AIMC Topic: Proteins

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GuiltyTargets: Prioritization of Novel Therapeutic Targets With Network Representation Learning.

IEEE/ACM transactions on computational biology and bioinformatics
The majority of clinical trials fail due to low efficacy of investigated drugs, often resulting from a poor choice of target protein. Existing computational approaches aim to support target selection either via genetic evidence or by putting potentia...

Boltzmann Machine Learning and Regularization Methods for Inferring Evolutionary Fields and Couplings From a Multiple Sequence Alignment.

IEEE/ACM transactions on computational biology and bioinformatics
The inverse Potts problem to infer a Boltzmann distribution for homologous protein sequences from their single-site and pairwise amino acid frequencies recently attracts a great deal of attention in the studies of protein structure and evolution. We ...

Importance of interface and surface areas in protein-protein binding affinity prediction: A machine learning analysis based on linear regression and artificial neural network.

Biophysical chemistry
Protein-protein interaction plays an important role in all biological systems. The binding affinity between two protein binding partners reflects the strength of their association, which is crucial to the elucidation of the biological functions of th...

Yuel: Improving the Generalizability of Structure-Free Compound-Protein Interaction Prediction.

Journal of chemical information and modeling
Predicting binding affinities between small molecules and the protein target is at the core of computational drug screening and drug target identification. Deep learning-based approaches have recently been adapted to predict binding affinities and th...

MDL-CPI: Multi-view deep learning model for compound-protein interaction prediction.

Methods (San Diego, Calif.)
Elucidating the mechanisms of Compound-Protein Interactions (CPIs) plays an essential role in drug discovery and development. Many computational efforts have been done to accelerate the development of this field. However, the current predictive perfo...

Cryo-EM and artificial intelligence visualize endogenous protein community members.

Structure (London, England : 1993)
Cellular function is underlined by megadalton assemblies organizing in proximity, forming communities. Metabolons are protein communities involving metabolic pathways such as protein, fatty acid, and thioesters of coenzyme-A synthesis. Metabolons are...

A two-step ensemble learning for predicting protein hot spot residues from whole protein sequence.

Amino acids
Protein hot spot residues are functional sites in protein-protein interactions. Biological experimental methods are traditionally used to identify hot spot residues, which is laborious and time-consuming. Thus a variety of computational methods were ...

Testing Precision Limits of Neural Network-Based Quality Control Metrics in High-Throughput Digital Microscopy.

Pharmaceutical research
OBJECTIVE: Digital microscopy is used to monitor particulates such as protein aggregates within biopharmaceutical products. The images that result encode a wealth of information that is underutilized in pharmaceutical process monitoring. For example,...

A deep learning approach to predict inter-omics interactions in multi-layer networks.

BMC bioinformatics
BACKGROUND: Despite enormous achievements in the production of high-throughput datasets, constructing comprehensive maps of interactions remains a major challenge. Lack of sufficient experimental evidence on interactions is more significant for heter...

Multiple Protein Subcellular Locations Prediction Based on Deep Convolutional Neural Networks with Self-Attention Mechanism.

Interdisciplinary sciences, computational life sciences
As an important research field in bioinformatics, protein subcellular location prediction is critical to reveal the protein functions and provide insightful information for disease diagnosis and drug development. Predicting protein subcellular locati...