[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 …
Semantic similarity in biomedical ontologies
In recent years, ontologies have become a mainstream topic in biomedical research. When
biological entities are described using a common schema, such as an ontology, they can be …
biological entities are described using a common schema, such as an ontology, they can be …
LightGBM-PPI: Predicting protein-protein interactions through LightGBM with multi-information fusion
Protein-protein interactions (PPIs) play an important role in cell life activities such as
transcriptional regulation, signal transduction and drug signal transduction. The study of …
transcriptional regulation, signal transduction and drug signal transduction. The study 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 …
[PDF][PDF] Kernel methods for predicting protein–protein interactions
Motivation: Despite advances in high-throughput methods for discovering protein–protein
interactions, the interaction networks of even well-studied model organisms are sketchy at …
interactions, the interaction networks of even well-studied model organisms are sketchy at …
Evaluation of different biological data and computational classification methods for use in protein interaction prediction
Protein–protein interactions play a key role in many biological systems. High‐throughput
methods can directly detect the set of interacting proteins in yeast, but the results are often …
methods can directly detect the set of interacting proteins in yeast, but the results are often …
High-throughput prediction of RNA, DNA and protein binding regions mediated by intrinsic disorder
Intrinsically disordered proteins and regions (IDPs and IDRs) lack stable 3D structure under
physiological conditions in-vitro, are common in eukaryotes, and facilitate interactions with …
physiological conditions in-vitro, are common in eukaryotes, and facilitate interactions with …
Large-Scale prediction of human protein− protein interactions from amino acid sequence based on latent topic features
Protein− protein interaction (PPI) is at the core of the entire interactomic system of any living
organism. Although there are many human protein− protein interaction links being …
organism. Although there are many human protein− protein interaction links being …
Evolution of in silico strategies for protein-protein interaction drug discovery
The advent of advanced molecular modeling software, big data analytics, and high-speed
processing units has led to the exponential evolution of modern drug discovery and better …
processing units has led to the exponential evolution of modern drug discovery and better …