{"title":"Recent progress in research and application of engineered implanted cells for biomedical applications","authors":"","doi":"10.15302/j-qb-021-0253","DOIUrl":"https://doi.org/10.15302/j-qb-021-0253","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"1 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67350999","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}
Background : Polygenic risk score (PRS) derived from summary statistics of genome-wide association studies (GWAS) is a useful tool to infer an individual ’ s genetic risk for health outcomes and has gained increasing popularity in human genetics research. PRS in its simplest form enjoys both computational ef fi ciency and easy accessibility, yet the predictive performance of PRS remains moderate for diseases and traits. Results : We provide an overview of recent advances in statistical methods to improve PRS ’ s performance by incorporating information from linkage disequilibrium, functional annotation, and pleiotropy. We also introduce model validation methods that fi ne-tune PRS using GWAS summary statistics. Conclusion : In this review, we showcase methodological advances and current limitations of PRS, and discuss several emerging issues in risk prediction research. Author summary: The prosperity of powerful genome-wide association studies (GWASs) has facilitated rapid development of polygenic risk score (PRS). Many post-GWAS PRS methods have been introduced to directly address the mediocre prediction accuracy of traditional PRS built upon marginal estimates from GWAS. This review fi rst summarizes PRS methods inspired by different biological concepts including LD, functional annotation, and pleiotropy to better quantify SNP effects. Then we introduce recent PRS frameworks that enable model optimization using summary statistics. Finally, we point out current pitfalls of risk prediction research. We expect emerging methods that address current challenges in the near future.
{"title":"Polygenic risk scores: effect estimation and model optimization","authors":"Zijie Zhao, Jie Song, Tuo Wang, Q. Lu","doi":"10.15302/j-qb-021-0238","DOIUrl":"https://doi.org/10.15302/j-qb-021-0238","url":null,"abstract":"Background : Polygenic risk score (PRS) derived from summary statistics of genome-wide association studies (GWAS) is a useful tool to infer an individual ’ s genetic risk for health outcomes and has gained increasing popularity in human genetics research. PRS in its simplest form enjoys both computational ef fi ciency and easy accessibility, yet the predictive performance of PRS remains moderate for diseases and traits. Results : We provide an overview of recent advances in statistical methods to improve PRS ’ s performance by incorporating information from linkage disequilibrium, functional annotation, and pleiotropy. We also introduce model validation methods that fi ne-tune PRS using GWAS summary statistics. Conclusion : In this review, we showcase methodological advances and current limitations of PRS, and discuss several emerging issues in risk prediction research. Author summary: The prosperity of powerful genome-wide association studies (GWASs) has facilitated rapid development of polygenic risk score (PRS). Many post-GWAS PRS methods have been introduced to directly address the mediocre prediction accuracy of traditional PRS built upon marginal estimates from GWAS. This review fi rst summarizes PRS methods inspired by different biological concepts including LD, functional annotation, and pleiotropy to better quantify SNP effects. Then we introduce recent PRS frameworks that enable model optimization using summary statistics. Finally, we point out current pitfalls of risk prediction research. We expect emerging methods that address current challenges in the near future.","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"10 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67350979","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}
{"title":"Interpretable prediction of drug-cell line response by triple matrix factorization","authors":"","doi":"10.15302/j-qb-021-0259","DOIUrl":"https://doi.org/10.15302/j-qb-021-0259","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"1 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67351119","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}
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.
{"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}
Pub Date : 2020-12-24Epub Date: 2020-12-07DOI: 10.1007/s40484-020-0226-1
Yawei Li, Yuan Luo
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.
{"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":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","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}
Pub Date : 2020-08-06DOI: 10.1007/s40484-020-0210-9
Xu Liao, Xiaoran Chai, Xingjie Shi, Lin S. Chen, Jin Liu
{"title":"The statistical practice of the GTEx Project: from single to multiple tissues","authors":"Xu Liao, Xiaoran Chai, Xingjie Shi, Lin S. Chen, Jin Liu","doi":"10.1007/s40484-020-0210-9","DOIUrl":"https://doi.org/10.1007/s40484-020-0210-9","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"1 1","pages":"1 - 17"},"PeriodicalIF":3.1,"publicationDate":"2020-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40484-020-0210-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42372291","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}
Pub Date : 2020-07-31DOI: 10.1007/s40484-020-0212-7
Xue Jiang, Mohammad Asad, Lin Li, Zhanpeng Sun, Jean-Sébastien Milanese, Bo Liao, Edwin Wang
{"title":"Germline genomes have a dominant-heritable contribution to cancer immune evasion and immunotherapy response","authors":"Xue Jiang, Mohammad Asad, Lin Li, Zhanpeng Sun, Jean-Sébastien Milanese, Bo Liao, Edwin Wang","doi":"10.1007/s40484-020-0212-7","DOIUrl":"https://doi.org/10.1007/s40484-020-0212-7","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"1 1","pages":"1 - 12"},"PeriodicalIF":3.1,"publicationDate":"2020-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40484-020-0212-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48750306","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}
Pub Date : 2020-07-23DOI: 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
{"title":"Monitoring and mathematical modeling of mitochondrial ATP in myotubes at single-cell level reveals two distinct population with different kinetics","authors":"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","doi":"10.1007/s40484-020-0211-8","DOIUrl":"https://doi.org/10.1007/s40484-020-0211-8","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"1 1","pages":"1 - 10"},"PeriodicalIF":3.1,"publicationDate":"2020-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40484-020-0211-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49412760","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}
Pub Date : 2020-07-18DOI: 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
{"title":"Direct-to-consumer genetic testing in China and its role in GWAS discovery and replication","authors":"Kang Kang, Xue-Long Sun, Lizhong Wang, Xiaotian Yao, Senwei Tang, Junjie Deng, Xiaoli Wu, Can Yang, Gang Chen","doi":"10.1007/s40484-020-0209-2","DOIUrl":"https://doi.org/10.1007/s40484-020-0209-2","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"1 1","pages":"1 - 15"},"PeriodicalIF":3.1,"publicationDate":"2020-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40484-020-0209-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49242355","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：