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- 저자 : Baichoo, Shakuntala (검색결과 2 건)
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Toward unrestricted use of public genomic data
Amann, Rudolf I.,Baichoo, Shakuntala,Blencowe, Benjamin J.,Bork, Peer,Borodovsky, Mark,Brooksbank, Cath,Chain, Patrick S. G.,Colwell, Rita R.,Daffonchio, Daniele G.,Danchin, Antoine,de Lorenzo, Victor American Association for the Advancement of Scienc 2019 Science Vol.363 No.6425
<P>Despite some notable progress in data sharing policies and practices, restrictions are still often placed on the open and unconditional use of various genomic data after they have received official approval for release to the public domain or to public databases. These restrictions, which often conflict with the terms and conditions of the funding bodies who supported the release of those data for the benefit of the scientific community and society, are perpetuated by the lack of clear guiding rules for data usage. Existing guidelines for data released to the public domain recognize but fail to resolve tensions between the importance of free and unconditional use of these data and the “right” of the data producers to the first publication. This self-contradiction has resulted in a loophole that allows different interpretations and a continuous debate between data producers and data users on the use of public data. We argue that the publicly available data should be treated as open data, a shared resource with unrestricted use for analysis, interpretation, and publication.</P>
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Lakshmi Yaneesha Sujeeun,Nowsheen Goonoo,Kaylina Marie Moutou,Shakuntala Baichoo,Archana Bhaw‑Luximon 한국고분자학회 2023 Macromolecular Research Vol.31 No.4
The success of tissue engineering scaffolds for wound healing relies on balanced physicochemical properties and the addition of small molecules. These two require an in-depth understanding of the interactions between the different components of the scaffolds for favorable and synergistic actions. Thus, the choice of polymeric blends is crucial to tune the properties of the resulting scaffolds. Miscibility of the polymer blends can be used to assess polymer–polymer interactions for effective scaffold engineering and cell–material interactions. The focus of this study was to apply machine learning (ML) methods to classify and predict the miscibility of polymer blends, namely poly(hydroxybutyrate-co-valerate)/fucoidan (PHBV/FUC), polyhydroxybutyrate/kappa-carrageenan (PHB/KCG), and cellulose acetate/polyamide (CA/PA). Physicochemical parameters assessed through Fourier transform infrared spectroscopy (FTIR), thermal analysis, and mechanical properties were used as input data. Depending on blend film compositions, the polymers were either partially miscible or completely immiscible. Six supervised classification algorithms were trained on the data with Scikit-learn. The random forest classifier outperformed the other algorithms with the optimal performance metrics. A simple multiple linear regression model was applied to polymer–drug interaction data. Preliminary results indicated that regression models could be correlated with ultraviolet (UV) absorbance of polymer–drug solutions.
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