AI Medical Compendium Journal:
Journal of theoretical biology

Showing 21 to 30 of 59 articles

Prediction of RNA-protein interactions by combining deep convolutional neural network with feature selection ensemble method.

Journal of theoretical biology
RNA-protein interaction (RPI) plays an important role in the basic cellular processes of organisms. Unfortunately, due to time and cost constraints, it is difficult for biological experiments to determine the relationship between RNA and protein to a...

Effective DNA binding protein prediction by using key features via Chou's general PseAAC.

Journal of theoretical biology
DNA-binding proteins (DBPs) are responsible for several cellular functions, starting from our immunity system to the transport of oxygen. In the recent studies, scientists have used supervised machine learning based methods that use information from ...

PBMDR: A particle swarm optimization-based multifactor dimensionality reduction for the detection of multilocus interactions.

Journal of theoretical biology
Studies on multilocus interactions have mainly investigated the associations between genetic variations from the related genes and histopathological tumor characteristics in patients. However, currently, the identification and characterization of sus...

Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou's PseAAC.

Journal of theoretical biology
The prediction of subcellular localization of an apoptosis protein is still a challenging task, and existing methods mainly based on protein primary sequences. In this study, we propose a novel model called MACC-PSSM by integrating Moran autocorrelat...

BlaPred: Predicting and classifying β-lactamase using a 3-tier prediction system via Chou's general PseAAC.

Journal of theoretical biology
Antibiotics of β-lactam class account for nearly half of the global antibiotic use. The β-lactamase enzyme is a major element of the bacterial arsenals to escape the lethal effect of β-lactam antibiotics. Different variants of β-lactamases have evolv...

Predicting membrane protein types by incorporating a novel feature set into Chou's general PseAAC.

Journal of theoretical biology
Membrane proteins are vital type of proteins that serve as channels, receptors and energy transducers in a cell. They perform various important functions, which are mainly associated with their types. They are also attractive targets of drug discover...

iMethyl-STTNC: Identification of N-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences.

Journal of theoretical biology
N- methyladenosine (mA) is a vital post-transcriptional modification, which adds another layer of epigenetic regulation at RNA level. It chemically modifies mRNA that effects protein expression. RNA sequence contains many genetic code motifs (GAC). A...

DPP-PseAAC: A DNA-binding protein prediction model using Chou's general PseAAC.

Journal of theoretical biology
A DNA-binding protein (DNA-BP) is a protein that can bind and interact with a DNA. Identification of DNA-BPs using experimental methods is expensive as well as time consuming. As such, fast and accurate computational methods are sought for predicting...

Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC.

Journal of theoretical biology
This study examines accurate and efficient computational method for identification of 5-methylcytosine sites in RNA modification. The occurrence of 5-methylcytosine (mC) plays a vital role in a number of biological processes. For better comprehension...

Using Chou's general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains.

Journal of theoretical biology
The receptor-associated protein (RAP) is an inhibitor of endocytic receptors that belong to the lipoprotein receptor gene family. In this study, a computational approach was tried to find the evolutionarily related fold of the RAP proteins. Through t...