Similarity-Based Machine Learning Model for Predicting the Metabolic Pathways of Compounds

Jia, Yanjuan; Zhao, Ran; Lei, Chen

doi:10.1109/access.2020.3009439

Cited by 58 publications

(41 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, we also tried SVM (RBF kernel) and random forest (RF) [ 43 ]. Like SVM, RF is also a widely used and powerful classification algorithm [ 8 , 11 , 22 , 44 – 47 ]. For SVM (RBF kernel), the same values of regularization parameter C were tried, and γ was set to 0.01, 0.02, and 0.03.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

iMPTCE-Hnetwork: A Multilabel Classifier for Identifying Metabolic Pathway Types of Chemicals and Enzymes with a Heterogeneous Network

Zhu

Lei

et al. 2021

Computational and Mathematical Methods in Medicine

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

“…In this case, the multilabel classification problem was transformed into a binary classification problem. Jia et al [ 11 ] extended the above model to an actual metabolic pathway rather than a pathway type. The concept of “similarity” was adopted to extract essential features for each pair of chemical and pathway.…”

Section: Introductionmentioning

confidence: 99%

iMPTCE-Hnetwork: A Multilabel Classifier for Identifying Metabolic Pathway Types of Chemicals and Enzymes with a Heterogeneous Network

Zhu

Lei

et al. 2021

Computational and Mathematical Methods in Medicine

Self Cite

View full text Add to dashboard Cite

show abstract

“…Random forest is widely adopted in the investigation of biological and biomedical data ( Pan et al, 2010 ; Zhao et al, 2018 ; Chen et al, 2019 ; Jia et al, 2020 ; Liang et al, 2020 ), and it has shown satisfactory performance in numerous studies. As a meta classifier, RF consists of multiple DTs, where each DT is learned from a bootstrap sample set with a randomly selected feature subset.…”

Section: Methodsmentioning

confidence: 99%

Identifying Transcriptomic Signatures and Rules for SARS-CoV-2 Infection

Zhang

Zeng

et al. 2021

Front. Cell Dev. Biol.

Self Cite

View full text Add to dashboard Cite

The world-wide Coronavirus Disease 2019 (COVID-19) pandemic was triggered by the widespread of a new strain of coronavirus named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Multiple studies on the pathogenesis of SARS-CoV-2 have been conducted immediately after the spread of the disease. However, the molecular pathogenesis of the virus and related diseases has still not been fully revealed. In this study, we attempted to identify new transcriptomic signatures as candidate diagnostic models for clinical testing or as therapeutic targets for vaccine design. Using the recently reported transcriptomics data of upper airway tissue with acute respiratory illnesses, we integrated multiple machine learning methods to identify effective qualitative biomarkers and quantitative rules for the distinction of SARS-CoV-2 infection from other infectious diseases. The transcriptomics data was first analyzed by Boruta so that important features were selected, which were further evaluated by the minimum redundancy maximum relevance method. A feature list was produced. This list was fed into the incremental feature selection, incorporating some classification algorithms, to extract qualitative biomarker genes and construct quantitative rules. Also, an efficient classifier was built to identify patients infected with SARS-COV-2. The findings reported in this study may help in revealing the potential pathogenic mechanisms of COVID-19 and finding new targets for vaccine design.

show abstract

“…The RF ( Breiman, 2001 ; Wei et al, 2017 ; Zhao et al, 2018 ; Baranwal et al, 2019 ; Jia et al, 2020 ; Liang et al, 2020 ) is a tree-based assembly model that predicts the class label of a new sample on the basis of the consensus results of the average predictions from multiple decision trees (DTs). In the present study, we used the RF implemented in the Scikit-learn package.…”

Section: Methodsmentioning

confidence: 99%

Identifying Robust Microbiota Signatures and Interpretable Rules to Distinguish Cancer Subtypes

Chen

Zeng

et al. 2020

Front. Mol. Biosci.

Self Cite

View full text Add to dashboard Cite

Cancer can be generally defined as a cluster of systematic diseases triggered by abnormal cell proliferation and growth. With the development of biological sciences and biotechnologies, the etiology of cancer is partially revealed, including some of the most substantial pathogenic factors [either endogenous (genetics) or exogenous (environmental)]. However, some remaining factors that contribute to the tumorigenesis but have not been analyzed and discussed in detail remain. For instance, some typical correlations between microorganisms and tumorigenesis have been reported already, but previous studies are just sporadic studies on single microorganism-cancer subtype pairs and do not explain and validate the specific contribution of microbiome on tumorigenesis. On the basis of the systematic microbiome analyses of blood and cancer-associated tissues in cancer patients/controls in public domain, we performed interpretable analyses. We identified several core regulatory microorganisms that contribute to the classification of multiple tumor subtypes and established quantitative predictive models for interpretable prediction by using multiple machine learning methods. We also compared the optimal features (microorganisms) and rules identified from microbiome profiles processed using the Kraken and the SHOGUN. Collectively, our study identified new microbiome signatures and their interpretable classification rules for cancer discrimination and carried out reliable methodological comparison for robust cancer microbiome analyses, thereby promoting the development of tumor etiology at the microbiome level.

show abstract

Similarity-Based Machine Learning Model for Predicting the Metabolic Pathways of Compounds

Cited by 58 publications

References 37 publications

iMPTCE-Hnetwork: A Multilabel Classifier for Identifying Metabolic Pathway Types of Chemicals and Enzymes with a Heterogeneous Network

iMPTCE-Hnetwork: A Multilabel Classifier for Identifying Metabolic Pathway Types of Chemicals and Enzymes with a Heterogeneous Network

Identifying Transcriptomic Signatures and Rules for SARS-CoV-2 Infection

Identifying Robust Microbiota Signatures and Interpretable Rules to Distinguish Cancer Subtypes

Contact Info

Product

Resources

About