AIMC Topic: Cell Line, Tumor
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Predicting Clinical Anticancer Drug Response of Patients by Using Domain Alignment and Prototypical Learning.
IEEE journal of biomedical and health informatics
Feb 10, 2025
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
Feb 8, 2025
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
Feb 4, 2025
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
Jan 30, 2025
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...
Concentration-dependent effects of immunomodulatory cocktails on the generation of leukemia-derived dendritic cells, DC mediated T-cell activation and on-target/off-tumor toxicity.
Frontiers in immunology
Jan 30, 2025
Acute myeloid leukemia (AML) remains a devastating diagnosis in clear need of therapeutic advances. Both targeted dendritic cells (DC) and particularly leukemia-derived dendritic cells (DC) can exert potent anti-leukemic activity. By converting AML b...
Machine learning-based lactate-related genes signature predicts clinical outcomes and unveils novel therapeutic targets in esophageal squamous cell carcinoma.
Cancer letters
Jan 27, 2025
Esophageal squamous cell carcinoma (ESCC), a predominant subtype of esophageal cancer, typically presents with poor prognosis. Lactate is a crucial metabolite in cancer and significantly impacts tumor biology. Here, we aimed to construct a lactate-re...
Sulfonic acid functionalized β-amyloid peptide aggregation inhibitors and antioxidant agents for the treatment of Alzheimer's disease: Combining machine learning, computational, in vitro and in vivo approaches.
International journal of biological macromolecules
Jan 20, 2025
Alzheimer's disease (AD) is characterized as a neurodegenerative disorder that is caused by plaque formation by accumulating β-amyloid (Aβ), leading to neurocognitive function and impaired mental development. Thus, targeting Aβ represents a promising...
Cellular Senescence in Hepatocellular Carcinoma: Immune Microenvironment Insights via Machine Learning and In Vitro Experiments.
International journal of molecular sciences
Jan 17, 2025
Hepatocellular carcinoma (HCC), a leading liver tumor globally, is influenced by diverse risk factors. Cellular senescence, marked by permanent cell cycle arrest, plays a crucial role in cancer biology, but its markers and roles in the HCC immune mic...
Exploring the anticancer activities of Sulfur and magnesium oxide through integration of deep learning and fuzzy rough set analyses based on the features of Vidarabine alkaloid.
Scientific reports
Jan 17, 2025
Drug discovery and development is a challenging and time-consuming process. Laboratory experiments conducted on Vidarabine showed IC 6.97 µg∕mL, 25.78 µg∕mL, and ˃ 100 µg∕mL against non-small Lung cancer (A-549), Human Melanoma (A-375), and Human epi...
Biologically relevant integration of transcriptomics profiles from cancer cell lines, patient-derived xenografts, and clinical tumors using deep learning.
Science advances
Jan 17, 2025
Cell lines and patient-derived xenografts are essential to cancer research; however, the results derived from such models often lack clinical translatability, as they do not fully recapitulate the complex cancer biology. Identifying preclinical model...
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