AIMC Topic: Neoplasm Proteins

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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...

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...

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...

A Transfer-Learning-Based Deep Convolutional Neural Network for Predicting Leukemia-Related Phosphorylation Sites from Protein Primary Sequences.

International journal of molecular sciences
As one of the most important post-translational modifications (PTMs), phosphorylation refers to the binding of a phosphate group with amino acid residues like Ser (S), Thr (T) and Tyr (Y) thus resulting in diverse functions at the molecular level. Ab...

Personalised Medicine for Colorectal Cancer Using Mechanism-Based Machine Learning Models.

International journal of molecular sciences
Gaining insight into the mechanisms of signal transduction networks (STNs) by using critical features from patient-specific mathematical models can improve patient stratification and help to identify potential drug targets. To achieve this, these mod...

The structure-based cancer-related single amino acid variation prediction.

Scientific reports
Single amino acid variation (SAV) is an amino acid substitution of the protein sequence that can potentially influence the entire protein structure or function, as well as its binding affinity. Protein destabilization is related to diseases, includin...

Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance.

Nature communications
Resistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learnin...

Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey.

International journal of molecular sciences
Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in me...