Mass data exploration in oncology: an information synthesis approach.
New technologies and equipment allow for mass treatment of samples and research teams share acquired data on an always larger scale. In this context scientists are facing a major data exploitation problem. More precisely, using these data sets through data mining tools or introducing them in a classical experimental approach require a preliminary understanding of the information space, in order to direct the process. But acquiring this grasp on the data is a complex activity, which is seldom supported by current software tools. The goal of this paper is to introduce a solution to this scientific data grasp problem. Illustrated in the Tissue MicroArrays application domain, the proposal is based on the synthesis notion, which is inspired by Information Retrieval paradigms. The envisioned synthesis model gives a central role to the study the researcher wants to conduct, through the task notion. It allows for the implementation of a task-oriented Information Retrieval prototype system. Cases studies and user studies were used to validate this prototype system. It opens interesting prospects for the extension of the model or extensions towards other application domains.
Bourbeillon
Julie
J
CNRS - Grenoble Universités, UMR 5525, Laboratoire TIMC-IMAG (Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications de Grenoble). F38710 La Tronche, France.
Garbay
Catherine
C
Giroud
Françoise
F
D019295:Computational Biology / Q000379:methods*; D003628:Database Management Systems; D030541:Databases, Genetic; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008495:Medical Oncology / Q000379:methods*; D008962:Models, Theoretical; D015203:Reproducibility of Results; D046888:Tissue Array Analysis / Q000379:methods*; D014584:User-Computer Interface