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LAS: A Software Platform to Support Oncological Data Management

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Abstract

The rapid technological evolution in the biomedical and molecular oncology fields is providing research laboratories with huge amounts of complex and heterogeneous data. Automated systems are needed to manage and analyze this knowledge, allowing the discovery of new information related to tumors and the improvement of medical treatments. This paper presents the Laboratory Assistant Suite (LAS), a software platform with a modular architecture designed to assist researchers throughout diverse laboratory activities. The LAS supports the management and the integration of heterogeneous biomedical data, and provides graphical tools to build complex analyses on integrated data. Furthermore, the LAS interfaces are designed to ease data collection and management even in hostile environments (e.g., in sterile conditions), so as to improve data quality.

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Notes

  1. https://www.djangoproject.com/

  2. http://www.mysql.com/

  3. http://www.mongodb.org/

  4. http://hadoop.apache.org

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Authors and Affiliations

  1. Dipartimento di Automatica e Informatica, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129, Torino, Italy

    Elena Baralis & Alberto Grand

  2. Laboratory of Molecular Pharmacology, Institute for Cancer Research and Treatment (IRCC), Str. Prov. 142 Km. 3.95, 10060, Candiolo, Italy

    Andrea Bertotti

  3. Department of Oncological Sciences, University of Torino Medical School, Str. Prov. 142 Km. 3.95, 10060, Candiolo, Italy

    Andrea Bertotti

  4. Fondazione Piemontese per la Ricerca sul Cancro-Onlus (FPRC), Institute for Cancer Research and Treatment (IRCC), Str. Prov. 142 Km. 3.95, 10060, Candiolo, Italy

    Alessandro Fiori

Authors
  1. Elena Baralis
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  2. Andrea Bertotti
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  3. Alessandro Fiori
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  4. Alberto Grand
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Correspondence to Alessandro Fiori.

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Baralis, E., Bertotti, A., Fiori, A. et al. LAS: A Software Platform to Support Oncological Data Management. J Med Syst 36 (Suppl 1), 81–90 (2012). https://doi.org/10.1007/s10916-012-9891-6

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  • DOI: https://doi.org/10.1007/s10916-012-9891-6

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