Prediction of Drug Side Effects with a Refined Negative Sample Selection Strategy

Liang, Haiyan; Lei, Chen; Xian, Zhao; Zhang, Xiaolin

doi:10.1155/2020/1573543

Cited by 59 publications

(52 citation statements)

References 41 publications

(60 reference statements)

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“…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%

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

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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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Section: Resultsmentioning

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

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“…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.

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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.

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“…The selection of classification algorithm is very important for constructing efficient classification models. In this study, a powerful and classic classification algorithm, RF [12], was adopted, which has been widely used to tackle several problems in bioinformatics [9,16,[20][21][22][23][24][25][26][27][28]. Its brief description was as below.…”

Section: Random Forestmentioning

confidence: 99%

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

2020

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Metabolic pathways refer to the continuous chemical reactions in the metabolic process in vivo. Compounds are the major participant for most metabolic pathways. It is essential to determine which compounds can constitute a metabolic pathway. This problem can be converted to the identification of the metabolic pathways of compounds. Although traditional experiments can provide solid results, they are always of low efficiency and high cost. To date, several machine leaning models have been proposed to address this problem. However, almost all models only identified metabolic pathway types of compounds rather than actual metabolic pathways. This study proposed a novel model for predicting actual metabolic pathways for given compounds. The pairs of compounds and metabolic pathways were termed as samples, thereby modeling a binary classification problem. With the concept of "similarity", each sample was represented by seven features, extracted from seven associations of compounds, which measure compound linkages from different aspects. The model adopted random forest as the classification algorithm. Two types of tenfold cross-validation were adopted to evaluate the performance of the model, indicating its utility. A feature analysis was also performed to determine which compound association was highly related to the identification of metabolic pathways of compounds.

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Prediction of Drug Side Effects with a Refined Negative Sample Selection Strategy

Cited by 59 publications

References 41 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 Robust Microbiota Signatures and Interpretable Rules to Distinguish Cancer Subtypes

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

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