[HTML][HTML] Random forests for genomic data analysis
Random forests (RF) is a popular tree-based ensemble machine learning tool that is highly
data adaptive, applies to “large p, small n” problems, and is able to account for correlation as …
data adaptive, applies to “large p, small n” problems, and is able to account for correlation as …
Computational approaches for the design of modulators targeting protein-protein interactions
ABSTRACT Background Protein-protein interactions (PPIs) are intriguing targets for
designing novel small-molecule inhibitors. The role of PPIs in various infectious and …
designing novel small-molecule inhibitors. The role of PPIs in various infectious and …
Genomic prediction of breeding values using a subset of SNPs identified by three machine learning methods
The analysis of large genomic data is hampered by issues such as a small number of
observations and a large number of predictive variables (commonly known as “large P small …
observations and a large number of predictive variables (commonly known as “large P small …
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 …
[HTML][HTML] Computational prediction of protein interfaces: A review of data driven methods
Reliably pinpointing which specific amino acid residues form the interface (s) between a
protein and its binding partner (s) is critical for understanding the structural and …
protein and its binding partner (s) is critical for understanding the structural and …
Supervised learning with decision tree-based methods in computational and systems biology
At the intersection between artificial intelligence and statistics, supervised learning allows
algorithms to automatically build predictive models from just observations of a system …
algorithms to automatically build predictive models from just observations of a system …
Sequence-based prediction of protein-protein interaction sites by simplified long short-term memory network
Proteins often interact with each other and form protein complexes to carry out various
biochemical activities. Knowledge of the interaction sites is helpful for understanding …
biochemical activities. Knowledge of the interaction sites is helpful for understanding …
Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition
With the explosive growth of protein sequences entering into protein data banks in the post-
genomic era, it is highly demanded to develop automated methods for rapidly and effectively …
genomic era, it is highly demanded to develop automated methods for rapidly and effectively …
[HTML][HTML] Systems Biology and Bioinformatics approach to Identify blood based signatures molecules and drug targets of patient with COVID-19
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection results in the
development of a highly contagious respiratory ailment known as new coronavirus disease …
development of a highly contagious respiratory ailment known as new coronavirus disease …
Deep learning of high-order interactions for protein interface prediction
Protein interactions are important in a broad range of biological processes. Traditionally,
computational methods have been developed to automatically predict protein interface from …
computational methods have been developed to automatically predict protein interface from …