Identifying the Risky SNP of Osteoporosis with ID3-PEP Decision Tree Algorithm

Yang, Jincai; Gu, Huichao; Jiang, Xingpeng; Huang, Qingyang; Hu, Xiaohua; Shen, Xin

doi:10.1155/2017/9194801

Cited by 3 publications

(2 citation statements)

References 21 publications

(21 reference statements)

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“…The expression of TNS4 was widely analyzed in cancers. The risky SNPs of RALY and SYNPO associated with osteoporosis were reported as well [ 39 ]. The RALY expression was significantly downregulated in non-small cell lung cancer patients with bone metastasis [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

Consensus gene modules related to levels of bone mineral density (BMD) among smokers and nonsmokers

Lin

Pan

2021

Bioengineered

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Osteoporosis, as a common metabolic disorder characterized by the decrease of bone mass, can cause fractures, thereby threatening the life quality of females, especially postmenopausal women. Thus, it is necessary to reveal the genes involved in osteoporosis and explore biomarkers for osteoporosis. In this study, two groups, smokers and nonsmokers with different bone mineral density (BMD) levels, were collected from the Gene Expression Omnibus (GEO) database GSE13850. Consensus modules of the two groups were identified; the variety of gene modules between smokers and nonsmokers with different BMD levels was observed; and a consensus module, including 390 genes significantly correlated with different BMD levels, was identified. Function analysis revealed the significantly enriched osteoporosis-related pathways, such as the PI3K-Akt signaling pathway. Hub genes analysis revealed the critical role of CXCL12 and CHRM2 in modules related to BMD levels. Based on the support vector machine recursive feature elimination (SVM-RFE) analysis, the model containing 10 genes ( TNS4, IRF2, BSG, GZMM, ARRB2, COX15, RALY, TP53, RPS6KA3 , and SYNPO ) with good performance in identifying people with different BMD levels was constructed. Among them, the roles of RALY and SYNPO in the osteogenic differentiation of hBMSCs were verified experimentally. Overall, this study provides a strategy to explore the biomarkers for osteoporosis through analysis of consensus modules.

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Section: Discussionmentioning

confidence: 99%

Consensus gene modules related to levels of bone mineral density (BMD) among smokers and nonsmokers

Lin

Pan

2021

Bioengineered

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“…Yang et al used DT to identify high-risk SNPs for Osteoporosis by constructing PPI networks of all human genes. [81] Taherzadeh et al established a ML method called SPRINT-Str based on RF and used structural information for predicting protein-peptide binding residues. [82] Hou et al used RF to evaluate the importance of various features.…”

Section: Applicationsmentioning

confidence: 99%

Machine Learning Advances in Predicting Peptide/Protein‐Protein Interactions Based on Sequence Information for Lead Peptides Discovery

Zheng

et al. 2023

Advanced Biology

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Peptides have shown increasing advantages and significant clinical value in drug discovery and development. With the development of high‐throughput technologies and artificial intelligence (AI), machine learning (ML) methods for discovering new lead peptides have been expanded and incorporated into rational drug design. Predictions of peptide–protein interactions (PepPIs) and protein–protein interactions (PPIs) are both opportunities and challenges in computational biology, which will help to better understand the mechanisms of disease and provide the impetus for the discovery of lead peptides. This paper comprehensively reviews computational models for PepPI and PPI predictions. It begins with an introduction of various databases of peptide ligands and target proteins. Then it discusses data formats and feature representations for proteins and peptides. Furthermore, classical ML methods and emerging deep learning (DL) methods that can be used to train prediction models of PepPI and PPI are classified into four categories, and their advantages and disadvantages are analyzed. To assess the relative performance of different models, different validation protocols and evaluation indexes are discussed. The goal of this review is to help researchers quickly get started to develop computational frameworks using these integrated resources and eventually promote the discovery of lead peptides.

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Classify and identify the risky loci of type 2 diabetes with computational method

Yang

Zhang

Jiang

et al. 2017

2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Identifying the Risky SNP of Osteoporosis with ID3-PEP Decision Tree Algorithm

Cited by 3 publications

References 21 publications

Consensus gene modules related to levels of bone mineral density (BMD) among smokers and nonsmokers

Consensus gene modules related to levels of bone mineral density (BMD) among smokers and nonsmokers

Machine Learning Advances in Predicting Peptide/Protein‐Protein Interactions Based on Sequence Information for Lead Peptides Discovery

Classify and identify the risky loci of type 2 diabetes with computational method

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