AIMC Topic: Proteins

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Improving Predictions with a Multitask Convolutional Siamese Network.

Journal of chemical information and modeling Apr 5, 2022
The lead optimization phase of drug discovery refines an initial hit molecule for desired properties, especially potency. Synthesis and experimental testing of the small perturbations during this refinement can be quite costly and time-consuming. Rel...
Neural Networks, Computer Drug Discovery Proteins Ligands Entropy
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XGB-DrugPred: computational prediction of druggable proteins using eXtreme gradient boosting and optimized features set.

Scientific reports Apr 1, 2022
Accurate identification of drug-targets in human body has great significance for designing novel drugs. Compared with traditional experimental methods, prediction of drug-targets via machine learning algorithms has enhanced the attention of many rese...
Humans Machine Learning Algorithms Proteins Amino Acids Amino Acid Sequence
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Current progress and open challenges for applying deep learning across the biosciences.

Nature communications Apr 1, 2022
Deep Learning (DL) has recently enabled unprecedented advances in one of the grand challenges in computational biology: the half-century-old problem of protein structure prediction. In this paper we discuss recent advances, limitations, and future pe...
Phylogeny Deep Learning Computational Biology Systems Biology Proteins
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GEFA: Early Fusion Approach in Drug-Target Affinity Prediction.

IEEE/ACM transactions on computational biology and bioinformatics Apr 1, 2022
Predicting the interaction between a compound and a target is crucial for rapid drug repurposing. Deep learning has been successfully applied in drug-target affinity (DTA)problem. However, previous deep learning-based methods ignore modeling the dire...
Neural Networks, Computer Drug Development Proteins Drug Repositioning Amino Acid Sequence
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Predicting the Survival of Cancer Patients With Multimodal Graph Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics Apr 1, 2022
In recent years, cancer patients survival prediction holds important significance for worldwide health problems, and has gained many researchers attention in medical information communities. Cancer patients survival prediction can be seen the classif...
Humans Neural Networks, Computer Neoplasms Proteins Attention
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Identifying Protein Subcellular Locations With Embeddings-Based node2loc.

IEEE/ACM transactions on computational biology and bioinformatics Apr 1, 2022
Identifying protein subcellular locations is an important topic in protein function prediction. Interacting proteins may share similar locations. Thus, it is imperative to infer protein subcellular locations by taking protein-protein interactions (PP...
Humans Neural Networks, Computer Proteins Saccharomyces cerevisiae Protein Interaction Mapping
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Predicting Biomedical Interactions With Higher-Order Graph Convolutional Networks.

IEEE/ACM transactions on computational biology and bioinformatics Apr 1, 2022
Biomedical interaction networks have incredible potential to be useful in the prediction of biologically meaningful interactions, identification of network biomarkers of disease, and the discovery of putative drug targets. Recently, graph neural netw...
Neural Networks, Computer Case-Control Studies Proteins
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Synergy Between Embedding and Protein Functional Association Networks for Drug Label Prediction Using Harmonic Function.

IEEE/ACM transactions on computational biology and bioinformatics Apr 1, 2022
Semi-Supervised Learning (SSL)is an approach to machine learning that makes use of unlabeled data for training with a small amount of labeled data. In the context of molecular biology and pharmacology, one can take advantage of unlabeled data. For in...
Proteins Supervised Machine Learning
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A Deep Learning Model for RNA-Protein Binding Preference Prediction Based on Hierarchical LSTM and Attention Network.

IEEE/ACM transactions on computational biology and bioinformatics Apr 1, 2022
Attention mechanism has the ability to find important information in the sequence. The regions of the RNA sequence that can bind to proteins are more important than those that cannot bind to proteins. Neither conventional methods nor deep learning-ba...
Deep Learning Protein Binding Proteins RNA
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Accurate Sampling of Macromolecular Conformations Using Adaptive Deep Learning and Coarse-Grained Representation.

Journal of chemical information and modeling Mar 30, 2022
Conformational sampling of protein structures is essential for understanding biochemical functions and for predicting thermodynamic properties such as free energies. Where previous approaches rely on sequential sampling procedures, recent development...
Deep Learning Proteins Molecular Dynamics Simulation Macromolecular Substances Molecular Conformation Thermodynamics
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