Quantitative Biology最新文献_第7页

Interpretable prediction of drug-cell line response by triple matrix factorization 三矩阵分解对药物细胞系反应的可解释性预测

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2021-01-01 DOI: 10.15302/j-qb-021-0259

引用次数: 1

A study of the COVID-19 epidemic in India using the SEIRD model 基于SEIRD模型的印度COVID-19疫情研究

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2021-01-01 DOI: 10.15302/j-qb-021-0260

R. Banerjee, S. Bhattacharjee, P. Varadwaj

{"title":"A study of the COVID-19 epidemic in India using the SEIRD model","authors":"R. Banerjee, S. Bhattacharjee, P. Varadwaj","doi":"10.15302/j-qb-021-0260","DOIUrl":"https://doi.org/10.15302/j-qb-021-0260","url":null,"abstract":"Background: The coronavirus pandemic (COVID-19) is causing a havoc globally, exacerbated by the newly discovered SARS-CoV-2 virus. Due to its high population density, India is one of the most badly effected countries from the first wave of COVID-19. Therefore, it is extremely necessary to accurately predict the state-wise and overall dynamics of COVID-19 to get the effective and efficient organization of resources across India. Methods: In this study, the dynamics of COVID-19 in India and several of its selected states with different demographic structures were analyzed using the SEIRD epidemiological model. The basic reproductive ratio R0 was systemically estimated to predict the dynamics of the temporal progression of COVID-19 in India and eight of its states, Andhra Pradesh, Chhattisgarh, Delhi, Gujarat, Madhya Pradesh, Maharashtra, Tamil Nadu, and Uttar Pradesh. Results: For India, the SEIRD model calculations show that the peak of infection is expected to appear around the middle of October, 2020. Furthermore, we compared the model scenario to a Gaussian fit of the daily infected cases and obtained similar results. The early imposition of a nation-wide lockdown has reduced the number of infected cases but delayed the appearance of the infection peak significantly. Conclusion: After comparing our calculations using India's data to the real life dynamics observed in Italy and Russia, we can conclude that the SEIRD model can predict the dynamics of COVID-19 with sufficient accuracy.","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"31 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67351195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Performance-weighted-voting model: An ensemble machine learning method for cancer type classification using whole-exome sequencing mutation. 性能加权投票模型:利用全外显子组测序突变进行癌症类型分类的集成机器学习方法。

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2020-12-24 Epub Date: 2020-12-07 DOI: 10.1007/s40484-020-0226-1

Yawei Li, Yuan Luo

{"title":"Performance-weighted-voting model: An ensemble machine learning method for cancer type classification using whole-exome sequencing mutation.","authors":"Yawei Li, Yuan Luo","doi":"10.1007/s40484-020-0226-1","DOIUrl":"https://doi.org/10.1007/s40484-020-0226-1","url":null,"abstract":"Background: With improvements in next-generation DNA sequencing technology, lower cost is needed to collect genetic data. More machine learning techniques can be used to help with cancer analysis and diagnosis.Methods: We developed an ensemble machine learning system named performance-weighted-voting model for cancer type classification in 6,249 samples across 14 cancer types. Our ensemble system consists of five weak classifiers (logistic regression, SVM, random forest, XGBoost and neural networks). We first used cross-validation to get the predicted results for the five classifiers. The weights of the five weak classifiers can be obtained based on their predictive performance by solving linear regression functions. The final predicted probability of the performance-weighted-voting model for a cancer type can be determined by the summation of each classifier's weight multiplied by its predicted probability.Results: Using the somatic mutation count of each gene as the input feature, the overall accuracy of the performance-weighted-voting model reached 71.46%, which was significantly higher than the five weak classifiers and two other ensemble models: the hard-voting model and the soft-voting model. In addition, by analyzing the predictive pattern of the performance-weighted-voting model, we found that in most cancer types, higher tumor mutational burden can improve overall accuracy.Conclusion: This study has important clinical significance for identifying the origin of cancer, especially for those where the primary cannot be determined. In addition, our model presents a good strategy for using ensemble systems for cancer type classification.","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"8 4","pages":"347-358"},"PeriodicalIF":3.1,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40484-020-0226-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39266424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Erratum to: Identifying miRNA-disease association based on integrating miRNA topological similarity and functional similarity 勘误:基于整合miRNA拓扑相似性和功能相似性来识别miRNA与疾病的关联

