AIMC Topic: Protein Interaction Maps
Clear Filters Showing 61 to 70 of 375 articles
A synthetic protein-level neural network in mammalian cells.
Science (New York, N.Y.)
Dec 12, 2024
Artificial neural networks provide a powerful paradigm for nonbiological information processing. To understand whether similar principles could enable computation within living cells, we combined de novo-designed protein heterodimers and engineered v...
Integration of biological data via NMF for identification of human disease-associated gene modules through multi-label classification.
PloS one
Dec 12, 2024
Proteins associated with multiple diseases often interact, forming disease modules that are critical for understanding disease mechanisms. This study integrates protein-protein interactions (PPIs) and Gene Ontology data using non-negative matrix fact...
Mapping the functional network of human cancer through machine learning and pan-cancer proteogenomics.
Nature cancer
Dec 11, 2024
Large-scale omics profiling has uncovered a vast array of somatic mutations and cancer-associated proteins, posing substantial challenges for their functional interpretation. Here we present a network-based approach centered on FunMap, a pan-cancer f...
An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
Protein-protein interactions (PPIs) are essential to understanding cellular mechanisms, signaling networks, disease processes, and drug development, as they represent the physical contacts and functional associations between proteins. Recent advances...
RGCNPPIS: A Residual Graph Convolutional Network for Protein-Protein Interaction Site Prediction.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
Accurate identification of protein-protein interaction (PPI) sites is crucial for understanding the mechanisms of biological processes, developing PPI networks, and detecting protein functions. Currently, most computational methods primarily concentr...
MDMNI-DGD: A novel graph neural network approach for druggable gene discovery based on the integration of multi-omics data and the multi-view network.
Computers in biology and medicine
Dec 6, 2024
Accurately predicting druggable genes is of paramount importance for enhancing the efficacy of targeted therapies, reducing drug-related toxicities and improving patients' survival rates. Nevertheless, accurately predicting candidate cancer-druggable...
Machine learning approaches for predicting craniofacial anomalies with graph neural networks.
Computational biology and chemistry
Dec 2, 2024
This study explores the use of machine learning algorithms, including traditional approaches and graph neural networks (GNNs), to predict certain diseases by analyzing protein-protein interactions. Protein-protein interactions (PPIs) are complex, mul...
Identification of immune-related mitochondrial metabolic disorder genes in septic shock using bioinformatics and machine learning.
Hereditas
Nov 28, 2024
PURPOSE: Mitochondria are involved in septic shock and inflammatory response syndrome, which severely affects the life security of patients. It is necessary to recognize and explore the immune-mitochondrial genes in septic shock.
The diagnostic value investigation of programmed cell death genes in heart failure.
BMC cardiovascular disorders
Nov 22, 2024
BACKGROUND: We aimed to identify the potential diagnostic markers and associated molecular mechanisms based on programmed cell death (PCD)-related genes in patients with heart failure (HF).
Humans
Machine Learning
Transcriptome
Genetic Markers
Case-Control Studies
Databases, Genetic
Heart Failure
Reproducibility of Results
Apoptosis
Protein Interaction Maps
Plasminogen Activator Inhibitor 1
Gene Expression Profiling
Predictive Value of Tests
Gene Regulatory Networks
Gene Expression Regulation
Nomograms
Apoptosis Regulatory Proteins
Leveraging Bioinformatics and Machine Learning for Identifying Prognostic Biomarkers and Predicting Clinical Outcomes in Lung Adenocarcinoma.
Genes
Nov 21, 2024
There exist significant challenges for lung adenocarcinoma (LUAD) due to its poor prognosis and limited treatment options, particularly in the advanced stages. It is crucial to identify genetic biomarkers for improving outcome predictions and guidin...
Stay Ahead of Medical AI
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 30978
- Neural Networks, Computer 29696
- Algorithms 26813
- Deep Learning 26324
- Artificial Intelligence 24267
- Robotics 13307
- Image Processing, Computer-Assisted 9512
- Retrospective Studies 9097
- Magnetic Resonance Imaging 6173
- Reproducibility of Results 5589
- Tomography, X-Ray Computed 4765
- Support Vector Machine 4678
- Computational Biology 4229
- Brain 4045
- Computer Simulation 3877
Recent Journals
- Scientific reports 5865
- Sensors (Basel, Switzerland) 4794
- PloS one 4011
- Neural networks : the official journal of the International Neural Network Society 2928
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1839
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1278
- IEEE journal of biomedical and health informatics 1129
- Journal of chemical information and modeling 994
- Nature communications 938
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 861
- Briefings in bioinformatics 850
- IEEE transactions on neural networks and learning systems 823