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Review and comparative assessment of sequence-based predictors of protein-binding residues
Understanding of molecular mechanisms that govern protein–protein interactions and
accurate modeling of protein–protein docking rely on accurate identification and prediction …
accurate modeling of protein–protein docking rely on accurate identification and prediction …
Protein–protein interaction predictions using text mining methods
It is beyond any doubt that proteins and their interactions play an essential role in most
complex biological processes. The understanding of their function individually, but also in …
complex biological processes. The understanding of their function individually, but also in …
iPPI-Esml: an ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC
A cell contains thousands of proteins. Many important functions of cell are carried out
through the proteins therein. Proteins rarely function alone. Most of their functions essential …
through the proteins therein. Proteins rarely function alone. Most of their functions essential …
Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis
Abstract Background Protein-protein interactions (PPIs) play crucial roles in the execution of
various cellular processes and form the basis of biological mechanisms. Although large …
various cellular processes and form the basis of biological mechanisms. Although large …
[HTML][HTML] Deep neural network based predictions of protein interactions using primary sequences
H Li, XJ Gong, H Yu, C Zhou - Molecules, 2018 - mdpi.com
Machine learning based predictions of protein–protein interactions (PPIs) could provide
valuable insights into protein functions, disease occurrence, and therapy design on a large …
valuable insights into protein functions, disease occurrence, and therapy design on a large …
[HTML][HTML] iStable 2.0: Predicting protein thermal stability changes by integrating various characteristic modules
Protein mutations can lead to structural changes that affect protein function and result in
disease occurrence. In protein engineering, drug design or and optimization industries …
disease occurrence. In protein engineering, drug design or and optimization industries …
Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study
Background Diabetic mellitus (DM) and cardiovascular diseases (CVD) cause significant
healthcare burden globally and often co-exists. Current approaches often fail to identify …
healthcare burden globally and often co-exists. Current approaches often fail to identify …
Predicting protein-protein interactions from primary protein sequences using a novel multi-scale local feature representation scheme and the random forest
The study of protein-protein interactions (PPIs) can be very important for the understanding
of biological cellular functions. However, detecting PPIs in the laboratories are both time …
of biological cellular functions. However, detecting PPIs in the laboratories are both time …
StackDPPred: a stacking based prediction of DNA-binding protein from sequence
Motivation Identification of DNA-binding proteins from only sequence information is one of
the most challenging problems in the field of genome annotation. DNA-binding proteins play …
the most challenging problems in the field of genome annotation. DNA-binding proteins play …
Prediction of protein-protein interactions from amino acid sequences using a novel multi-scale continuous and discontinuous feature set
Background Identifying protein-protein interactions (PPIs) is essential for elucidating protein
functions and understanding the molecular mechanisms inside the cell. However, the …
functions and understanding the molecular mechanisms inside the cell. However, the …