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      A novel method for multifactorial bio-chemical experiments design based on combinational design theory

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          Abstract

          Experimental design focuses on describing or explaining the multifactorial interactions that are hypothesized to reflect the variation. The design introduces conditions that may directly affect the variation, where particular conditions are purposely selected for observation. Combinatorial design theory deals with the existence, construction and properties of systems of finite sets whose arrangements satisfy generalized concepts of balance and/or symmetry. In this work, borrowing the concept of “balance” in combinatorial design theory, a novel method for multifactorial bio-chemical experiments design is proposed, where balanced templates in combinational design are used to select the conditions for observation. Balanced experimental data that covers all the influencing factors of experiments can be obtianed for further processing, such as training set for machine learning models. Finally, a software based on the proposed method is developed for designing experiments with covering influencing factors a certain number of times.

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          Response surface methodology

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            A comprehensive overview and evaluation of circular RNA detection tools

            Circular RNA (circRNA) is mainly generated by the splice donor of a downstream exon joining to an upstream splice acceptor, a phenomenon known as backsplicing. It has been reported that circRNA can function as microRNA (miRNA) sponges, transcriptional regulators, or potential biomarkers. The availability of massive non-polyadenylated transcriptomes data has facilitated the genome-wide identification of thousands of circRNAs. Several circRNA detection tools or pipelines have recently been developed, and it is essential to provide useful guidelines on these pipelines for users, including a comprehensive and unbiased comparison. Here, we provide an improved and easy-to-use circRNA read simulator that can produce mimicking backsplicing reads supporting circRNAs deposited in CircBase. Moreover, we compared the performance of 11 circRNA detection tools on both simulated and real datasets. We assessed their performance regarding metrics such as precision, sensitivity, F1 score, and Area under Curve. It is concluded that no single method dominated on all of these metrics. Among all of the state-of-the-art tools, CIRI, CIRCexplorer, and KNIFE, which achieved better balanced performance between their precision and sensitivity, compared favorably to the other methods.
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              A Secure and Dynamic Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data

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                Author and article information

                Contributors
                Role: Investigation
                Role: Software
                Role: Validation
                Role: Methodology
                Role: Investigation
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2017
                2 November 2017
                : 12
                : 11
                : e0186853
                Affiliations
                [1 ] College of Computer and Communication Engineering, China University of Petroleum, Qingdao 266580, Shandong, China
                [2 ] State-owned Asset and Laboratory Management Department, China University of Petroleum, Qingdao 266580, Shandong, China
                [3 ] Institute of Complexity Science, Qingdao University, Qingdao 266071, Shandong, China
                [4 ] Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak Campus, Kuching 93350, Malaysia
                Xiamen University, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-6067-626X
                Article
                PONE-D-17-27223
                10.1371/journal.pone.0186853
                5667848
                29095845
                8d9c1e2d-6b5c-403b-bdc7-5a855c1fcc23
                © 2017 Wang et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 20 July 2017
                : 9 October 2017
                Page count
                Figures: 3, Tables: 1, Pages: 10
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 61402187, 61502535, 61572522, 61572523, 61672033, 61672248
                Award Recipient :
                Funded by: China Postdoctoral Science Foundation funded project
                Award ID: 2016M592267
                Award Recipient :
                Funded by: PetroChina Innovation Foundation
                Award ID: 2016D-5007-0305
                Award Recipient :
                Funded by: Fundamental Research Funds for the Central Universities
                Award ID: R1607005A
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100007129, Natural Science Foundation of Shandong Province;
                Award ID: ZR2015FM022
                Award Recipient :
                Funded by: Key Research and Development Program of Shandong Province
                Award ID: 2017GGX10147
                Award Recipient :
                This work was supported by National Natural Science Foundation of China (61402187, 61502535, 61572522, 61572523, 61672033 and 61672248) to XW, China Postdoctoral Science Foundation funded project (2016M592267) to XW, PetroChina Innovation Foundation (2016D-5007-0305) to PZ, Fundamental Research Funds for the Central Universities (R1607005A) to BL, Natural Science Foundation of Shandong Province (ZR2015FM022) to XW, Key Research and Development Program of Shandong Province (2017GGX10147) to BS.
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