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Gene Expression Regulation, Neoplastic

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Integrative machine learning approach for identification of new molecular scaffold and prediction of inhibition responses in cancer cells using multi-omics data.

Briefings in functional genomics
MDM2 (Mouse Double Minute 2), a fundamental governor of the p53 tumor suppressor pathway, has garnered significant attention as a favorable target for cancer therapy. Recent years have witnessed the development and synthesis of potent MDM2 inhibitors...

Unveiling the power of Treg.Sig: a novel machine-learning derived signature for predicting ICI response in melanoma.

Frontiers in immunology
BACKGROUND: Although immune checkpoint inhibitor (ICI) represents a significant breakthrough in cancer immunotherapy, only a few patients benefit from it. Given the critical role of Treg cells in ICI treatment resistance, we explored a Treg-associate...

Identification of gene signatures associated with lactation for predicting prognosis and treatment response in breast cancer patients through machine learning.

Scientific reports
As a newly discovered histone modification, abnormal lactation has been found to be present in and contribute to the development of various cancers. The aim of this study was to investigate the potential role between lactylation and the prognosis of ...

Machine learning-based characterization of stemness features and construction of a stemness subtype classifier for bladder cancer.

BMC cancer
BACKGROUND: Bladder cancer (BLCA) is a highly heterogeneous disease that presents challenges in predicting prognosis and treatment response. Cancer stem cells are key drivers of tumor development, progression, metastasis, and treatment resistance. Th...

Identification of M1 macrophage infiltration-related genes for immunotherapy in Her2-positive breast cancer based on bioinformatics analysis and machine learning.

Scientific reports
Over the past several decades, there has been a significant increase in the number of breast cancer patients. Among the four subtypes of breast cancer, Her2-positive breast cancer is one of the most aggressive breast cancers. In this study, we screen...

Integration of graph neural networks and transcriptomics analysis identify key pathways and gene signature for immunotherapy response and prognosis of skin melanoma.

BMC cancer
OBJECTIVE: The assessment of immunotherapy plays a pivotal role in the clinical management of skin melanoma. Graph neural networks (GNNs), alongside other deep learning algorithms and bioinformatics approaches, have demonstrated substantial promise i...

Combining multi-omics analysis with machine learning to uncover novel molecular subtypes, prognostic markers, and insights into immunotherapy for melanoma.

BMC cancer
BACKGROUND: Melanoma (SKCM) is an extremely aggressive form of cancer, characterized by high mortality rates, frequent metastasis, and limited treatment options. Our study aims to identify key target genes and enhance the diagnostic accuracy of melan...

Machine learning-based in-silico analysis identifies signatures of lysyl oxidases for prognostic and therapeutic response prediction in cancer.

Cell communication and signaling : CCS
BACKGROUND: Lysyl oxidases (LOX/LOXL1-4) are crucial for cancer progression, yet their transcriptional regulation, potential therapeutic targeting, prognostic value and involvement in immune regulation remain poorly understood. This study comprehensi...

Identification of thyroid cancer biomarkers using WGCNA and machine learning.

European journal of medical research
OBJECTIVE: The incidence of thyroid cancer (TC) is increasing in China, largely due to overdiagnosis from widespread screening and improved ultrasound technology. Identifying precise TC biomarkers is crucial for accurate diagnosis and effective treat...