Classification of COVID-19 severity using scRNA-Seq

Last Updated On: 
Sun, 10/03/2021 - 21:09
Citation Author(s): 
Mario Flores

This study presented six datasets for DNA/RNA sequence alignment for one of the most common alignment algorithms, namely, the Needleman–Wunsch (NW) algorithm. This research proposed a fast and parallel implementation of the NW algorithm by using machine learning techniques. This study is an extension and improved version of our previous work . The current implementation achieves 99.7% accuracy using a multilayer perceptron with ADAM optimizer and up to 2912 giga cell updates per second on two real DNA sequences with a of length 4.1 M nucleotides.

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The relative binding affiniy values of all 8000 tripeptide sequenses are shown here. The values are standardized by isoforms so that the mean is zero and the variance is one. The sequences are ordered by the results of hierarchical cluster analysis. 

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The group of tripeptides in N-terminal sublibrary that found by cluster analysis in the study is highlighted by black borders. And the sequences that reported to bind to 14-3-3s previously are marked by red color. The marked sequences are:RST(c-Raf-1, A-Raf), RDS(Cdc25a), RPS(Cdc25b), RAA(PKC-ε), RAK(PCTAIRE-2), RSH(mT), RHA(Tyr hydroxylase), RHS(Tryp hydroxylase), RSK(A20), RIH(Cdc25a), RFQ(Cdc25b), CVR(PKCγ), PTR(IRS-1), SYT(K8 keratin), LYR(Clathrin assembly prot).

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Large p small n problem is a challenging problem in big data analytics. There are no de facto standard methods available to it. In this study, we propose a tensor decomposition (TD) based unsupervised feature extraction (FE) formalism applied to multiomics datasets, where the number of features is more than 100000 while the number of instances is as small as about 100.

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Enrichment analysis performed by Enrichr toward genes associated with coronavirus infeciton

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Genes are selected by TD based unsupervised FE and are uploaded to Enrichr

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The dataset is used to detect essential protein in uncertain PPI network.

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Computational modelling of metabolic processes has proven to be a useful approach to formulate our knowledge and improve our understanding of core biochemical systems that are crucial to maintain cellular functions. Recently, it has become evident that metabolism is not only responsible for generating the required energy and controlling the abundance of metabolites within a cell, but also has an important role in and influence on cellular fate specification.

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Data Science is all about the processes and methods to access and analyze data to gain insights for informed decision making. To promote the awareness and analytic technology of Big Data, IEEE EMBS and the IEEE Big Data Initiative are organizing a Data Analytics Competition. The competition will be held during the International Conference on Biomedical and Health Informatics (IEEE BHI2017), 16-19 February 2017 in Orlando, Florida, and is open to all participants of the conference.

Last Updated On: 
Tue, 08/08/2017 - 10:52
Citation Author(s): 
United States Patent and Trademark Office