AI Medical Compendium Topic:
Amino Acid Sequence

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PSPEL: In Silico Prediction of Self-Interacting Proteins from Amino Acids Sequences Using Ensemble Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Self interacting proteins (SIPs) play an important role in various aspects of the structural and functional organization of the cell. Detecting SIPs is one of the most important issues in current molecular biology. Although a large number of SIPs dat...

Improved protein-protein interactions prediction via weighted sparse representation model combining continuous wavelet descriptor and PseAA composition.

BMC systems biology
BACKGROUND: Protein-protein interactions (PPIs) are essential to most biological processes. Since bioscience has entered into the era of genome and proteome, there is a growing demand for the knowledge about PPI network. High-throughput biological te...

Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy.

BMC systems biology
BACKGROUND: It is necessary and essential to discovery protein function from the novel primary sequences. Wet lab experimental procedures are not only time-consuming, but also costly, so predicting protein structure and function reliably based only o...

Prediction of HIV-1 protease cleavage site using a combination of sequence, structural, and physicochemical features.

BMC bioinformatics
BACKGROUND: The human immunodeficiency virus type 1 (HIV-1) aspartic protease is an important enzyme owing to its imperative part in viral development and a causative agent of deadliest disease known as acquired immune deficiency syndrome (AIDS). Dev...

Mapping membrane activity in undiscovered peptide sequence space using machine learning.

Proceedings of the National Academy of Sciences of the United States of America
There are some ∼1,100 known antimicrobial peptides (AMPs), which permeabilize microbial membranes but have diverse sequences. Here, we develop a support vector machine (SVM)-based classifier to investigate ⍺-helical AMPs and the interrelated nature o...

Predicting Protein-DNA Binding Residues by Weightedly Combining Sequence-Based Features and Boosting Multiple SVMs.

IEEE/ACM transactions on computational biology and bioinformatics
Protein-DNA interactions are ubiquitous in a wide variety of biological processes. Correctly locating DNA-binding residues solely from protein sequences is an important but challenging task for protein function annotations and drug discovery, especia...

SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity.

PloS one
Knowledge of protein function is important for biological, medical and therapeutic studies, but many proteins are still unknown in function. There is a need for more improved functional prediction methods. Our SVM-Prot web-server employed a machine l...

Machine Learning of Protein Interactions in Fungal Secretory Pathways.

PloS one
In this paper we apply machine learning methods for predicting protein interactions in fungal secretion pathways. We assume an inter-species transfer setting, where training data is obtained from a single species and the objective is to predict prote...

gDNA-Prot: Predict DNA-binding proteins by employing support vector machine and a novel numerical characterization of protein sequence.

Journal of theoretical biology
DNA-binding proteins are the functional proteins in cells, which play an important role in various essential biological activities. An effective and fast computational method gDNA-Prot is proposed to predict DNA-binding proteins in this paper, which ...

GGIP: Structure and sequence-based GPCR-GPCR interaction pair predictor.

Proteins
G Protein-Coupled Receptors (GPCRs) are important pharmaceutical targets. More than 30% of currently marketed pharmaceutical medicines target GPCRs. Numerous studies have reported that GPCRs function not only as monomers but also as homo- or hetero-d...