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

Zhu, Yuanyuan; Hu, Bin; Lei, Chen; Dai, Qi

doi:10.1155/2021/6683051

Cited by 31 publications

(21 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…. SVM [32][33][34][35][36][37][38] consists of several computational steps. First, it transforms the original data from a low-dimensional data space to a highdimensional data space.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods

Lei

Zeng

et al. 2021

BioMed Research International

Self Cite

View full text Add to dashboard Cite

COVID-19, a severe respiratory disease caused by a new type of coronavirus SARS-CoV-2, has been spreading all over the world. Patients infected with SARS-CoV-2 may have no pathogenic symptoms, i.e., presymptomatic patients and asymptomatic patients. Both patients could further spread the virus to other susceptible people, thereby making the control of COVID-19 difficult. The two major challenges for COVID-19 diagnosis at present are as follows: (1) patients could share similar symptoms with other respiratory infections, and (2) patients may not have any symptoms but could still spread the virus. Therefore, new biomarkers at different omics levels are required for the large-scale screening and diagnosis of COVID-19. Although some initial analyses could identify a group of candidate gene biomarkers for COVID-19, the previous work still could not identify biomarkers capable for clinical use in COVID-19, which requires disease-specific diagnosis compared with other multiple infectious diseases. As an extension of the previous study, optimized machine learning models were applied in the present study to identify some specific qualitative host biomarkers associated with COVID-19 infection on the basis of a publicly released transcriptomic dataset, which included healthy controls and patients with bacterial infection, influenza, COVID-19, and other kinds of coronavirus. This dataset was first analysed by Boruta, Max-Relevance and Min-Redundancy feature selection methods one by one, resulting in a feature list. This list was fed into the incremental feature selection method, incorporating one of the classification algorithms to extract essential biomarkers and build efficient classifiers and classification rules. The capacity of these findings to distinguish COVID-19 with other similar respiratory infectious diseases at the transcriptomic level was also validated, which may improve the efficacy and accuracy of COVID-19 diagnosis.

show abstract

“…. SVM [32][33][34][35][36][37][38] consists of several computational steps. First, it transforms the original data from a low-dimensional data space to a highdimensional data space.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods

Lei

Zeng

et al. 2021

BioMed Research International

Self Cite

View full text Add to dashboard Cite

show abstract

“…After obtaining the outcomes of tenfold cross-validation, we calculated three measurements to assess the quality of results, including exact matching, accuracy, and hamming loss [ 25 – 27 ], which can be computed by

where n denotes the overall number of samples, m stands for the number of labels ( m = 6 in this study), L i and L i ′ represent the set of true labels and predicted labels of the i th sample, respectively,Δ stands for the set symmetric difference operation, and ∇ is defined as follows:

…”

Section: Methodsmentioning

confidence: 99%

iMPT-FDNPL: Identification of Membrane Protein Types with Functional Domains and a Natural Language Processing Approach

Chen

Dai

2021

Computational and Mathematical Methods in Medicine

Self Cite

View full text Add to dashboard Cite

Membrane protein is an important kind of proteins. It plays essential roles in several cellular processes. Based on the intramolecular arrangements and positions in a cell, membrane proteins can be divided into several types. It is reported that the types of a membrane protein are highly related to its functions. Determination of membrane protein types is a hot topic in recent years. A plenty of computational methods have been proposed so far. Some of them used functional domain information to encode proteins. However, this procedure was still crude. In this study, we designed a novel feature extraction scheme to obtain informative features of proteins from their functional domain information. Such scheme termed domains as words and proteins, represented by its domains, as sentences. The natural language processing approach, word2vector, was applied to access the features of domains, which were further refined to protein features. Based on these features, RAndom k-labELsets with random forest as the base classifier was employed to build the multilabel classifier, namely, iMPT-FDNPL. The tenfold cross-validation results indicated the good performance of such classifier. Furthermore, such classifier was superior to other classifiers based on features derived from functional domains via one-hot scheme or derived from other properties of proteins, suggesting the effectiveness of protein features generated by the proposed scheme.

show abstract

“…In detail, the first feature subset included the top feature in the list F , the second feature subset contained the top two features, and so forth. For each constructed feature subset, a random forest (RF) classifier was built based on samples represented by features in the subset and it was further evaluated by 10-fold cross-validation ( Kohavi, 1995 ; Li J. et al, 2020 ; Zhou et al, 2020 ; Liu et al, 2021 ; Pan et al, 2021 ; Zhang et al, 2021a , b , c ; Zhu et al, 2021 ). After testing all feature subsets, we obtained the optimal feature subset with the optimal performance.…”

Section: Methodsmentioning

confidence: 99%

Recognizing Pattern and Rule of Mutation Signatures Corresponding to Cancer Types

Lei

Zhou

Zeng

et al. 2021

Front. Cell Dev. Biol.

Self Cite

View full text Add to dashboard Cite

Cancer has been generally defined as a cluster of systematic malignant pathogenesis involving abnormal cell growth. Genetic mutations derived from environmental factors and inherited genetics trigger the initiation and progression of cancers. Although several well-known factors affect cancer, mutation features and rules that affect cancers are relatively unknown due to limited related studies. In this study, a computational investigation on mutation profiles of cancer samples in 27 types was given. These profiles were first analyzed by the Monte Carlo Feature Selection (MCFS) method. A feature list was thus obtained. Then, the incremental feature selection (IFS) method adopted such list to extract essential mutation features related to 27 cancer types, find out 207 mutation rules and construct efficient classifiers. The top 37 mutation features corresponding to different cancer types were discussed. All the qualitatively analyzed gene mutation features contribute to the distinction of different types of cancers, and most of such mutation rules are supported by recent literature. Therefore, our computational investigation could identify potential biomarkers and prediction rules for cancers in the mutation signature level.

show abstract

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

Cited by 31 publications

References 48 publications

Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods

Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods

iMPT-FDNPL: Identification of Membrane Protein Types with Functional Domains and a Natural Language Processing Approach

Recognizing Pattern and Rule of Mutation Signatures Corresponding to Cancer Types

Contact Info

Product

Resources

About