Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions
Activity detection and classification using different sensor modalities have emerged as
revolutionary technology for real-time and autonomous monitoring in behaviour analysis …
revolutionary technology for real-time and autonomous monitoring in behaviour analysis …
Application of machine learning in microbiology
Microorganisms are ubiquitous and closely related to people's daily lives. Since they were
first discovered in the 19th century, researchers have shown great interest in …
first discovered in the 19th century, researchers have shown great interest in …
A survey of uncertainty in deep neural networks
Over the last decade, neural networks have reached almost every field of science and
become a crucial part of various real world applications. Due to the increasing spread …
become a crucial part of various real world applications. Due to the increasing spread …
Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways
Identifying essential genes in a given organism is important for research on their
fundamental roles in organism survival. Furthermore, if possible, uncovering the links …
fundamental roles in organism survival. Furthermore, if possible, uncovering the links …
A first computational frame for recognizing heparin-binding protein
W Zhu, SS Yuan, J Li, CB Huang, H Lin, B Liao - Diagnostics, 2023 - mdpi.com
Heparin-binding protein (HBP) is a cationic antibacterial protein derived from multinuclear
neutrophils and an important biomarker of infectious diseases. The correct identification of …
neutrophils and an important biomarker of infectious diseases. The correct identification of …
Comprehensive ensemble in QSAR prediction for drug discovery
Background Quantitative structure-activity relationship (QSAR) is a computational modeling
method for revealing relationships between structural properties of chemical compounds …
method for revealing relationships between structural properties of chemical compounds …
Deep-Resp-Forest: a deep forest model to predict anti-cancer drug response
The identification of therapeutic biomarkers predictive of drug response is crucial in
personalized medicine. A number of computational models to predict response of anti …
personalized medicine. A number of computational models to predict response of anti …
Meta-4mCpred: a sequence-based meta-predictor for accurate DNA 4mC site prediction using effective feature representation
DNA N4-methylcytosine (4mC) is an important genetic modification and plays crucial roles in
differentiation between self and non-self DNA and in controlling DNA replication, cell cycle …
differentiation between self and non-self DNA and in controlling DNA replication, cell cycle …
AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest
Antimicrobial peptides (AMPs) are promising candidates in the fight against multidrug-
resistant pathogens owing to AMPs' broad range of activities and low toxicity. Nonetheless …
resistant pathogens owing to AMPs' broad range of activities and low toxicity. Nonetheless …
Anticancer peptides prediction with deep representation learning features
Anticancer peptides constitute one of the most promising therapeutic agents for combating
common human cancers. Using wet experiments to verify whether a peptide displays …
common human cancers. Using wet experiments to verify whether a peptide displays …