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Amino Acid Sequence

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Transformer Neural Networks for Protein Family and Interaction Prediction Tasks.

Journal of computational biology : a journal of computational molecular cell biology
The scientific community is rapidly generating protein sequence information, but only a fraction of these proteins can be experimentally characterized. While promising deep learning approaches for protein prediction tasks have emerged, they have comp...

IDRBP-PPCT: Identifying Nucleic Acid-Binding Proteins Based on Position-Specific Score Matrix and Position-Specific Frequency Matrix Cross Transformation.

IEEE/ACM transactions on computational biology and bioinformatics
DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs) are two important nucleic acid-binding proteins (NABPs), which play important roles in biological processes such as replication, translation and transcription of genetic material. Some prote...

ppdx: Automated modeling of protein-protein interaction descriptors for use with machine learning.

Journal of computational chemistry
This paper describes ppdx, a python workflow tool that combines protein sequence alignment, homology modeling, and structural refinement, to compute a broad array of descriptors for characterizing protein-protein interactions. The descriptors can be ...

DCSE:Double-Channel-Siamese-Ensemble model for protein protein interaction prediction.

BMC genomics
BACKGROUND: Protein-protein interaction (PPI) is very important for many biochemical processes. Therefore, accurate prediction of PPI can help us better understand the role of proteins in biochemical processes. Although there are many methods to pred...

DeepRHD: An efficient hybrid feature extraction technique for protein remote homology detection using deep learning strategies.

Computational biology and chemistry
In computational biology, the Protein Remote homology Detection technique (PRHD) has got undeniable significance. It is mostly important for structure and function identification of a protein sequence. The previous years have seen a challenge that la...

bHLHDB: A next generation database of basic helix loop helix transcription factors based on deep learning model.

Journal of bioinformatics and computational biology
The basic helix loop helix (bHLH) superfamily is a large and diverse protein family that plays a role in various vital functions in nearly all animals and plants. The bHLH proteins form one of the largest families of transcription factors found in pl...

Predicting compound-protein interaction using hierarchical graph convolutional networks.

PloS one
MOTIVATION: Convolutional neural networks have enabled unprecedented breakthroughs in a variety of computer vision tasks. They have also drawn much attention from other domains, including drug discovery and drug development. In this study, we develop...

cACP-DeepGram: Classification of anticancer peptides via deep neural network and skip-gram-based word embedding model.

Artificial intelligence in medicine
Cancer is a Toxic health concern worldwide, it happens when cellular modifications cause the irregular growth and division of human cells. Several traditional approaches such as therapies and wet laboratory-based methods have been applied to treat ca...

A convolutional neural network based tool for predicting protein AMPylation sites from binary profile representation.

Scientific reports
AMPylation is an emerging post-translational modification that occurs on the hydroxyl group of threonine, serine, or tyrosine via a phosphodiester bond. AMPylators catalyze this process as covalent attachment of adenosine monophosphate to the amino a...

SDNN-PPI: self-attention with deep neural network effect on protein-protein interaction prediction.

BMC genomics
BACKGROUND: Protein-protein interactions (PPIs) dominate intracellular molecules to perform a series of tasks such as transcriptional regulation, information transduction, and drug signalling. The traditional wet experiment method to obtain PPIs info...