AIMC Topic: Proteins

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Protein molecular defect detection method based on a neural network algorithm.

Cellular and molecular biology (Noisy-le-Grand, France)
Proteins, as the largest macromolecules in the body, are among the most important components of the body and play very vital and important roles. These substances are made up of a series of amino acid chains that, depending on the type of protein, th...

A Machine Learning Protocol for Predicting Protein Infrared Spectra.

Journal of the American Chemical Society
Infrared (IR) absorption provides important chemical fingerprints of biomolecules. Protein secondary structure determination from IR spectra is tedious since its theoretical interpretation requires repeated expensive quantum-mechanical calculations i...

Automatic Gene Function Prediction in the 2020's.

Genes
The current rate at which new DNA and protein sequences are being generated is too fast to experimentally discover the functions of those sequences, emphasizing the need for accurate Automatic Function Prediction (AFP) methods. AFP has been an active...

Self-evoluting framework of deep convolutional neural network for multilocus protein subcellular localization.

Medical & biological engineering & computing
In the present paper, deep convolutional neural network (DCNN) is applied to multilocus protein subcellular localization as it is more suitable for multi-class classification. There are two main problems with this application. First, the appropriate ...

Using Deep Learning to Extrapolate Protein Expression Measurements.

Proteomics
Mass spectrometry (MS)-based quantitative proteomics experiments typically assay a subset of up to 60% of the ≈20 000 human protein coding genes. Computational methods for imputing the missing values using RNA expression data usually allow only for i...

Guiding Conventional Protein-Ligand Docking Software with Convolutional Neural Networks.

Journal of chemical information and modeling
The high-performance computational techniques have brought significant benefits for drug discovery efforts in recent decades. One of the most challenging problems in drug discovery is the protein-ligand binding pose prediction. To predict the most st...

Application of deep learning in genomics.

Science China. Life sciences
In recent years, deep learning has been widely used in diverse fields of research, such as speech recognition, image classification, autonomous driving and natural language processing. Deep learning has showcased dramatically improved performance in ...

DeepACTION: A deep learning-based method for predicting novel drug-target interactions.

Analytical biochemistry
Drug-target interactions (DTIs) play a key role in drug development and discovery processes. Wet lab prediction of DTIs is time-consuming, expensive, and tedious. Fortunately, computational approaches can identify new interactions (drug-target pairs)...

SPOTONE: Hot Spots on Protein Complexes with Extremely Randomized Trees via Sequence-Only Features.

International journal of molecular sciences
Protein Hot-Spots (HS) are experimentally determined amino acids, key to small ligand binding and tend to be structural landmarks on protein-protein interactions. As such, they were extensively approached by structure-based Machine Learning (ML) pred...

DeepAdd: Protein function prediction from k-mer embedding and additional features.

Computational biology and chemistry
With the application of new high throughput sequencing technology, a large number of protein sequences is becoming available. Determination of the functional characteristics of these proteins by experiments is an expensive endeavor that requires a lo...