ArticleComputational and mathematical methods in medicine2021
iMPTCE-Hnetwork: A Multilabel Classifier for Identifying Metabolic Pathway Types of Chemicals and Enzymes with a Heterogeneous Network.
Article in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed.
- Prediction of plant secondary metabolic pathways using deep transfer learning.BMC bioinformatics · 2023Article
- MLGL-MP: a Multi-Label Graph Learning framework enhanced by pathway interdependence for Metabolic Pathway prediction.Bioinformatics (Oxford, England) · 2022Article
- Identification of cortical interneuron cell markers in mouse embryos based on machine learning analysis of single-cell transcriptomics.Frontiers in neuroscience · 2022Article
- Identification of Type 2 Diabetes Biomarkers From Mixed Single-Cell Sequencing Data With Feature Selection Methods.Frontiers in bioengineering and biotechnology · 2022Article
- Identifying Functions of Proteins in Mice With Functional Embedding Features.Frontiers in genetics · 2022Article
- Identifying luminal and basal mammary cell specific genes and their expression patterns during pregnancy.PloS one · 2022Article
- Similarity-Based Method with Multiple-Feature Sampling for Predicting Drug Side Effects.Computational and mathematical methods in medicine · 2022Article
- Predicting RNA 5-Methylcytosine Sites by Using Essential Sequence Features and Distributions.BioMed research international · 2022Article
- Identification of Novel Lung Cancer Driver Genes Connecting Different Omics Levels With a Heat Diffusion Algorithm.Frontiers in cell and developmental biology · 2022Article
- Detecting Brain Structure-Specific Methylation Signatures and Rules for Alzheimer's Disease.Frontiers in neuroscience · 2022Article
- Predicting gene phenotype by multi-label multi-class model based on essential functional features.Molecular genetics and genomics : MGG · 2021Article
- Analysis of the Sequence Characteristics of Antifreeze Protein.Life (Basel, Switzerland) · 2021Article
- A Random Walk-Based Method to Identify Candidate Genes Associated With Lymphoma.Frontiers in genetics · 2021Article
- iMPT-FDNPL: Identification of Membrane Protein Types with Functional Domains and a Natural Language Processing Approach.Computational and mathematical methods in medicine · 2021Article
- Identification of Novel Choroidal Neovascularization-Related Genes Using Laplacian Heat Diffusion Algorithm.BioMed research international · 2021Article
- Recognizing Pattern and Rule of Mutation Signatures Corresponding to Cancer Types.Frontiers in cell and developmental biology · 2021Article
- Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods.BioMed research international · 2021Article
- Identification of Microbiota Biomarkers With Orthologous Gene Annotation for Type 2 Diabetes.Frontiers in microbiology · 2021Article
- Article
- Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.Frontiers in cell and developmental biology · 2021Article
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4 authors.
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Abstract
Metabolic pathway is an important type of biological pathways. It produces essential molecules and energies to maintain the life of living organisms. Each metabolic pathway consists of a chain of chemical reactions, which always need enzymes to participate in. Thus, chemicals and enzymes are two major components for each metabolic pathway. Although several metabolic pathways have been uncovered, the metabolic pathway system is still far from complete. Some hidden chemicals or enzymes are not discovered in a certain metabolic pathway. Besides the traditional experiments to detect hidden chemicals or enzymes, an alternative pipeline is to design efficient computational methods. In this study, we proposed a powerful multilabel classifier, called iMPTCE-Hnetwork, to uniformly assign chemicals and enzymes to metabolic pathway types reported in KEGG. Such classifier adopted the embedding features derived from a heterogeneous network, which defined chemicals and enzymes as nodes and the interactions between chemicals and enzymes as edges, through a powerful network embedding algorithm, Mashup. The popular RAndom k-labELsets (RAKEL) algorithm was employed to construct the classifier, which incorporated the support vector machine (polynomial kernel) as the basic classifier. The ten-fold cross-validation results indicated that such a classifier had good performance with accuracy higher than 0.800 and exact match higher than 0.750. Several comparisons were done to indicate the superiority of the iMPTCE-Hnetwork.
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