Welcome to Bionotate, a web tool designed to enrich our understanding of published genetic research on Autism Spectrum Disorder (ASD). Bionotate represents an important step forward towards a sophisticated search engine that will enable faster, focused searches through the legacy of biomedical text on ASD, ultimately quickening the pace to discovery. The effort is a collaboration between Harvard University, University of Granada and Alias-i Inc. Thank you for considering contributing to this project. Please visit the blog at annotate4asd blog.
Objective 1 of the annotation project is to have human beings (you) identify whether an ASD citation contains mention of a gene. Please review the instructions before starting the task.
At this point we are asking people to annotate 20 articles but it is fine if you do less. If you are interested in participating further, send us email.
If Objective 1 goes well, later objectives will include spotting particular mentions of genes in ASD citations and the holy grail of genetics text mining, relationships between genes and other biologically relevant entities.
Currently there are over 11000 articles associated with ASD in the publicly registered literature spanning from 1943-present. Current search engines cannot readily differentiate which of these articles mention a gene in a context relevant for genetics research. With your help, we are going to build a system that can learn the difference between abstracts about genes and abstracts that are not about genes. This "education" will allow us to design a search engine that can automatically show researchers working on the genetics of ASD articles to only see the kinds of articles they need (the ratio of useful articles to not useful may be as low as 1 to 8). This will make their research faster, but will also enable us to build even more sophistication into the engine to enable gene spotting, and automatic extraction of molecular context.
Questions can be addressed to email@example.com
This project is a joint effort of Alias-i, Inc., the Department of Computer Science and A.I., University of Granada and the Center for Biomedical Informatics, Harvard Medical School. Partially funded by NIH grant 2R44RR020259.
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