TCM2COVID: A resource of anti‐COVID‐19 traditional Chinese medicine with effects and mechanisms

Ren, Liping; Xu, Yi; Lin, Ning; Pan, Xianrun; Li, Yuchen; Zhao, Qi; Pang, Bo; Huang, Jian; Deng, Kejun; Zhang, Yang

doi:10.1002/imt2.42

Cited by 21 publications

(14 citation statements)

References 51 publications

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“…Thus, the oncogenic and inhibitory function of NKX2-1 in the same tumor type confirms its role as a bifunctional lineage factor. Aquaporins (AQPs) are water channel proteins that can be capable of selectively transporting water and other small solutes across cells [ 44 , 45 ]. In the lung, AQPs were supposed to facilitate fluid transportation in alveolar space, airway humidification, pleural fluid absorption, and submucosal gland secretion.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive Analysis of the Molecular Characteristics and Prognosis value of AT II-associated Genes in Non-small Cell Lung Cancer

Ren

Wen

Liu

et al. 2022

Computational and Mathematical Methods in Medicine

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Alveolar type II (AT II) is a key structure of the distal lung epithelium and essential to maintain normal lung homeostasis. Dedifferentiation of AT II cells is significantly correlated with lung tumor progression. However, the potential molecular mechanism and clinical significance of AT II-associated genes for lung cancer has not yet been fully elucidated. In this study, we comprehensively analyzed the gene expression, prognosis value, genetic alteration, and immune cell infiltration of eight AT II-associated genes (AQP4, SFTPB, SFTPC, SFTPD, CLDN18, FOXA2, NKX2-1, and PGC) in Nonsmall Cell Lung Cancer (NSCLC). The results have shown that the expression of eight genes were remarkably reduced in cancer tissues and observably relating to clinical cancer stages. Survival analysis of the eight genes revealed that low-expression of CLDN18, FOXA2, NKX2-1, PGC, SFTPB, SFTPC, and SFTPD were significantly related to a reduced progression-free survival (FP), and low CLDN18, FOXA2, and SFTPD mRNA expression led to a short postprogression survival (PPS). Meanwhile, the alteration of 8 AT II-associated genes covered 273 out of 1053 NSCLC samples (26%). Additionally, the expression level of eight genes were significantly correlated with the infiltration of diverse immune cells, including six types of CD4+T cells, macrophages, neutrophils, B cells, CD8+ T cells, and dendritic cells. In summary, this study provided clues of the values of eight AT II-associated genes as clinical biomarkers and therapeutic targets in NSCLC and might provide some new inspirations to assist the design of new immunotherapies.

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Section: Discussionmentioning

confidence: 99%

Comprehensive Analysis of the Molecular Characteristics and Prognosis value of AT II-associated Genes in Non-small Cell Lung Cancer

Ren

Wen

Liu

et al. 2022

Computational and Mathematical Methods in Medicine

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“…Representing the DNA sequences with a mathematical manifestation is very important in functional element identification. Some DNA sequences coding strategies such as accumulated nucleotide frequency, physiochemical properties, binary encodings, nucleotide chemical properties and k -tuple nucleotide frequency component, nucleotide pair spectrum encoding, and natural vector have been applied in bioinformatics (Dao et al, 2020 ; Yang X. et al, 2021 ; Zhang Y. et al, 2021 ; Ao et al, 2022b ; Ren et al, 2022 ). The performance of these feature descriptors was good.…”

Section: Methodsmentioning

confidence: 99%

Computational prediction of promotors in Agrobacterium tumefaciens strain C58 by using the machine learning technique

et al. 2023

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Promotors are those genomic regions on the upstream of genes, which are bound by RNA polymerase for starting gene transcription. Because it is the most critical element of gene expression, the recognition of promoters is crucial to understand the regulation of gene expression. This study aimed to develop a machine learning-based model to predict promotors in Agrobacterium tumefaciens (A. tumefaciens) strain C58. In the model, promotor sequences were encoded by three different kinds of feature descriptors, namely, accumulated nucleotide frequency, k-mer nucleotide composition, and binary encodings. The obtained features were optimized by using correlation and the mRMR-based algorithm. These optimized features were inputted into a random forest (RF) classifier to discriminate promotor sequences from non-promotor sequences in A. tumefaciens strain C58. The examination of 10-fold cross-validation showed that the proposed model could yield an overall accuracy of 0.837. This model will provide help for the study of promoters in A. tumefaciens C58 strain.

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“…SVM always seeks solutions for global optimization and avoids overfitting. SVM has been successfully applied to many bioinformatic problems (Wei et al, 2017;Wei et al, 2018;Manayalan et al, 2019a;Manayalan et al, 2019b;Ao et al, 2021;Basith et al, 2021;Zeng et al, 2021;Basith et al, 2022;, such as the disease development prediction (Zhang et al, 2020;Zhang et al, 2021a;Ren et al, 2022;Yu et al, 2022), protein prediction (Tang et al, 2018;Tao et al, 2020;Zou et al, 2021;Ao et al, 2022), etc. In this paper, a widely used software LIBSVM (http://www.csie.ntu.edu.tw/~cjlin/ libsvm) (Chang and Lin, 2011) was used to implement the support vector machine.…”

Section: Support Vector Machinementioning

confidence: 99%

Prediction of apoptosis protein subcellular location based on amphiphilic pseudo amino acid composition

Deng

et al. 2023

Front. Genet.

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Introduction: Apoptosis proteins play an important role in the process of cell apoptosis, which makes the rate of cell proliferation and death reach a relative balance. The function of apoptosis protein is closely related to its subcellular location, it is of great significance to study the subcellular locations of apoptosis proteins. Many efforts in bioinformatics research have been aimed at predicting their subcellular location. However, the subcellular localization of apoptotic proteins needs to be carefully studied.Methods: In this paper, based on amphiphilic pseudo amino acid composition and support vector machine algorithm, a new method was proposed for the prediction of apoptosis proteins\x{2019} subcellular location.Results and Discussion: The method achieved good performance on three data sets. The Jackknife test accuracy of the three data sets reached 90.5%, 93.9% and 84.0%, respectively. Compared with previous methods, the prediction accuracies of APACC_SVM were improved.

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TCM2COVID: A resource of anti‐COVID‐19 traditional Chinese medicine with effects and mechanisms

Cited by 21 publications

References 51 publications

Comprehensive Analysis of the Molecular Characteristics and Prognosis value of AT II-associated Genes in Non-small Cell Lung Cancer

Comprehensive Analysis of the Molecular Characteristics and Prognosis value of AT II-associated Genes in Non-small Cell Lung Cancer

Computational prediction of promotors in Agrobacterium tumefaciens strain C58 by using the machine learning technique

Prediction of apoptosis protein subcellular location based on amphiphilic pseudo amino acid composition

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