Exploring the efficacy of caption search for bioscience journal search interfaces
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Biomedical question answering: A survey
Computer Methods and Programs in Biomedicine
An IR-Aided Machine Learning Framework for the BioCreative II.5 Challenge
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
ISMB/ECCB'09 Proceedings of the 2009 workshop of the BioLink Special Interest Group, international conference on Linking Literature, Information, and Knowledge for Biology
Extraction of relevant figures and tables for multi-document summarization
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part II
Automatic figure classification in bioscience literature
Journal of Biomedical Informatics
Hi-index | 3.84 |
Images (e.g., figures) are important experimental results that are typically reported in bioscience full-text articles. Biologists need to access images to validate research facts and to formulate or to test novel research hypotheses. On the other hand, biologists live in an age of information explosion. As thousands of biomedical articles are published every day, systems that help biologists efficiently access images in literature would greatly facilitate biomedical research. We hypothesize that much of image content reported in a full-text article can be summarized by the sentences in the abstract of the article. In our study, more than one hundred biologists had tested this hypothesis and more than 40 biologists had evaluated a novel user-interface BioEx that allows biologists to access images directly from abstract sentences. Our results show that 87.8% biologists were in favor of BioEx over two other baseline user-interfaces. We further developed systems that explored hierarchical clustering algorithms to automatically identify abstract sentences that summarize the images. One of the systems achieves a precision of 100% that corresponds to a recall of 4.6%. Contact:hongyu@uwm.edu or hy52@columbia.edu