2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB)最新文献

Development of Intra-Aortic Balloon Pump with Vascular Stent and Vitro Simulation Verification 血管支架主动脉内球囊泵的研制及体外仿真验证

2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2021-05-25 DOI: 10.1109/ICBCB52223.2021.9459233

Yao Xie, Dong Yang, Honglong Yu, Kun Wang, Qilian Xie

引用次数: 1

Study on Recent Developments from Aquilaria Sinensis and Future Perspectives 中国沉香研究进展及展望

2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2021-05-25 DOI: 10.1109/ICBCB52223.2021.9459232

A. H. Kaleri, Xi-Qiang Song, Hao-fu Dai, Anum Mehmood, U. Bhatti, M. M. Nizamani, A. Kaleri

{"title":"Study on Recent Developments from Aquilaria Sinensis and Future Perspectives","authors":"A. H. Kaleri, Xi-Qiang Song, Hao-fu Dai, Anum Mehmood, U. Bhatti, M. M. Nizamani, A. Kaleri","doi":"10.1109/ICBCB52223.2021.9459232","DOIUrl":"https://doi.org/10.1109/ICBCB52223.2021.9459232","url":null,"abstract":"Latest development in agarwood creates better use in different fields of life sciences. With the development of social economy, China's agarwood industry has developed rapidly in recent years and has gradually become a characteristic industry of local pillar industries. Due to the particularity of agarwood and its precious rarity, it is the most treasured treasure of collection. The agarwood collection is a fashion and the best way to preserve value, both ancient and modern. New advancement was discussed in advance use of chemical constituents of Aquilaria siwasis mainly include flavonoids, benzophenones, lignans, phenyl propanoids, terpenoids, alkaloids, steroids, phenolic compounds. Some of them have anti-tumor, antibacterial, anti-inflammatory, analgesic, and laxative activities antibacterial constituents isolated from Aquilaria sinensis were reviewed, the latest uses of agarwood for religious as well as medical treatment and the biological activities of some compounds were also introduced. These would provide scientific basis for development and utilization of A.","PeriodicalId":178168,"journal":{"name":"2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121696754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Clustering Single-Cell RNA Sequencing Data by Deep Learning Algorithm 基于深度学习算法的单细胞RNA测序数据聚类

2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2021-05-25 DOI: 10.1109/ICBCB52223.2021.9459219

Litai Bai, Yuan Zhu, Ming Yi

引用次数: 1

In-Silico Modelling of Phenotypic Switching in Tumours: Investigating Potentials for Non-invasive Therapies 肿瘤表型转换的计算机模拟:研究非侵入性治疗的潜力

2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2021-05-25 DOI: 10.1109/ICBCB52223.2021.9459214

Dario Panada, R. King, B. Parsia

{"title":"In-Silico Modelling of Phenotypic Switching in Tumours: Investigating Potentials for Non-invasive Therapies","authors":"Dario Panada, R. King, B. Parsia","doi":"10.1109/ICBCB52223.2021.9459214","DOIUrl":"https://doi.org/10.1109/ICBCB52223.2021.9459214","url":null,"abstract":"We developed an in-silico model of cancer growth to investigate the extent to which metabolic switching occurs in tumour masses. Cancer therapies based on glycoconjugation, the linking of a drug to glucose or another sugar, allow improved selectivity and targeting, thus reducing harmful side effects. This mechanism exploits the over-expression of glucose membrane transporters, a phenotypic alteration in cancer cells included in an array of metabolic alterations known as the Warburg effect. However, the extent to which tumour masses adopt the Warburg phenotype is unclear, potentially limiting the efficacy of therapies based on glycoconjugation. We simulated multiple “what-if” scenarios, each modelling increasing proportions of tumour populations that adopted the Warburg phenotype, and compared the results to the expected growth curves derived from laboratory studies. Our results suggest that the Warburg phenotype is prevalent in tumours, with the population of cancer cells adopting this phenotype significantly outnumbering that of cells that do not.","PeriodicalId":178168,"journal":{"name":"2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114558633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analyzing Phenotype Microarray Data for Escherichia coli Using an Infinite Relational Model 利用无限关联模型分析大肠杆菌表型微阵列数据

