AIMC Topic: Transcriptome
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Role of different omics data in the diagnosis of schizophrenia disorder: A machine learning study.
Schizophrenia research
Jul 14, 2024
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
Jul 10, 2024
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
Jul 10, 2024
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...
Applications of spatial transcriptomics and artificial intelligence to develop integrated management of pancreatic cancer.
Advances in cancer research
Jul 9, 2024
Cancer is a complex disease intrinsically associated with cellular processes and gene expression. With the development of techniques such as single-cell sequencing and sequential fluorescence in situ hybridization (seqFISH), it was possible to map th...
Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients.
Cell reports methods
Jul 8, 2024
Deep-learning tools that extract prognostic factors derived from multi-omics data have recently contributed to individualized predictions of survival outcomes. However, the limited size of integrated omics-imaging-clinical datasets poses challenges. ...
Characterizing mitochondrial features in osteoarthritis through integrative multi-omics and machine learning analysis.
Frontiers in immunology
Jul 4, 2024
PURPOSE: Osteoarthritis (OA) stands as the most prevalent joint disorder. Mitochondrial dysfunction has been linked to the pathogenesis of OA. The main goal of this study is to uncover the pivotal role of mitochondria in the mechanisms driving OA dev...
A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics.
Nature cancer
Jul 3, 2024
Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an indirect two-step approach consisting of (1) DeepPT, a deep-learning framework that...
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...
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...
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.
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