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2020-09-01 DOI: 10.1007/s40484-020-0220-7

Qingfeng Chen, Zhao Zhe, Wei Lan, Ruchang Zhang, Zhiqiang Wang, Cheng Luo, Yi-Ping Phoebe Chen

引用次数: 0

The statistical practice of the GTEx Project: from single to multiple tissues GTEx项目的统计实践:从单一组织到多个组织

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2020-08-06 DOI: 10.1007/s40484-020-0210-9

Xu Liao, Xiaoran Chai, Xingjie Shi, Lin S. Chen, Jin Liu

引用次数: 1

Germline genomes have a dominant-heritable contribution to cancer immune evasion and immunotherapy response 生殖系基因组对癌症免疫逃避和免疫治疗反应具有显性遗传贡献

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2020-07-31 DOI: 10.1007/s40484-020-0212-7

Xue Jiang, Mohammad Asad, Lin Li, Zhanpeng Sun, Jean-Sébastien Milanese, Bo Liao, Edwin Wang

引用次数: 2

Monitoring and mathematical modeling of mitochondrial ATP in myotubes at single-cell level reveals two distinct population with different kinetics 在单细胞水平上对肌管中线粒体ATP的监测和数学建模揭示了具有不同动力学的两个不同群体

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2020-07-23 DOI: 10.1007/s40484-020-0211-8

Naoki Matsuda, Ken-ichi Hironaka, Masashi Fujii, Takumi Wada, Katsuyuki Kunida, Haruki Inoue, M. Eto, Daisuke Hoshino, Y. Furuichi, Y. Manabe, N. Fujii, H. Noji, H. Imamura, Shinya Kuroda

引用次数: 4

Direct-to-consumer genetic testing in China and its role in GWAS discovery and replication 中国直接面向消费者的基因检测及其在GWAS发现和复制中的作用

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2020-07-18 DOI: 10.1007/s40484-020-0209-2

Kang Kang, Xue-Long Sun, Lizhong Wang, Xiaotian Yao, Senwei Tang, Junjie Deng, Xiaoli Wu, Can Yang, Gang Chen

引用次数: 4

Confidence intervals for Markov chain transition probabilities based on next generation sequencing reads data. 基于下一代测序读取数据的马尔可夫链转移概率置信区间。

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2020-07-13 Epub Date: 2020-05-25 DOI: 10.1007/s40484-020-0200-y

Lin Wan, Xin Kang, Jie Ren, Fengzhu Sun

{"title":"Confidence intervals for Markov chain transition probabilities based on next generation sequencing reads data.","authors":"Lin Wan, Xin Kang, Jie Ren, Fengzhu Sun","doi":"10.1007/s40484-020-0200-y","DOIUrl":"https://doi.org/10.1007/s40484-020-0200-y","url":null,"abstract":"Background: Markov chains (MC) have been widely used to model molecular sequences. The estimations of MC transition matrix and confidence intervals of the transition probabilities from long sequence data have been intensively studied in the past decades. In next generation sequencing (NGS), a large amount of short reads are generated. These short reads can overlap and some regions of the genome may not be sequenced resulting in a new type of data. Based on NGS data, the transition probabilities of MC can be estimated by moment estimators. However, the classical asymptotic distribution theory for MC transition probability estimators based on long sequences is no longer valid.Methods: In this study, we present the asymptotic distributions of several statistics related to MC based on NGS data. We show that, after scaling by the effective coverage d defined in a previous study by the authors, these statistics based on NGS data approximate to the same distributions as the corresponding statistics for long sequences.Results: We apply the asymptotic properties of these statistics for finding the theoretical confidence regions for MC transition probabilities based on NGS short reads data. We validate our theoretical confidence intervals using both simulated data and real data sets, and compare the results with those by the parametric bootstrap method.Conclusions: We find that the asymptotic distributions of these statistics and the theoretical confidence intervals of transition probabilities based on NGS data given in this study are highly accurate, providing a powerful tool for NGS data analysis.","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"8 2","pages":"143-154"},"PeriodicalIF":3.1,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40484-020-0200-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39185013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

DNA sequencing using nanopores and kinetic proofreading 利用纳米孔进行DNA测序和动力学校对

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2020-07-02 DOI: 10.1007/s40484-020-0201-x

X. Ling

引用次数: 0