11 results found
Emam I, Elyasigomari V, Matthews A, et al., 2019, PlatformTM, a standards-based data custodianship platform for translational medicine research., Scientific Data, Vol: 6, Pages: 149-149, ISSN: 2052-4463
Biomedical informatics has traditionally adopted a linear view of the informatics process (collect, store and analyse) in translational medicine (TM) studies; focusing primarily on the challenges in data integration and analysis. However, a data management challenge presents itself with the new lifecycle view of data emphasized by the recent calls for data re-use, long term data preservation, and data sharing. There is currently a lack of dedicated infrastructure focused on the 'manageability' of the data lifecycle in TM research between data collection and analysis. Current community efforts towards establishing a culture for open science prompt the creation of a data custodianship environment for management of TM data assets to support data reuse and reproducibility of research results. Here we present the development of a lifecycle-based methodology to create a metadata management framework based on community driven standards for standardisation, consolidation and integration of TM research data. Based on this framework, we also present the development of a new platform (PlatformTM) focused on managing the lifecycle for translational research data assets.
Oehmichen A, Guitton F, Agapow P, et al., 2018, A multi-tenant computational platform for translational medicine, 38th IEEE International Conference on Distributed Computing Systems (ICDCS), Publisher: IEEE, Pages: 1553-1556, ISSN: 1063-6927
Wang S, Pandis I, Johnson D, et al., 2014, Optimising Correlation Matrix Calculations on Gene Expression Data, BMC Bioinformatics, Vol: 15, ISSN: 1471-2105
Wang S, Pandis I, Emam I, et al., 2014, DSIMBench: A benchmark for Microarray Data using R, the 40th International Conference on Very Large Databases (VLDB 2014)
Wang S, Pandis I, Wu C, et al., 2014, High Dimensional Biological Data Retrieval Optimization with NoSQL Technology, BMC Genomics, ISSN: 1471-2164
Rustici G, Kolesnikov N, Brandizi M, et al., 2013, ArrayExpress update-trends in database growth and links to data analysis tools, NUCLEIC ACIDS RESEARCH, Vol: 41, Pages: D987-D990, ISSN: 0305-1048
Dalby AR, Emam I, Franke R, 2012, Analysis of Gene Expression Data from Non-Small Cell Lung Carcinoma Cell Lines Reveals Distinct Sub-Classes from Those Identified at the Phenotype Level, PLOS ONE, Vol: 7, ISSN: 1932-6203
Parkinson H, Sarkans U, Kolesnikov N, et al., 2011, ArrayExpress update-an archive of microarray and high-throughput sequencing-based functional genomics experiments, NUCLEIC ACIDS RESEARCH, Vol: 39, Pages: D1002-D1004, ISSN: 0305-1048
Kapushesky M, Emam I, Holloway E, et al., 2010, Gene Expression Atlas at the European Bioinformatics Institute, NUCLEIC ACIDS RESEARCH, Vol: 38, Pages: D690-D698, ISSN: 0305-1048
Parkinson H, Kapushesky M, Kolesnikov N, et al., 2009, ArrayExpress update-from an archive of functional genomics experiments to the atlas of gene expression, NUCLEIC ACIDS RESEARCH, Vol: 37, Pages: D868-D872, ISSN: 0305-1048
El-Shishiny H, Soliman THA, Emam I, 2006, Mining drug targets: The challenges and a proposed framework, Pages: 239-244, ISSN: 1530-1346
Drug target identification, being the first phase in drug discovery is becoming an overly time consuming process and in many cases produces inefficient results due to failure of conventional approaches to investigate large scale data. The main goal of this work is to identify drug targets, where there are genes or proteins associated with specific diseases. With the help of Microarray technology, the relationship between biological entities such as protein-protein, gene-gene and related chemical compounds are used as a means to identify drug targets. In this work, we focus on the challenges facing drug target discovery and propose a novel unified framework for mining disease related drug targets. © 2006 IEEE.
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