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Cell Proliferation

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Automated AI-based grading of neuroendocrine tumors using Ki-67 proliferation index: comparative evaluation and performance analysis.

Medical & biological engineering & computing
Early detection is critical for successfully diagnosing cancer, and timely analysis of diagnostic tests is increasingly important. In the context of neuroendocrine tumors, the Ki-67 proliferation index serves as a fundamental biomarker, aiding pathol...

Eravacycline, an antibacterial drug, repurposed for pancreatic cancer therapy: insights from a molecular-based deep learning model.

Briefings in bioinformatics
BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) remains a serious threat to health, with limited effective therapeutic options, especially due to advanced stage at diagnosis and its inherent resistance to chemotherapy, making it one of the leadin...

Development and validation of AI/ML derived splice-switching oligonucleotides.

Molecular systems biology
Splice-switching oligonucleotides (SSOs) are antisense compounds that act directly on pre-mRNA to modulate alternative splicing (AS). This study demonstrates the value that artificial intelligence/machine learning (AI/ML) provides for the identificat...

Integrated multi-omics analysis and machine learning developed a prognostic model based on mitochondrial function in a large multicenter cohort for Gastric Cancer.

Journal of translational medicine
BACKGROUND: Gastric cancer (GC) is a common and aggressive type of cancer worldwide. Despite recent advancements in its treatment, the prognosis for patients with GC remains poor. Understanding the mechanisms of cell death in GC, particularly those r...

Identification of BGN positive fibroblasts as a driving factor for colorectal cancer and development of its related prognostic model combined with machine learning.

BMC cancer
BACKGROUND: Numerous studies have indicated that cancer-associated fibroblasts (CAFs) play a crucial role in the progression of colorectal cancer (CRC). However, there are still many unknowns regarding the exact role of CAF subtypes in CRC.

Machine learning identifies the role of SMAD6 in the prognosis and drug susceptibility in bladder cancer.

Journal of cancer research and clinical oncology
BACKGROUND: Bladder cancer (BCa) is among the most prevalent malignant tumors affecting the urinary system. Due to its highly recurrent nature, standard treatments such as surgery often fail to significantly improve patient prognosis. Our research ai...

Machine Learning-Based Integrated Multiomics Characterization of Colorectal Cancer Reveals Distinctive Metabolic Signatures.

Analytical chemistry
The metabolic signature identification of colorectal cancer is critical for its early diagnosis and therapeutic approaches that will significantly block cancer progression and improve patient survival. Here, we combined an untargeted metabolic analys...

Validating linalool as a potential drug for breast cancer treatment based on machine learning and molecular docking.

Drug development research
Breast cancer (BC) is a common cancer for women. This study aims to construct a prognostic risk model of BC and identify prognostic biomarkers through machine learning approaches, and clarify the mechanism by which linalool exerts tumor-suppressive f...

Discovery of a Novel and Potent LCK Inhibitor for Leukemia Treatment via Deep Learning and Molecular Docking.

Journal of chemical information and modeling
The lymphocyte-specific protein tyrosine kinase (LCK) plays a crucial role in both T-cell development and activation. Dysregulation of LCK signaling has been demonstrated to drive the oncogenesis of T-cell acute lymphoblastic leukemia (T-ALL), thus p...

From Deep Learning to the Discovery of Promising VEGFR-2 Inhibitors.

ChemMedChem
Vascular endothelial growth factor receptor 2 (VEGFR-2) stands as a prominent therapeutic target in oncology, playing a critical role in angiogenesis, tumor growth, and metastasis. FDA-approved VEGFR-2 inhibitors are associated with diverse side effe...