Pub Date : 2022-10-01DOI: 10.1016/j.jocs.2022.101879
B. Kumari, P. S. Dholaniya
{"title":"Parkinson's disease gene prioritising using an efficient and biologically appropriate network-based consensus strategy","authors":"B. Kumari, P. S. Dholaniya","doi":"10.1016/j.jocs.2022.101879","DOIUrl":"https://doi.org/10.1016/j.jocs.2022.101879","url":null,"abstract":"","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"9 1","pages":"101879"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78922186","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}
Pub Date : 2022-10-01DOI: 10.1016/j.jocs.2022.101887
Matous Cejnek, J. Vrba
{"title":"Padasip: An open-source Python toolbox for adaptive filtering","authors":"Matous Cejnek, J. Vrba","doi":"10.1016/j.jocs.2022.101887","DOIUrl":"https://doi.org/10.1016/j.jocs.2022.101887","url":null,"abstract":"","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"15 1","pages":"101887"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81453687","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}
Correlation matrices contain a wide variety of spatio-temporal information about a dynamical system. Predicting correlation matrices from partial time series information of a few nodes characterizes the spatio-temporal dynamics of the entire underlying system. This information can help to predict the underlying network structure, e.g., inferring neuronal connections from spiking data, deducing causal dependencies between genes from expression data, and discovering long spatial range influences in climate variations. Traditional methods of predicting correlation matrices utilize time series data of all the nodes of the underlying networks. Here, we use a supervised machine learning technique to predict the correlation matrix of entire systems from finite time series information of a few randomly selected nodes. The accuracy of the prediction validates that only a limited time series of a subset of the entire system is enough to make good correlation matrix predictions. Furthermore, using an unsupervised learning algorithm, we furnish insights into the success of the predictions from our model. Finally, we employ the machine learning model developed here to real-world data sets.
{"title":"Estimation of Correlation Matrices from Limited time series Data using Machine Learning","authors":"Nikhil Easaw, Woo Soek, Prashant Singh Lohiya, S. Jalan, Priodyuti Pradhan","doi":"10.48550/arXiv.2209.01198","DOIUrl":"https://doi.org/10.48550/arXiv.2209.01198","url":null,"abstract":"Correlation matrices contain a wide variety of spatio-temporal information about a dynamical system. Predicting correlation matrices from partial time series information of a few nodes characterizes the spatio-temporal dynamics of the entire underlying system. This information can help to predict the underlying network structure, e.g., inferring neuronal connections from spiking data, deducing causal dependencies between genes from expression data, and discovering long spatial range influences in climate variations. Traditional methods of predicting correlation matrices utilize time series data of all the nodes of the underlying networks. Here, we use a supervised machine learning technique to predict the correlation matrix of entire systems from finite time series information of a few randomly selected nodes. The accuracy of the prediction validates that only a limited time series of a subset of the entire system is enough to make good correlation matrix predictions. Furthermore, using an unsupervised learning algorithm, we furnish insights into the success of the predictions from our model. Finally, we employ the machine learning model developed here to real-world data sets.","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"41 1","pages":"102053"},"PeriodicalIF":0.0,"publicationDate":"2022-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77158547","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}
Pub Date : 2022-07-07DOI: 10.1016/j.jocs.2022.101876
K. Fossum, S. Alyaev, J. Tveranger, A. Elsheikh
{"title":"Verification of a real-time ensemble-based method for updating earth model based on GAN","authors":"K. Fossum, S. Alyaev, J. Tveranger, A. Elsheikh","doi":"10.1016/j.jocs.2022.101876","DOIUrl":"https://doi.org/10.1016/j.jocs.2022.101876","url":null,"abstract":"","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"3 4 1","pages":"101876"},"PeriodicalIF":0.0,"publicationDate":"2022-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79713424","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}
Pub Date : 2022-05-31DOI: 10.48550/arXiv.2206.00135
Kathryn Dover, Zixuan Cang, A. Ma, Qing Nie, R. Vershynin
High-dimensional multimodal data arises in many scientific fields. The integration of multimodal data becomes challenging when there is no known correspondence between the samples and the features of different datasets. To tackle this challenge, we introduce AVIDA, a framework for simultaneously performing data alignment and dimension reduction. In the numerical experiments, Gromov-Wasserstein optimal transport and t-distributed stochastic neighbor embedding are used as the alignment and dimension reduction modules respectively. We show that AVIDA correctly aligns high-dimensional datasets without common features with four synthesized datasets and two real multimodal single-cell datasets. Compared to several existing methods, we demonstrate that AVIDA better preserves structures of individual datasets, especially distinct local structures in the joint low-dimensional visualization, while achieving comparable alignment performance. Such a property is important in multimodal single-cell data analysis as some biological processes are uniquely captured by one of the datasets. In general applications, other methods can be used for the alignment and dimension reduction modules.
{"title":"AVIDA: Alternating method for Visualizing and Integrating Data","authors":"Kathryn Dover, Zixuan Cang, A. Ma, Qing Nie, R. Vershynin","doi":"10.48550/arXiv.2206.00135","DOIUrl":"https://doi.org/10.48550/arXiv.2206.00135","url":null,"abstract":"High-dimensional multimodal data arises in many scientific fields. The integration of multimodal data becomes challenging when there is no known correspondence between the samples and the features of different datasets. To tackle this challenge, we introduce AVIDA, a framework for simultaneously performing data alignment and dimension reduction. In the numerical experiments, Gromov-Wasserstein optimal transport and t-distributed stochastic neighbor embedding are used as the alignment and dimension reduction modules respectively. We show that AVIDA correctly aligns high-dimensional datasets without common features with four synthesized datasets and two real multimodal single-cell datasets. Compared to several existing methods, we demonstrate that AVIDA better preserves structures of individual datasets, especially distinct local structures in the joint low-dimensional visualization, while achieving comparable alignment performance. Such a property is important in multimodal single-cell data analysis as some biological processes are uniquely captured by one of the datasets. In general applications, other methods can be used for the alignment and dimension reduction modules.","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"16 1","pages":"101998"},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82291790","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}
Pub Date : 2022-05-13DOI: 10.1016/j.jocs.2022.101884
Marius Kurz, Philipp Offenhauser, Dominic Viola, Oleksandr Shcherbakov, Michael M. Resch, A. Beck
{"title":"Deep Reinforcement Learning for Computational Fluid Dynamics on HPC Systems","authors":"Marius Kurz, Philipp Offenhauser, Dominic Viola, Oleksandr Shcherbakov, Michael M. Resch, A. Beck","doi":"10.1016/j.jocs.2022.101884","DOIUrl":"https://doi.org/10.1016/j.jocs.2022.101884","url":null,"abstract":"","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"32 1","pages":"101884"},"PeriodicalIF":0.0,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90496802","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}
Pub Date : 2022-04-01DOI: 10.1016/j.jocs.2022.101576
R. Kozik, Sebastian Kula, M. Choraś, Michael Wozniak
{"title":"Technical solution to counter potential crime: Text analysis to detect fake news and disinformation","authors":"R. Kozik, Sebastian Kula, M. Choraś, Michael Wozniak","doi":"10.1016/j.jocs.2022.101576","DOIUrl":"https://doi.org/10.1016/j.jocs.2022.101576","url":null,"abstract":"","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"20 1","pages":"101576"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76707624","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}
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