AIMC Topic: Transcriptome

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SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning.

Nature communications
Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for transcriptome-wide association studies (TWAS). To leverage expression imputa...

Machine learning-driven diagnosis of multiple sclerosis from whole blood transcriptomics.

Brain, behavior, and immunity
Multiple sclerosis (MS) is a neurological disorder characterized by immune dysregulation. It begins with a first clinical manifestation, a clinically isolated syndrome (CIS), which evolves to definite MS in case of further clinical and/or neuroradiol...

Interactive molecular causal networks of hypertension using a fast machine learning algorithm MRdualPC.

BMC medical research methodology
BACKGROUND: Understanding the complex interactions between genes and their causal effects on diseases is crucial for developing targeted treatments and gaining insight into biological mechanisms. However, the analysis of molecular networks, especiall...

Identification of potential biomarkers for atrial fibrillation and stable coronary artery disease based on WGCNA and machine algorithms.

BMC cardiovascular disorders
BACKGROUND: Patients with atrial fibrillation (AF) often have coronary artery disease (CAD), but the biological link between them remains unclear. This study aims to explore the common pathogenesis of AF and CAD and identify common biomarkers.

Machine learning-based biomarker screening for acute myeloid leukemia prognosis and therapy from diverse cell-death patterns.

Scientific reports
Acute myeloid leukemia (AML) exhibits pronounced heterogeneity and chemotherapy resistance. Aberrant programmed cell death (PCD) implicated in AML pathogenesis suggests PCD-related signatures could serve as biomarkers to predict clinical outcomes and...

Identification and immune landscape of sarcopenia-related molecular clusters in inflammatory bowel disease by machine learning and integrated bioinformatics.

Scientific reports
Sarcopenia, a prevalent comorbidity of inflammatory bowel disease (IBD), is characterized by diminished skeletal muscle mass and strength. Nevertheless, the underlying interconnected mechanisms remain elusive. This study identified distinct expressio...

A stemness-based signature with inspiring indications in discriminating the prognosis, immune response, and somatic mutation of endometrial cancer patients revealed by machine learning.

Aging
Endometrial cancer (EC) is a fatal gynecologic tumor. Bioinformatic tools are increasingly developed to screen out molecular targets related to EC. Our study aimed to identify stemness-related prognostic biomarkers for new therapeutic strategies in E...

Utilizing machine learning and bioinformatics analysis to identify drought-responsive genes affecting yield in foxtail millet.

International journal of biological macromolecules
Drought stress is a major constraint on crop development, potentially causing huge yield losses and threatening global food security. Improving Crop's stress tolerance is usually associated with a yield penalty. One way to balance yield and stress to...

Identification of diagnostic genes and drug prediction in metabolic syndrome-associated rheumatoid arthritis by integrated bioinformatics analysis, machine learning, and molecular docking.

Frontiers in immunology
BACKGROUND: Interactions between the immune and metabolic systems may play a crucial role in the pathogenesis of metabolic syndrome-associated rheumatoid arthritis (MetS-RA). The purpose of this study was to discover candidate biomarkers for the diag...