transcriptomics using multi-scale manifold learning.

PLoS computational biology Jun 27, 2024
Spatial transcriptomics has gained popularity over the past decade due to its ability to evaluate transcriptome data while preserving spatial information. Cell segmentation is a crucial step in spatial transcriptomic analysis, as it enables the avoid...
Computational Biology Humans Machine Learning Transcriptome Image Processing, Computer-Assisted Algorithms Gene Expression Profiling
View on PubMed DOI

Identification of immune-related genes and small-molecule drugs in hypertension-induced left ventricular hypertrophy based on machine learning algorithms and molecular docking.

Frontiers in immunology Jun 27, 2024
BACKGROUND: Left ventricular hypertrophy (LVH) is a common consequence of hypertension and can lead to heart failure. The immune response plays an important role in hypertensive LVH; however, there is no comprehensive method to investigate the mechan...
Gene Expression Profiling Machine Learning Disease Models, Animal Mice Gene Regulatory Networks Mice, Inbred C57BL Animals Hypertension Hypertrophy, Left Ventricular Male Molecular Docking Simulation
View on PubMed DOI

Identification and validation of cuproptosis-related genes in acetaminophen-induced liver injury using bioinformatics analysis and machine learning.

Frontiers in immunology Jun 27, 2024
BACKGROUND: Acetaminophen (APAP) is commonly used as an antipyretic analgesic. However, acetaminophen overdose may contribute to liver injury and even liver failure. Acetaminophen-induced liver injury (AILI) is closely related to mitochondrial oxidat...
Copper Acetaminophen Chemical and Drug Induced Liver Injury Machine Learning Computational Biology Animals Disease Models, Animal Male Mice, Inbred C57BL Gene Expression Profiling Liver Transcriptome Protein Interaction Maps Mice
View on PubMed DOI

Integrated machine learning identifies a cellular senescence-related prognostic model to improve outcomes in uterine corpus endometrial carcinoma.

Frontiers in immunology Jun 27, 2024
BACKGROUND: Uterine Corpus Endometrial Carcinoma (UCEC) stands as one of the prevalent malignancies impacting women globally. Given its heterogeneous nature, personalized therapeutic approaches are increasingly significant for optimizing patient outc...
Prognosis Female Humans Machine Learning Endometrial Neoplasms Gene Expression Profiling Tumor Microenvironment Gene Expression Regulation, Neoplastic Transcriptome Biomarkers, Tumor Cellular Senescence Middle Aged
View on PubMed DOI

Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery.

Nature communications Jun 25, 2024
Artificial intelligence transforms drug discovery, with phenotype-based approaches emerging as a promising alternative to target-based methods, overcoming limitations like lack of well-defined targets. While chemical-induced transcriptional profiles ...
Gene Expression Profiling Transcriptome Deep Learning Pancreatic Neoplasms Phenotype Drug Discovery Antineoplastic Agents Drug Repositioning Humans Artificial Intelligence
View on PubMed DOI
Popular Topics
Recent Journals