(b) is cellular compartment distribution of H. sapiens proteins targeted by the conventional homology-based approach predicted host-pathogen PPIs (Top 10 cellular compartments).
(a) is cellular compartment distribution of H. sapiens proteins targeted by the stringent homology-based approach predicted host-pathogen PPIs (Top 10 cellular compartments).
(a) summarizes the most significantly enriched level 5 MF (Molecular Function) GO terms for H. sapiens proteins involved in the stringent homology-based approach predicted host-pathogen PPI dataset using DAVID database (threshold "count >2, p-value <0.01").
(b) summarizes the most significantly enriched level 5 MF (Molecular Function) GO terms for H. sapiens proteins involved in the conventional homology-based approach predicted host-pathogen PPI dataset using DAVID database (threshold "count >2, p-value <0.01").
In contrast, there are three relevant CC terms out of the top ten most frequent cellular compartments where the conventional homology-based approach predicted host-pathogen PPIs targeted H. sapiens proteins locate at.
As proteins from the same family exhibit similar characteristics, homology based approaches predict protein functions via protein classification.
In this study, we established a machine learning based approach to predict whether an intronic miRNA is high co-expressed with its host gene.
Here, we present a machine learning based approach to predict whether an intronic microRNA show high co-expression with its host gene, by doing so, we could infer the tissues in which a microRNA is high expressed through the expression profile of its host gene.
In summary, we presented a machine learning based approach to predict the co-expression patterns of the human intronic miRNAs and their host genes, which show a high accuracy and validation and could be further extended to other species.
Nagaraj and Reverter [ 1] proposed a Boolean logic based approach to predict colorectal cancer genes.
We compare our stringent homology-based approach to a conventional homology-based approach for predicting host-pathogen PPIs, based on cellular compartment distribution analysis, disease gene list enrichment analysis, pathway enrichment analysis and functional category enrichment analysis.
