AIMC Topic: Transcriptome

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A personalized mRNA signature for predicting hypertrophic cardiomyopathy applying machine learning methods.

Scientific reports
Hypertrophic cardiomyopathy (HCM) may lead to cardiac dysfunction and sudden death. This study was designed to develop a HCM signature applying bioinformatics and machine learning methods. Data of HCM and normal tissues were obtained from public data...

Construction of a multi-tissue compound-target interaction network of Qingfei Paidu decoction in COVID-19 treatment based on deep learning and transcriptomic analysis.

Journal of bioinformatics and computational biology
The Qingfei Paidu decoction (QFPDD) is a widely acclaimed therapeutic formula employed nationwide for the clinical management of coronavirus disease 2019 (COVID-19). QFPDD exerts a synergistic therapeutic effect, characterized by its multi-component,...

Machine learning for catalysing the integration of noncoding RNA in research and clinical practice.

EBioMedicine
The human transcriptome predominantly consists of noncoding RNAs (ncRNAs), transcripts that do not encode proteins. The noncoding transcriptome governs a multitude of pathophysiological processes, offering a rich source of next-generation biomarkers....

Novel AT2 Cell Subpopulations and Diagnostic Biomarkers in IPF: Integrating Machine Learning with Single-Cell Analysis.

International journal of molecular sciences
Idiopathic pulmonary fibrosis (IPF) is a long-term condition with an unidentified cause, and currently there are no specific treatment options available. Alveolar epithelial type II cells (AT2) constitute a heterogeneous population crucial for secret...

Developing a machine learning-based prognosis and immunotherapeutic response signature in colorectal cancer: insights from ferroptosis, fatty acid dynamics, and the tumor microenvironment.

Frontiers in immunology
INSTRUCTION: Colorectal cancer (CRC) poses a challenge to public health and is characterized by a high incidence rate. This study explored the relationship between ferroptosis and fatty acid metabolism in the tumor microenvironment (TME) of patients ...

Role of different omics data in the diagnosis of schizophrenia disorder: A machine learning study.

Schizophrenia research
Schizophrenia is a serious mental disorder that affects millions of people worldwide. This disorder slowly disintegrates thinking ability and changes behaviours of patients. These patients will show some psychotic symptoms such as hallucinations, del...

Integrated Transcriptomic Landscape and Deep Learning Based Survival Prediction in Uterine Sarcomas.

Cancer research and treatment
PURPOSE: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs).

Identification of core genes in intervertebral disc degeneration using bioinformatics and machine learning algorithms.

Frontiers in immunology
BACKGROUND: Intervertebral Disc Degeneration (IDD) is a major cause of lower back pain and a significant global health issue. However, the specific mechanisms of IDD remain unclear. This study aims to identify key genes and pathways associated with I...