Analysis and prediction of protein stability based on interaction network, gene ontology, and KEGG pathway enrichment scores

Huang, FeiMing; Fu, Minfei; Li, JiaRui; Chen, Lei; Kong, Feng; Huang, Tao; Cai, Yi

doi:10.1016/j.bbapap.2023.140889

Cited by 41 publications

(22 citation statements)

References 39 publications

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“…Similarly, if the KEGG enrichment fraction of a chemical substance and a pathway is high, then they are highly correlated. The KEGG enrichment score helps to evaluate the stability of metabolic pathways 40,41 . In this research, KEGG enrichment analysis showed that 37 metabolites in A. catechu seeds from Chinese producing areas were enriched in 51 pathways (only the top 20 pathways were shown), and 20 of the 37 metabolites were the differential metabolites listed above.…”

Section: Resultsmentioning

confidence: 74%

“…The KEGG enrichment score helps to evaluate the stability of metabolic pathways. 40,41 In this research, KEGG enrichment analysis showed that above. Among them, the "linoleic acid metabolism" pathway (ko00591, p = 0.0002484), the "fatty acid biosynthesis" pathway (ko00061, p = 0.03427), and the "porphyrin metabolism" pathway (ko00860, p = 0.035) were significantly enriched pathways (Figure 10A), and 28 metabolites were significantly enriched.…”

Section: Differential Metabolites Of Areca Nut Seeds From Different C...mentioning

confidence: 63%

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Comparative metabolomics study on areca nut from China and Southeast Asia (Thailand and Indonesia)

Zhang,

Sun,

et al. 2023

Phytochemical Analysis

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IntroductionAreca nut is an economic crop and an important component in traditional Chinese medicine (TCM) and ethnomedicine. The crop is rich in alkaloids and flavonoids. Most previous studies have focused on the chemical components, especially alkaloids, in crops from certain areca nut‐producing areas.ObjectiveThe purpose of this study was to compare the differences in areca nut seeds in two main cultivation areas, identify differential metabolites, and evaluate seed quality in different production areas.MethodsA widely targeted metabolomics method based on ultrahigh‐performance liquid chromatography coupled with triple quadrupole mass spectrometry (UHPLC‐QQQ‐MS), combined with the TCM systems pharmacology (TCMSP) database and multivariate statistical analysis, was used in this study to maximise the differentiation between quality characteristics of areca nut seeds from China and Southeast Asian regions.ResultsAltogether, 1031 metabolites were identified in areca nut seeds; by querying the TCMSP database, 375 metabolites were identified as the main active ingredients. Moreover, the research showed that the metabolic profiles of areca nut seeds from China (ASCN) and Southeast Asia (ASSA) exhibit significant differences, and the difference is mainly reflected in 318 compounds. The relative content of 146 metabolites in ASCN was significantly higher than that in ASSA. Through Kyoto Encyclopedia of Genes and Genomes (KEGG) comparative analysis, areca nut seed metabolites in Chinese production areas were determined to have a wider metabolic pathway.ConclusionThe areca nut seeds from cultivation areas possess many metabolites that are beneficial for health, including alkaloids, amino acids, phenolic acids, and lipids. Thus, compared with ASSA, ASCN have a higher medicinal value. This study provides a direction for the subsequent development and utilisation of areca nut seeds.

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Section: Resultsmentioning

confidence: 74%

Section: Differential Metabolites Of Areca Nut Seeds From Different C...mentioning

confidence: 63%

Comparative metabolomics study on areca nut from China and Southeast Asia (Thailand and Indonesia)

Zhang,

Sun,

et al. 2023

Phytochemical Analysis

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“…Random forest. The RF algorithm is an ensemble learning based on DT algorithms [ 14 , 22 , 23 , 24 , 25 , 26 ] and creates a number of independent DT classifiers that do not interfere with one another. These classifiers were constructed by randomly taking samples from the training set and features.…”

Section: Methodsmentioning

confidence: 99%

Identification of Genes Associated with the Impairment of Olfactory and Gustatory Functions in COVID-19 via Machine-Learning Methods

