AIMC Topic: Gene Expression Profiling
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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
Jul 15, 2024
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 ...
Self-supervised learning for characterising histomorphological diversity and spatial RNA expression prediction across 23 human tissue types.
Nature communications
Jul 13, 2024
As vast histological archives are digitised, there is a pressing need to be able to associate specific tissue substructures and incident pathology to disease outcomes without arduous annotation. Here, we learn self-supervised representations using a ...
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...
Lung Adenocarcinoma Systems Biomarker and Drug Candidates Identified by Machine Learning, Gene Expression Data, and Integrative Bioinformatics Pipeline.
Omics : a journal of integrative biology
Jul 9, 2024
Lung adenocarcinoma (LUAD) is a significant planetary health challenge with its high morbidity and mortality rate, not to mention the marked interindividual variability in treatment outcomes and side effects. There is an urgent need for robust system...
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...
Improving prediction of blood cancer using leukemia microarray gene data and Chi2 features with weighted convolutional neural network.
Scientific reports
Jul 7, 2024
Blood cancer has emerged as a growing concern over the past decade, necessitating early diagnosis for timely and effective treatment. The present diagnostic method, which involves a battery of tests and medical experts, is costly and time-consuming. ...
Identification and validation of potential common biomarkers for papillary thyroid carcinoma and Hashimoto's thyroiditis through bioinformatics analysis and machine learning.
Scientific reports
Jul 6, 2024
There is a growing body of evidence suggesting that Hashimoto's thyroiditis (HT) may contribute to an increased risk of papillary thyroid carcinoma (PTC). However, the exact relationship between HT and PTC is still not fully understood. The objective...
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...
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