{"title":"Prediction of chromatin looping using deep hybrid learning (DHL)","authors":"","doi":"10.15302/j-qb-022-0315","DOIUrl":"https://doi.org/10.15302/j-qb-022-0315","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67351567","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":"Exploration on learning molecular docking with deep learning models","authors":"","doi":"10.15302/j-qb-022-0321","DOIUrl":"https://doi.org/10.15302/j-qb-022-0321","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67351747","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: Pooled CRISPR screen is a promising tool in drug targets or essential genes identification with the utilization of three different systems including CRISPR knockout (CRISPRko), CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa). Aside from continuous improvements in technology, more and more bioinformatics methods have been developed to analyze the data obtained by CRISPR screens which facilitate better understanding of physiological effects.
Results: Here, we provide an overview on the application of CRISPR screens and bioinformatics approaches to analyzing different types of CRISPR screen data. We also discuss mechanisms and underlying challenges for the analysis of dropout screens, sorting-based screens and single-cell screens.
Conclusion: Different analysis approaches should be chosen based on the design of screens. This review will help community to better design novel algorithms and provide suggestions for wet-lab researchers to choose from different analysis methods.
{"title":"Bioinformatics approaches to analyzing CRISPR screen data: from dropout screens to single-cell CRISPR screens.","authors":"Yueshan Zhao, Min Zhang, Da Yang","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Background: </strong>Pooled CRISPR screen is a promising tool in drug targets or essential genes identification with the utilization of three different systems including CRISPR knockout (CRISPRko), CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa). Aside from continuous improvements in technology, more and more bioinformatics methods have been developed to analyze the data obtained by CRISPR screens which facilitate better understanding of physiological effects.</p><p><strong>Results: </strong>Here, we provide an overview on the application of CRISPR screens and bioinformatics approaches to analyzing different types of CRISPR screen data. We also discuss mechanisms and underlying challenges for the analysis of dropout screens, sorting-based screens and single-cell screens.</p><p><strong>Conclusion: </strong>Different analysis approaches should be chosen based on the design of screens. This review will help community to better design novel algorithms and provide suggestions for wet-lab researchers to choose from different analysis methods.</p>","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10019185/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9163080","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}
The existence of doublets in single-cell RNA sequencing (scRNA-seq) data poses a great challenge in downstream data analysis. Computational doublet-detection methods have been developed to remove doublets from scRNA-seq data. Yet, the default hyperparameter settings of those methods may not provide optimal performance. Here, we propose a strategy to tune hyperparameters for a cutting-edge doublet-detection method. We utilize a full factorial design to explore the relationship between hyperparameters and detection accuracy on 16 real scRNA-seq datasets. The optimal hyperparameters are obtained by a response surface model and convex optimization. We show that the optimal hyperparameters provide top performance across scRNA-seq datasets under various biological conditions. Our tuning strategy can be applied to other computational doublet-detection methods. It also offers insights into hyperparameter tuning for broader computational methods in scRNA-seq data analysis.
{"title":"Tuning hyperparameters of doublet-detection methods for single-cell RNA sequencing data","authors":"N. Xi, Angelos Vasilopoulos","doi":"10.15302/j-qb-022-0324","DOIUrl":"https://doi.org/10.15302/j-qb-022-0324","url":null,"abstract":"The existence of doublets in single-cell RNA sequencing (scRNA-seq) data poses a great challenge in downstream data analysis. Computational doublet-detection methods have been developed to remove doublets from scRNA-seq data. Yet, the default hyperparameter settings of those methods may not provide optimal performance. Here, we propose a strategy to tune hyperparameters for a cutting-edge doublet-detection method. We utilize a full factorial design to explore the relationship between hyperparameters and detection accuracy on 16 real scRNA-seq datasets. The optimal hyperparameters are obtained by a response surface model and convex optimization. We show that the optimal hyperparameters provide top performance across scRNA-seq datasets under various biological conditions. Our tuning strategy can be applied to other computational doublet-detection methods. It also offers insights into hyperparameter tuning for broader computational methods in scRNA-seq data analysis.","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46420397","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 : 2022-01-01DOI: 10.1007/978-981-16-5018-5_4
A. Kimura
{"title":"Implementing Toy Models in Microsoft Excel","authors":"A. Kimura","doi":"10.1007/978-981-16-5018-5_4","DOIUrl":"https://doi.org/10.1007/978-981-16-5018-5_4","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"51118971","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 : 2022-01-01DOI: 10.1007/978-981-16-5018-5_9
A. Kimura
{"title":"Self-Organization of the Cell","authors":"A. Kimura","doi":"10.1007/978-981-16-5018-5_9","DOIUrl":"https://doi.org/10.1007/978-981-16-5018-5_9","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"51119544","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 : 2022-01-01DOI: 10.1007/978-981-16-5018-5_5
A. Kimura
{"title":"Implementing Toy Models in Python","authors":"A. Kimura","doi":"10.1007/978-981-16-5018-5_5","DOIUrl":"https://doi.org/10.1007/978-981-16-5018-5_5","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"51119040","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}
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