2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2021-05-25 DOI: 10.1109/ICBCB52223.2021.9459236

Y. Tohsato, T. Taniguchi, H. Mori, M. Ito

引用次数: 0

Drug-Target Interaction Identification via Dual-Graph Regularized Robust PCA in Heterogeneous Networks 异构网络中基于双图正则鲁棒PCA的药物-靶标相互作用识别

2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2021-05-25 DOI: 10.1109/ICBCB52223.2021.9459207

Sun Dengdi, Ni Shouhang, Ding Zhuanlian, Bin Luo

{"title":"Drug-Target Interaction Identification via Dual-Graph Regularized Robust PCA in Heterogeneous Networks","authors":"Sun Dengdi, Ni Shouhang, Ding Zhuanlian, Bin Luo","doi":"10.1109/ICBCB52223.2021.9459207","DOIUrl":"https://doi.org/10.1109/ICBCB52223.2021.9459207","url":null,"abstract":"Drug-target interaction identification is an essential step of drug discovering and adverse effect prediction. In real bio-environment, the connections among drugs and targets, as well as themselves construct a complex heterogeneous network, and profoundly affect the predictive performance of drug-target interactions. However, the current methods usually focus on the drug-target interactions alone, which may be very sparse and with numerous noises, may not produce satisfactory prediction results. In this paper, we propose a novel approach, dual-graph regularized robust PCA in heterogeneous network, for drug-target interaction prediction task. In particular, we aim at decompose the bipartite graph of drug-target interactions into two low-rank matrices, which represent the latent representations of drugs and targets respectively, and smooth the drug-drug and target-target graphs simultaneously. Moreover, an improved robust PCA model is used to suppress the widespread noisy connections in the decomposition stage. For the optimization, we design an efficient algorithm to solve few subproblems with close-form solution. Finally the extensive experiments on real world drug-target heterogeneous networks are presented to show the effectiveness of the proposed methods.","PeriodicalId":178168,"journal":{"name":"2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124261602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Codon Effect on the Entire Genome Based upon Genome-Wide Recoded Escherichia coli 基于全基因组重编码大肠杆菌的全基因组密码子效应

2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2021-05-25 DOI: 10.1109/ICBCB52223.2021.9459235

Yi Huang

引用次数: 0

Using Machine Learning Models to Study Medication Adherence in Hypertensive Patients Based on National Stroke Screening Data 基于国家脑卒中筛查数据，使用机器学习模型研究高血压患者的药物依从性

2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2021-05-25 DOI: 10.1109/ICBCB52223.2021.9459205

Xuemeng Li, Haifeng Xu, Mei Li, Dongsheng Zhao

{"title":"Using Machine Learning Models to Study Medication Adherence in Hypertensive Patients Based on National Stroke Screening Data","authors":"Xuemeng Li, Haifeng Xu, Mei Li, Dongsheng Zhao","doi":"10.1109/ICBCB52223.2021.9459205","DOIUrl":"https://doi.org/10.1109/ICBCB52223.2021.9459205","url":null,"abstract":"Stroke, with high incidence, prevalence and mortality, has brought a heavy burden to families as well as society in China nowadays. In 2009, the China national stroke screening and intervention program was launched by the Ministry of Health of China. In the national program, risk factors of stroke are screened and people aged over 40 with high-risk of stroke will be followed-up. From the experience, it is found that hypertension is an important risk factor of stroke. Improving the adherence of hypertension medication can effectively control blood pressure and further decrease stroke incidence. In this study, firstly, we employ oversampling and undersampling method to process the imbalanced dataset. Then, we build four machine learning models, namely logistic regression model, decision tree model, neural network model and random forest model, to classify medication adherence in hypertensive patients. We use the recall and precision to evaluate these models, and considering these two criteria, the model based on decision tree achieves best performance. The models constructed in this paper can be used to identify people with low adherence of antihypertensive drugs in the stroke screening program and improve the efficiency of the follow-up interventions, which can effectively control blood pressure and reduce the possibility of stroke.","PeriodicalId":178168,"journal":{"name":"2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB)","volume":"161 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129071051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

