AIMC Topic: Cell Line, Tumor

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Multi-omics and single-cell analysis reveals machine learning-based pyrimidine metabolism-related signature in the prognosis of patients with lung adenocarcinoma.

International journal of medical sciences
Pyrimidine metabolism is a hallmark of tumor metabolic reprogramming, while its significance in the prognostic and therapeutic implications of patients with lung adenocarcinoma (LUAD) still remains unclear. In this study, an integrated framework of...

Monitoring Immunohistochemical Staining Variations Using Artificial Intelligence on Standardized Controls.

Laboratory investigation; a journal of technical methods and pathology
Quality control of immunohistochemistry (IHC) slides is crucial to ascertain accurate patient management. Visual assessment is the most commonly used method to assess the quality of IHC slides from patient samples in daily pathology routines. Control...

A hybrid machine learning framework for functional annotation of mitochondrial glutathione transport and metabolism proteins in cancers.

BMC bioinformatics
BACKGROUND: Alterations of metabolism, including changes in mitochondrial metabolism as well as glutathione (GSH) metabolism are a well appreciated hallmark of many cancers. Mitochondrial GSH (mGSH) transport is a poorly characterized aspect of GSH m...

Real-Time, AI-Guided Photodynamic Laparoscopy Enhances Detection in a Rabbit Model of Peritoneal Cancer Metastasis.

Cancer science
Accurate diagnosis is essential for effective cancer treatment, particularly in peritoneal surface malignancies, where failure to detect metastatic lesions can mislead the treatment plan. This study assessed the diagnostic accuracy of staging laparos...

Machine Learning Identification and Classification of Mitosis and Migration of Cancer Cells in a Lab-on-CMOS Capacitance Sensing Platform.

IEEE journal of biomedical and health informatics
Cell culture assays play a vital role in various fields of biology. Conventional assay techniques like immunohistochemistry, immunofluorescence, and flow cytometry offer valuable insights into cell phenotype and behavior. However, each of these techn...

Predicting Clinical Anticancer Drug Response of Patients by Using Domain Alignment and Prototypical Learning.

IEEE journal of biomedical and health informatics
Anticancer drug response prediction is crucial in developing personalized treatment plans for cancer patients. However, High-quality patient anticancer drug response data are scarce and cell line data and patient data have different distributions, mo...

Unravelling single-cell DNA replication timing dynamics using machine learning reveals heterogeneity in cancer progression.

Nature communications
Genomic heterogeneity has largely been overlooked in single-cell replication timing (scRT) studies. Here, we develop MnM, an efficient machine learning-based tool that allows disentangling scRT profiles from heterogenous samples. We use single-cell c...

Machine learning-based integration reveals immunological heterogeneity and the clinical potential of T cell receptor (TCR) gene pattern in hepatocellular carcinoma.

Apoptosis : an international journal on programmed cell death
The T Cell Receptor (TCR) significantly contributes to tumor immunity, whereas the intricate interplay with the Hepatocellular Carcinoma (HCC) microenvironment and clinical significance remains largely unexplored. Here, we aimed to examine the functi...

ASGCL: Adaptive Sparse Mapping-based graph contrastive learning network for cancer drug response prediction.

PLoS computational biology
Personalized cancer drug treatment is emerging as a frontier issue in modern medical research. Considering the genomic differences among cancer patients, determining the most effective drug treatment plan is a complex and crucial task. In response to...