[HTML][HTML] Deep learning frameworks for protein–protein interaction prediction
X Hu, C Feng, T Ling, M Chen - Computational and structural …, 2022 - Elsevier
Protein-protein interactions (PPIs) play key roles in a broad range of biological processes.
The disorder of PPIs often causes various physical and mental diseases, which makes PPIs …
The disorder of PPIs often causes various physical and mental diseases, which makes PPIs …
Progress and challenges in predicting protein interfaces
The majority of biological processes are mediated via protein–protein interactions.
Determination of residues participating in such interactions improves our understanding of …
Determination of residues participating in such interactions improves our understanding of …
Random forest for bioinformatics
Y Qi - Ensemble machine learning: Methods and applications, 2012 - Springer
Modern biology has experienced an increased use of machine learning techniques for large
scale and complex biological data analysis. In the area of Bioinformatics, the Random Forest …
scale and complex biological data analysis. In the area of Bioinformatics, the Random Forest …
Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?
Abstract In the Life Sciences 'omics' data is increasingly generated by different high-
throughput technologies. Often only the integration of these data allows uncovering …
throughput technologies. Often only the integration of these data allows uncovering …
A computational-based method for predicting drug–target interactions by using stacked autoencoder deep neural network
Identifying the interaction between drugs and target proteins is an important area of drug
research, which provides a broad prospect for low-risk and faster drug development …
research, which provides a broad prospect for low-risk and faster drug development …
[PDF][PDF] Oversampling method for imbalanced classification
Classification problem for imbalanced datasets is pervasive in a lot of data mining domains.
Imbalanced classification has been a hot topic in the academic community. From data level …
Imbalanced classification has been a hot topic in the academic community. From data level …
Applying the Naïve Bayes classifier with kernel density estimation to the prediction of protein–protein interaction sites
Y Murakami, K Mizuguchi - Bioinformatics, 2010 - academic.oup.com
Motivation: The limited availability of protein structures often restricts the functional
annotation of proteins and the identification of their protein–protein interaction sites …
annotation of proteins and the identification of their protein–protein interaction sites …
DELPHI: accurate deep ensemble model for protein interaction sites prediction
Motivation Proteins usually perform their functions by interacting with other proteins, which is
why accurately predicting protein–protein interaction (PPI) binding sites is a fundamental …
why accurately predicting protein–protein interaction (PPI) binding sites is a fundamental …
SCRIBER: accurate and partner type-specific prediction of protein-binding residues from proteins sequences
Motivation Accurate predictions of protein-binding residues (PBRs) enhances understanding
of molecular-level rules governing protein–protein interactions, helps protein–protein …
of molecular-level rules governing protein–protein interactions, helps protein–protein …
iPPBS-Opt: a sequence-based ensemble classifier for identifying protein-protein binding sites by optimizing imbalanced training datasets
Knowledge of protein-protein interactions and their binding sites is indispensable for in-
depth understanding of the networks in living cells. With the avalanche of protein sequences …
depth understanding of the networks in living cells. With the avalanche of protein sequences …