HilbertEPIs: Enhancer-Promoter Interactions Prediction with Hilbert Curve and CNN Model hilbertepi:基于Hilbert曲线和CNN模型的增强子-启动子相互作用预测

2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2021-05-25 DOI: 10.1109/ICBCB52223.2021.9459226

Yujia Hu, Ruichen Peng, Chunlin Long, Min Zhu

{"title":"HilbertEPIs: Enhancer-Promoter Interactions Prediction with Hilbert Curve and CNN Model","authors":"Yujia Hu, Ruichen Peng, Chunlin Long, Min Zhu","doi":"10.1109/ICBCB52223.2021.9459226","DOIUrl":"https://doi.org/10.1109/ICBCB52223.2021.9459226","url":null,"abstract":"Enhancers are DNA cis-regulatory sequences that control the transcriptional activities of many gene regulation elements. Due to enhancers always get close to promoters by complex spatial structures, accurately identifying Enhancer-Promoter Interactions will help us understand mechanisms of gene regulations, recognize specific genes associated with diseases, as well as offer help with disease diagnosis and treatment. In this article, we develop a model named HilbertEPIs to predict the interactions between enhancers and promoters. We first transfer 1D sequence into 3D picture representations with Hilbert Curve to preserve the spatial structure of this sequence. Then extract features by CNN model. Finally, using two strategies to deal with unbalanced data. Experimental results have proved that HilbertEPIs has perfect performance compared to existed methods, as well as to show that Hilbert Curve is qualified to represent spatial relationships among different genetic regulatory elements. We train model in two ways and learn from six cell lines, finally achieve the data in 0.908~0.983 of AUROC, 0.926~0.988 of AUPR.","PeriodicalId":178168,"journal":{"name":"2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132896164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

ITGAX: A Potential Biomarker of Acute Myeloid Leukemia (AML) through Bioinformatic Analysis 通过生物信息学分析ITGAX:急性髓性白血病(AML)的潜在生物标志物

2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB) Pub Date : 2021-05-25 DOI: 10.1109/ICBCB52223.2021.9459204

Heng Yang, Tian Mao

{"title":"ITGAX: A Potential Biomarker of Acute Myeloid Leukemia (AML) through Bioinformatic Analysis","authors":"Heng Yang, Tian Mao","doi":"10.1109/ICBCB52223.2021.9459204","DOIUrl":"https://doi.org/10.1109/ICBCB52223.2021.9459204","url":null,"abstract":"Acute Myeloid Leukemia (AML) constitutes nearly eighty percent of total adult leukemias, which also is almost all common origin cause of leukemia death. Consequently, Understanding the molecular mechanisms of AML and identifying potential biomarkers are significant for clinical treatment. To identify the differentially expressed genes (DEGs), microarray datasets GSE114868, GSE67936 and GSE65409 were downloaded from Gene Expression Omnibus (GEO) database. Function enrichment analysis was performed and protein-protein interaction network (PPI) was constructed.62 DEGs were identified, made up of 15 downregulated genes and 47 upregulated genes. The module analysis was performed using STRING and Cytoscape. The enriched functions and pathways of the DEGs include leukocyte degranulation, cytokine production, Th1 and Th2 cell differentiation, chromatin remodeling at centromere, somatic diversification of immune receptors and Renin-angiotensin system. Ten hub genes were identified through degrees calculated by CytoHubba and KEGG analysis indicated that hub genes were particularly enriched in Th1 and Th2 cell differentiation, Natural killer cell mediated cytotoxicity and Cytokine-cytokine receptor interaction. Supplementary analysis showed that only ITGAX gene had a big potential as higher expression and considerably worse survival in AML compared with normal. In a word, our study drew a conclusion that ITGAX could be a potential prognostic factor and beneficial target for AML therapy. And we should do further experimentations to verify the result.","PeriodicalId":178168,"journal":{"name":"2021 IEEE 9th International Conference on Bioinformatics and Computational Biology (ICBCB)","volume":"403 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133498808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0