Computational prediction of drug–target interactions using chemogenomic approaches: an empirical survey
Computational prediction of drug–target interactions (DTIs) has become an essential task in
the drug discovery process. It narrows down the search space for interactions by suggesting …
the drug discovery process. It narrows down the search space for interactions by suggesting …
Data science in unveiling COVID-19 pathogenesis and diagnosis: evolutionary origin to drug repurposing
Motivation The outbreak of novel severe acute respiratory syndrome coronavirus (SARS-
CoV-2, also known as COVID-19) in Wuhan has attracted worldwide attention. SARS-CoV-2 …
CoV-2, also known as COVID-19) in Wuhan has attracted worldwide attention. SARS-CoV-2 …
Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon
F **anghua, L Guo, G Yanyan, W Zhiqiang - Knowledge-Based Systems, 2013 - Elsevier
User-generated reviews on the Web reflect users' sentiment about products, services and
social events. Existing researches mostly focus on the sentiment classification of the product …
social events. Existing researches mostly focus on the sentiment classification of the product …
Semantic similarity analysis of protein data: assessment with biological features and issues
The integration of proteomics data with biological knowledge is a recent trend in
bioinformatics. A lot of biological information is available and is spread on different sources …
bioinformatics. A lot of biological information is available and is spread on different sources …
Survey of local and global biological network alignment: the need to reconcile the two sides of the same coin
Analogous to genomic sequence alignment that allows for across-species transfer of
biological knowledge between conserved sequence regions, biological network alignment …
biological knowledge between conserved sequence regions, biological network alignment …
Challenges and limitations of biological network analysis
High-Throughput technologies are producing an increasing volume of data that needs large
amounts of data storage, effective data models and efficient, possibly parallel analysis …
amounts of data storage, effective data models and efficient, possibly parallel analysis …
Understanding complex systems through differential causal networks
In the evolving landscape of data science and computational biology, Causal Networks
(CNs) have emerged as a robust framework for modelling causal relationships among …
(CNs) have emerged as a robust framework for modelling causal relationships among …
A method based on temporal embedding for the pairwise alignment of dynamic networks
In network analysis, real-world systems may be represented via graph models, where nodes
and edges represent the set of biological objects (eg, genes, proteins, molecules) and their …
and edges represent the set of biological objects (eg, genes, proteins, molecules) and their …
Visualization of protein interaction networks: problems and solutions
Background Visualization concerns the representation of data visually and is an important
task in scientific research. Protein-protein interactions (PPI) are discovered using either wet …
task in scientific research. Protein-protein interactions (PPI) are discovered using either wet …
Temporal networks in biology and medicine: a survey on models, algorithms, and tools
The use of static graphs for modelling and analysis of biological and biomedical data plays a
key role in biomedical research. However, many real-world scenarios present dynamic …
key role in biomedical research. However, many real-world scenarios present dynamic …