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DeepAVP-TPPred: identification of antiviral peptides using transformed image-based localized descriptors and binary tree growth algorithm
Motivation Despite the extensive manufacturing of antiviral drugs and vaccination, viral
infections continue to be a major human ailment. Antiviral peptides (AVPs) have emerged as …
infections continue to be a major human ailment. Antiviral peptides (AVPs) have emerged as …
Protein secondary structure prediction: A survey of the state of the art
Protein secondary structure prediction (PSSP) is a fundamental task in protein science and
computational biology, and it can be used to understand protein 3-dimensional (3-D) …
computational biology, and it can be used to understand protein 3-dimensional (3-D) …
Designing template-free predictor for targeting protein-ligand binding sites with classifier ensemble and spatial clustering
Accurately identifying the protein-ligand binding sites or pockets is of significant importance
for both protein function analysis and drug design. Although much progress has been made …
for both protein function analysis and drug design. Although much progress has been made …
Predicting protein-DNA binding residues by weightedly combining sequence-based features and boosting multiple SVMs
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 …
locating DNA-binding residues solely from protein sequences is an important but …
Improving protein-ATP binding residues prediction by boosting SVMs with random under-sampling
Correctly localizing the protein-ATP binding residues is valuable for both basic experimental
biology and drug discovery studies. Protein-ATP binding residues prediction is a typical …
biology and drug discovery studies. Protein-ATP binding residues prediction is a typical …
DeepCPPred: a deep learning framework for the discrimination of cell-penetrating peptides and their uptake efficiencies
Cell-penetrating peptides (CPPs) are special peptides capable of carrying a variety of
bioactive molecules, such as genetic materials, short interfering RNAs and nanoparticles …
bioactive molecules, such as genetic materials, short interfering RNAs and nanoparticles …
A cascade random forests algorithm for predicting protein-protein interaction sites
ZS Wei, JY Yang, HB Shen… - IEEE transactions on …, 2015 - ieeexplore.ieee.org
Protein-protein interactions exist ubiquitously and play important roles in the life cycles of
living cells. The interaction sites (residues) are essential to understanding the underlying …
living cells. The interaction sites (residues) are essential to understanding the underlying …
An enhanced protein secondary structure prediction using deep learning framework on hybrid profile based features
Accurate protein secondary structure prediction (PSSP) is essential to identify structural
classes, protein folds, and its tertiary structure. To identify the secondary structure …
classes, protein folds, and its tertiary structure. To identify the secondary structure …
Constructing query-driven dynamic machine learning model with application to protein-ligand binding sites prediction
We are facing an era with annotated biological data rapidly and continuously generated.
How to effectively incorporate new annotated data into the learning step is crucial for …
How to effectively incorporate new annotated data into the learning step is crucial for …
Metaheuristics for Protein Structure Prediction: Review and Empirical Analysis
SO Oladejo - 2024 IEEE International Conference on Artificial …, 2024 - ieeexplore.ieee.org
Metaheuristics have been employed in solving several optimization and NP-hard problems
owing to their ability to avoid local optima entrapment, flexibility and robustness, simplicity …
owing to their ability to avoid local optima entrapment, flexibility and robustness, simplicity …