Ren

Zhang

Guo

et al. 2023

Life

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The coronavirus disease 2019 (COVID-19), as a severe respiratory disease, affects many parts of the body, and approximately 20–85% of patients exhibit functional impairment of the senses of smell and taste, some of whom even experience the permanent loss of these senses. These symptoms are not life-threatening but severely affect patients’ quality of life and increase the risk of depression and anxiety. The pathological mechanisms of these symptoms have not been fully identified. In the current study, we aimed to identify the important biomarkers at the expression level associated with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection-mediated loss of taste or olfactory ability, and we have suggested the potential pathogenetic mechanisms of COVID-19 complications. We designed a machine-learning-based approach to analyze the transcriptome of 577 COVID-19 patient samples, including 84 COVID-19 samples with a decreased ability to taste or smell and 493 COVID-19 samples without impairment. Each sample was represented by 58,929 gene expression levels. The features were analyzed and sorted by three feature selection methods (least absolute shrinkage and selection operator, light gradient boosting machine, and Monte Carlo feature selection). The optimal feature sets were obtained through incremental feature selection using two classification algorithms: decision tree (DT) and random forest (RF). The top genes identified by these multiple methods (H3-5, NUDT5, and AOC1) are involved in olfactory and gustatory impairments. Meanwhile, a high-performance RF classifier was developed in this study, and three sets of quantitative rules that describe the impairment of olfactory and gustatory functions were obtained based on the optimal DT classifiers. In summary, this study provides a new computation analysis and suggests the latent biomarkers (genes and rules) for predicting olfactory and gustatory impairment caused by COVID-19 complications.

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“…It is still quite difficult to extract essential features from a feature list to comprise an optimal feature space for a given classification algorithm. Here, we introduced IFS, a well-liked method for determining the optimal feature space (Liu and Setiono, 1998;Chen L. et al, 2019;Zhang et al, 2020;Huang et al, 2023a;Huang et al, 2023b). The main steps of IFS are as follows: 1) From the feature list, lots of feature subsets are constructed with a fixed step, each of which contained some top features in the list.…”

Section: Incremental Feature Selectionmentioning

confidence: 99%

Characterization of chromatin accessibility patterns in different mouse cell types using machine learning methods at single-cell resolution

Huang

Guo

et al. 2023

Front. Genet.

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Chromatin accessibility is a generic property of the eukaryotic genome, which refers to the degree of physical compaction of chromatin. Recent studies have shown that chromatin accessibility is cell type dependent, indicating chromatin heterogeneity across cell lines and tissues. The identification of markers used to distinguish cell types at the chromosome level is important to understand cell function and classify cell types. In the present study, we investigated transcriptionally active chromosome segments identified by sci-ATAC-seq at single-cell resolution, including 69,015 cells belonging to 77 different cell types. Each cell was represented by existence status on 20,783 genes that were obtained from 436,206 active chromosome segments. The gene features were deeply analyzed by Boruta, resulting in 3897 genes, which were ranked in a list by Monte Carlo feature selection. Such list was further analyzed by incremental feature selection (IFS) method, yielding essential genes, classification rules and an efficient random forest (RF) classifier. To improve the performance of the optimal RF classifier, its features were further processed by autoencoder, light gradient boosting machine and IFS method. The final RF classifier with MCC of 0.838 was constructed. Some marker genes such as H2-Dmb2, which are specifically expressed in antigen-presenting cells (e.g., dendritic cells or macrophages), and Tenm2, which are specifically expressed in T cells, were identified in this study. Our analysis revealed numerous potential epigenetic modification patterns that are unique to particular cell types, thereby advancing knowledge of the critical functions of chromatin accessibility in cell processes.

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Analysis and prediction of protein stability based on interaction network, gene ontology, and KEGG pathway enrichment scores

Cited by 41 publications

References 39 publications

Comparative metabolomics study on areca nut from China and Southeast Asia (Thailand and Indonesia)

Comparative metabolomics study on areca nut from China and Southeast Asia (Thailand and Indonesia)

Identification of Genes Associated with the Impairment of Olfactory and Gustatory Functions in COVID-19 via Machine-Learning Methods

Characterization of chromatin accessibility patterns in different mouse cell types using machine learning methods at single-cell resolution

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