AIMC Topic: Transcriptome
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Unmasking Neuroendocrine Prostate Cancer with a Machine Learning-Driven Seven-Gene Stemness Signature That Predicts Progression.
International journal of molecular sciences
Oct 22, 2024
Prostate cancer (PCa) poses a significant global health challenge, particularly due to its progression into aggressive forms like neuroendocrine prostate cancer (NEPC). This study developed and validated a stemness-associated gene signature using adv...
Identification and validation of efferocytosis-related biomarkers for the diagnosis of metabolic dysfunction-associated steatohepatitis based on bioinformatics analysis and machine learning.
Frontiers in immunology
Oct 21, 2024
BACKGROUND: Metabolic dysfunction-associated steatohepatitis (MASH) is a highly prevalent liver disease globally, with a significant risk of progressing to cirrhosis and even liver cancer. Efferocytosis, a process implicated in a broad spectrum of ch...
Autism Spectrum Disorder and Atypical Brain Connectivity: Novel Insights from Brain Connectivity-Associated Genes by Combining Random Forest and Support Vector Machine Algorithm.
Omics : a journal of integrative biology
Oct 17, 2024
It is estimated that approximately one in every 100 children is diagnosed with autism spectrum disorder (ASD) around the globe. Currently, there are no curative pharmacological treatments for ASD. Discoveries on key molecular mechanisms of ASD are es...
Machine learning-derived peripheral blood transcriptomic biomarkers for early lung cancer diagnosis: Unveiling tumor-immune interaction mechanisms.
BioFactors (Oxford, England)
Oct 16, 2024
Lung cancer continues to be the leading cause of cancer-related mortality worldwide. Early detection and a comprehensive understanding of tumor-immune interactions are crucial for improving patient outcomes. This study aimed to develop a novel biomar...
Combining bioinformatics and machine learning to identify diagnostic biomarkers of TB associated with immune cell infiltration.
Tuberculosis (Edinburgh, Scotland)
Oct 11, 2024
OBJECTIVE: The asymptomatic nature of tuberculosis (TB) during its latent phase, combined with limitations in current diagnostic methods, makes accurate diagnosis challenging. This study aims to identify TB diagnostic biomarkers by integrating gene e...
Bulk integrated single-cell-spatial transcriptomics reveals the impact of preoperative chemotherapy on cancer-associated fibroblasts and tumor cells in colorectal cancer, and construction of related predictive models using machine learning.
Biochimica et biophysica acta. Molecular basis of disease
Oct 5, 2024
BACKGROUND: Preoperative chemotherapy (PC) is an important component of Colorectal cancer (CRC) treatment, but its effects on the biological functions of fibroblasts and epithelial cells in CRC are unclear.
Peripheral Blood Mononuclear Cell Biomarkers for Major Depressive Disorder: A Transcriptomic Approach.
Depression and anxiety
Oct 3, 2024
Major depressive disorder (MDD) is a complex condition characterized by persistent depressed mood, loss of interest or pleasure, loss of energy or fatigue, and, in severe case, recurrent thoughts of death. Despite its prevalence, reliable diagnostic...
Machine learning based analysis of single-cell data reveals evidence of subject-specific single-cell gene expression profiles in acute myeloid leukaemia patients and healthy controls.
Biochimica et biophysica acta. Gene regulatory mechanisms
Oct 2, 2024
Acute Myeloid Leukaemia (AML) is characterized by uncontrolled growth of immature myeloid cells, disrupting normal blood production. Treatment typically involves chemotherapy, targeted therapy, and stem cell transplantation but many patients develop ...
Enhancing Gene Expression Predictions Using Deep Learning and Functional Annotations.
Genetic epidemiology
Sep 30, 2024
Transcriptome-wide association studies (TWAS) aim to uncover genotype-phenotype relationships through a two-stage procedure: predicting gene expression from genotypes using an expression quantitative trait locus (eQTL) data set, then testing the pred...
Prognostic prediction of gastric cancer based on H&E findings and machine learning pathomics.
Molecular and cellular probes
Sep 30, 2024
AIM: In this research, we aimed to develop a model for the accurate prediction of gastric cancer based on H&E findings combined with machine learning pathomics.
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