AIMC Topic: Neoplasm Proteins
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Explainable artificial intelligence model for identifying COVID-19 gene biomarkers.
Computers in biology and medicine
Feb 1, 2023
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
Feb 3, 2022
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
Sep 15, 2021
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
Jun 30, 2021
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
May 11, 2021
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
Apr 22, 2021
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...
Machine learning-based investigation of the cancer protein secretory pathway.
PLoS computational biology
Apr 5, 2021
Deregulation of the protein secretory pathway (PSP) is linked to many hallmarks of cancer, such as promoting tissue invasion and modulating cell-cell signaling. The collection of secreted proteins processed by the PSP, known as the secretome, is ofte...
Systematic characterization of mutations altering protein degradation in human cancers.
Molecular cell
Feb 9, 2021
The ubiquitin-proteasome system (UPS) is the primary route for selective protein degradation in human cells. The UPS is an attractive target for novel cancer therapies, but the precise UPS genes and substrates important for cancer growth are incomple...
Predicting potential residues associated with lung cancer using deep neural network.
Mutation research
Dec 11, 2020
Lung cancer is a prominent type of cancer, which leads to high mortality rate worldwide. The major lung cancers lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) occur mainly due to somatic driver mutations in proteins and screening of su...
Identifying transcriptomic correlates of histology using deep learning.
PloS one
Nov 25, 2020
Linking phenotypes to specific gene expression profiles is an extremely important problem in biology, which has been approached mainly by correlation methods or, more fundamentally, by studying the effects of gene perturbations. However, genome-wide ...
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