ArticlePloS one2011
Classification and analysis of regulatory pathways using graph property, biochemical and physicochemical property, and functional property.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
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- Identification of uveitis-associated functions based on the feature selection analysis of gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment scores.Frontiers in molecular neuroscience · 2022Article
- Computational Identification of Guillain-Barré Syndrome-Related Genes by an mRNA Gene Expression Profile and a Protein-Protein Interaction Network.Frontiers in molecular neuroscience · 2022Article
- Use of a Network-Based Method to Identify Latent Genes Associated with Hearing Loss in Children.Frontiers in cell and developmental biology · 2021Article
- Article
- Inferring Novel Tumor Suppressor Genes with a Protein-Protein Interaction Network and Network Diffusion Algorithms.Molecular therapy. Methods & clinical development · 2018Article
- Computational Approach to Investigating Key GO Terms and KEGG Pathways Associated with CNV.BioMed research international · 2018Article
- Identifying and analyzing different cancer subtypes using RNA-seq data of blood platelets.Oncotarget · 2017Article
- Prediction of the aquatic toxicity of aromatic compounds to tetrahymena pyriformis through support vector regression.Oncotarget · 2017Article
- A Multi-Modal Graph-Based Semi-Supervised Pipeline for Predicting Cancer Survival.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2016Article
- Analysis of Important Gene Ontology Terms and Biological Pathways Related to Pancreatic Cancer.BioMed research international · 2016Article
- Analysis and Identification of Aptamer-Compound Interactions with a Maximum Relevance Minimum Redundancy and Nearest Neighbor Algorithm.BioMed research international · 2016Article
- Identification of potential biomarkers to differentially diagnose solid pseudopapillary tumors and pancreatic malignancies via a gene regulatory network.Journal of translational medicine · 2015Article
- Modelling the molecular mechanism of protein-protein interactions and their inhibition: CypD-p53 case study.Molecular diversity · 2015Article
- Identifying new targets in leukemogenesis using computational approaches.Saudi journal of biological sciences · 2015Article
- Discriminating between deleterious and neutral non-frameshifting indels based on protein interaction networks and hybrid properties.Molecular genetics and genomics : MGG · 2015Article
- Article
- Identification of novel thyroid cancer-related genes and chemicals using shortest path algorithm.BioMed research international · 2015Article
- Enhancing protein-vitamin binding residues prediction by multiple heterogeneous subspace SVMs ensemble.BMC bioinformatics · 2014Article
- Prediction of protein-protein interaction with pairwise kernel support vector machine.International journal of molecular sciences · 2014Article
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4 authors.
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Abstract
Given a regulatory pathway system consisting of a set of proteins, can we predict which pathway class it belongs to? Such a problem is closely related to the biological function of the pathway in cells and hence is quite fundamental and essential in systems biology and proteomics. This is also an extremely difficult and challenging problem due to its complexity. To address this problem, a novel approach was developed that can be used to predict query pathways among the following six functional categories: (i) "Metabolism", (ii) "Genetic Information Processing", (iii) "Environmental Information Processing", (iv) "Cellular Processes", (v) "Organismal Systems", and (vi) "Human Diseases". The prediction method was established trough the following procedures: (i) according to the general form of pseudo amino acid composition (PseAAC), each of the pathways concerned is formulated as a 5570-D (dimensional) vector; (ii) each of components in the 5570-D vector was derived by a series of feature extractions from the pathway system according to its graphic property, biochemical and physicochemical property, as well as functional property; (iii) the minimum redundancy maximum relevance (mRMR) method was adopted to operate the prediction. A cross-validation by the jackknife test on a benchmark dataset consisting of 146 regulatory pathways indicated that an overall success rate of 78.8% was achieved by our method in identifying query pathways among the above six classes, indicating the outcome is quite promising and encouraging. To the best of our knowledge, the current study represents the first effort in attempting to identity the type of a pathway system or its biological function. It is anticipated that our report may stimulate a series of follow-up investigations in this new and challenging area.
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