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Section: Research Program
Data Integration
Participants : Christine Froidevaux, Alain Denise, Sarah Cohen-Boulakia, Bryan Brancotte, Jiuqiang Chen.
Faced with the inherent features of biological and biomedical data, researchers from the database and artificial intelligence communities have joined together to form a community dedicated to the study of the specific problems posed by integrating life sciences data. With the deluge of new sequenced genome sequences and the amount of data produced by high-throughput approaches, the need to cross and compare massive and heterogeneous data is more important than ever to improve functional annotation and design biological networks. Challenges are numerous. One may cite the need to provide support to scientists to perform and share complex and reproducible complex biological analyses. A special attention is paid to the more specific domain of scientific workflows management and ranking biological data. One aims at exploring the relationships between those two domains, from the investigation of various specific problems posed by ranking scientific workflows to the problem of considering consensus workflows.
Designing and Comparing Scientific workflows
Participants : Sarah Cohen-Boulakia, Christine Froidevaux, Jiuqiang Chen.
Scientific workflows management systems are increasingly used to specify and manage bioinformatics experiments. Their programming model appeals to bioinformaticians, who use them to easily specify complex data processing pipelines. Such a model is underpinned by a graph structure, where nodes represent bioinformatics tasks and links represent the dataflow. As underlined both in a study and a review of existing approaches, the complexity of such graph structures is increasing over time, making them more difficult to share and reuse.
One of the major current challenges is thus to provide means to reduce the structural complexity of workflows while ensuring that any structural transformation will not have any impact on the executions of the transformed workflows, that is, preserving provenance.
Ranking biological data
Participants : Alain Denise, Sarah Cohen-Boulakia, Bryan Brancotte.
We are addressing the increase of the number of resources available. The BioGuide project aim at helping user navigation in the maze of available biological sources. More recently, a second problem was tackled: the number of answers returned by even one single queried biological resource may be too large for the user to deal with. We have provided solutions for ranking biological data. The main difficulty lies in considering various ranking criteria (recent data first, popular data first, curated data first...). Many approaches combine ranking criteria to design a ranking function, possibly leading to arbitrary choices made in the way of combining the ranking criteria. Instead, in collaboration with the University of Montreal, we have proposed to follow a median ranking approach named BioConsert (for generating Biological Consensus ranking with ties): considering as many rankings as they are ranking criteria for the same data set, and providing a consensus ranking that minimizes the disagreements between the input rankings. We have shown the benefit of using median ranking in several biological settings.
Additionally, in a close collaboration with the Institut Curie, we have also developed the GeneValorization tool that ranks a list of genes of interest given as input with respect to a set of keywords representing the context of study. Here the single ranking criterion considered for each gene is the number of publications in PubMed co-citing the gene name and the keywords. The tool is able to make use of the MeSH taxonomy when considering the keywords and the dictionary of gene names and aliases for the gene names.