AIMC Topic: Cell Proliferation

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A Novel Effective Models for Identifying BRCA Patients and Optimizing Clinical Treatments.

Anti-cancer agents in medicinal chemistry
OBJECTIVE: This study aimed to develop an effective model that identifies high-risk breast cancer (BRCA) patients and optimizes clinical treatments.

Deciphering the Role of SLFN12: A Novel Biomarker for Predicting Immunotherapy Outcomes in Glioma Patients Through Artificial Intelligence.

Journal of cellular and molecular medicine
Gliomas are the most prevalent form of primary brain tumours. Recently, targeting the PD-1 pathway with immunotherapies has shown promise as a novel glioma treatment. However, not all patients experience long-lasting benefits, underscoring the necess...

Machine Learning Reveals Aneuploidy Characteristics in Cancers: The Impact of BEX4.

Frontiers in bioscience (Landmark edition)
BACKGROUND: Aneuploidy is crucial yet under-explored in cancer pathogenesis. Specifically, the involvement of brain expressed X-linked gene 4 () in microtubule formation has been identified as a potential aneuploidy mechanism. Nevertheless, 's compre...

Identification of novel M2 macrophage-related molecule ATP6V1E1 and its biological role in hepatocellular carcinoma based on machine learning algorithms.

Journal of cellular and molecular medicine
Hepatocellular carcinoma (HCC) remains the most prevalent form of primary liver cancer, characterized by late detection and suboptimal response to current therapies. The tumour microenvironment, especially the role of M2 macrophages, is pivotal in th...

Unravelling tumour cell diversity and prognostic signatures in cutaneous melanoma through machine learning analysis.

Journal of cellular and molecular medicine
Melanoma, a highly malignant tumour, presents significant challenges due to its cellular heterogeneity, yet research on this aspect in cutaneous melanoma remains limited. In this study, we utilized single-cell data from 92,521 cells to explore the tu...

Deciphering lung adenocarcinoma prognosis and immunotherapy response through an AI-driven stemness-related gene signature.

Journal of cellular and molecular medicine
Lung adenocarcinoma (LUAD) is a leading cause of cancer-related deaths, and improving prognostic accuracy is vital for personalised treatment approaches, especially in the context of immunotherapy. In this study, we constructed an artificial intellig...

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...

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...

[Evaluation of the efficacy of antifibrotic drugs on cell cultures in Salzmann's nodular degeneration].

Vestnik oftalmologii
UNLABELLED: Excessive production of extracellular matrix is a key component in the pathogenesis of Salzmann's nodular degeneration (SND). studies of drugs that suppress excessive fibroblast activity may become crucial in developing pathogenetically ...

Artificial intelligence-assisted analysis for tumor-immune interaction within the invasive margin of colorectal cancer.

Annals of medicine
BACKGROUND: In colorectal cancer (CRC), both tumor invasion and immunological analysis at the tumor invasive margin (IM) are significantly associated with patient prognosis, but have traditionally been reported independently. We propose a new scoring...