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Neoplasm Proteins

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Association between serum endocan levels and organ failure in hospitalized patients with cirrhosis.

PloS one
BACKGROUND & AIMS: Acute-on-chronic liver failure is a syndrome characterized by organ failure and high short-term mortality. The lack of reliable biomarkers for the early detection of acute-on-chronic liver failure is a significant challenge. Endoth...

Muscle magnetic resonance characterization of STIM1 tubular aggregate myopathy using unsupervised learning.

PloS one
PURPOSE: Congenital myopathies are a heterogeneous group of diseases affecting the skeletal muscles and characterized by high clinical, genetic, and histological variability. Magnetic Resonance (MR) is a valuable tool for the assessment of involved m...

Identification of gene profiles related to the development of oral cancer using a deep learning technique.

BMC medical genomics
BACKGROUND: Oral cancer (OC) is a debilitating disease that can affect the quality of life of these patients adversely. Oral premalignant lesion patients have a high risk of developing OC. Therefore, identifying robust survival subgroups among them m...

Explainable artificial intelligence model for identifying COVID-19 gene biomarkers.

Computers in biology and medicine
AIM: COVID-19 has revealed the need for fast and reliable methods to assist clinicians in diagnosing the disease. This article presents a model that applies explainable artificial intelligence (XAI) methods based on machine learning techniques on COV...

Machine Learning Techniques Applied to the Study of Drug Transporters.

Molecules (Basel, Switzerland)
With the advancement of computer technology, machine learning-based artificial intelligence technology has been increasingly integrated and applied in the fields of medicine, biology, and pharmacy, thereby facilitating their development. Transporters...

Machine learning approach to predict blood-secretory proteins and potential biomarkers for liver cancer using omics data.

Journal of proteomics
Identifying non-invasive blood-based biomarkers is crucial for early detection and monitoring of liver cancer (LC), thereby improving patient outcomes. This study leveraged computational approaches to predict potential blood-based biomarkers for LC. ...

Unraveling druggable cancer-driving proteins and targeted drugs using artificial intelligence and multi-omics analyses.

Scientific reports
The druggable proteome refers to proteins that can bind to small molecules with appropriate chemical affinity, inducing a favorable clinical response. Predicting druggable proteins through screening and in silico modeling is imperative for drug desig...

A graph neural network approach for hierarchical mapping of breast cancer protein communities.

BMC bioinformatics
BACKGROUND: Comprehensively mapping the hierarchical structure of breast cancer protein communities and identifying potential biomarkers from them is a promising way for breast cancer research. Existing approaches are subjective and fail to take info...

TPD52 as a Therapeutic Target Identified by Machine Learning Shapes the Immune Microenvironment in Breast Cancer.

Journal of cellular and molecular medicine
Breast cancer (BRCA) is one of the most common malignancies and a leading cause of cancer-related mortality among women globally. Despite advances in diagnosis and treatment, the heterogeneity of BRCA presents significant challenges for effective man...

Molecular structure and mechanism of protein MSMB, TPPP3, SPI1: Construction of novel 4 pancreatic cancer-related protein signatures model based on machine learning.

International journal of biological macromolecules
The high mortality rate of pancreatic cancer is closely related to its inconspicuous early symptoms and difficult diagnosis. In recent years, with the rapid development of proteomics and bioinformatics, the use of machine learning technology to analy...