Machine Learning and Big Data for Biocuration
in conjunction with Biocuration 2017
Stanford University, Palo Alto, CA, USA
March 26-29, 2017
Given the current uncertainties that potential participants face regarding their attendance, we cancelled our workshop at Biocuration 2017.
We plan to organize the workshop in Montreal this fall: we will keep you posted!
The massive amount of publicly available data is an amazing opportunity for artificial intelligence to play a key role in biocuration.
Automatic approaches have proven to be effective in supporting life sciences research, yet mining complex and unstructured big data is still a major challenge.
Sharing ideas, approaches, and resources is critical to provide biocurators, and bioinformatics teams supporting them, with cutting-edge solutions.
The general areas of interest of this workshop include machine learning based approaches for supporting the curation of large volumes of data.
The objective is to present new machine learning models and algorithms for supporting big bio-data curation.
Contributions presenting negative results are welcome!
Domains of applications cover triage of textual or non-textual documents, bio-entity discovery and linking in massive data sets, prediction of data of interest for curation and experimental investigation, retrieval of various types of documents (articles, genomics feature files, experimental data, etc.) according to topics of interest, and all the applications based on machine learning to support the biocuration of huge volumes of data.
Topics of Interest
Topics of interest include, but are not limited to:
- Classification of textual and non-textual documents
- Function prediction
- Big bio-data computing and processing
- Clustering of big bio-data
- Discovery of bio-entities
- Automatic annotation of big bio-data
- Bio-entity linking
- Information retrieval for biocuration
We invite submissions of full papers (up to 6 pages), short papers, posters, system description and demonstrations (up to 4 pages).
Papers must be presented in English, and must not substantially overlap with papers published or simultaneously submitted to a journal or a conference with proceedings.
All submissions will be reviewed using a simple blind process, and will be assessed based on their novelty, potential impact, and clarity of writing.
Accepted papers will be presented at the workshop, and included in the workshop proceedings. At least one author of each paper is expected to register for the workshop, and attend to present the paper.
Papers must be formatted according to the LaTeX
, Libre Office
or Microsoft Word
Camera Ready: TBD
For further information, please contact Marie-Jean Meurs [meurs [dot] marie-jean [at] uqam [dot] ca]
Olivier Bodenreider, U.S. National Library of Medicine, USA
Kevin Bretonnel Cohen, University of Colorado, USA
Wajdi Dhifli, UQAM, Canada
Cyril Grouin, LIMSI-CNRS, France
Lynette Hirschman, The MITRE Corporation, USA
Robert Leaman, NCBI/NLM/NIH, USA
Vladimir Makarenkov, UQAM, Canada
Yannick Pouliot, TOMA Biosciences
Mariana Lara Neves, Hasso-Plattner-Institute, Germany
Dietrich Rebholz-Schuhmann, Insight NUIG, Ireland
Julien Tremblay, National Research Council, Canada
SIB Swiss Institute of Bioinformatics Geneva, Switzerland