Publications

Found 130 results
2019
Chen Q, Peng Y, Keenan T, Dharssi S, N EAgro, Wong WT, Chew EY, Lu Z.  2019.  A multi-task deep learning model for the classification of Age-related Macular Degeneration. AMIA Jt Summits Transl Sci Proc. 2019:505-514.
Peng Y, Yan K, Sandfort V, Summers RM, Lu Z.  2019.  A self-attention based deep learning method for lesion attribute detection from CT reports. 2019 IEEE International Conference on Healthcare Informatics (ICHI).
Peng Y, Zhang Z, Wang X, Yang L, Lu L.  2019.  Text Mining and Deep Learning for Disease Classification. Handbook of Medical Image Computing and Computer Assisted Intervention. :109-136.
Peng Y, Yan S, Lu Z.  2019.  Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets. Proceedings of the 18th BioNLP Workshop and Shared Task. :58–65.
2018
Peng Y, Rios A, Kavuluru R, Lu Z.  2018.  Extracting chemical-protein relations with ensembles of SVM and deep learning models. Database (Oxford). 2018
Allot A, Peng Y, Wei C-H, Lee K, Phan L, Lu Z.  2018.  LitVar: a semantic search engine for linking genomic variant data in PubMed and PMC. Nucleic Acids Res. 46(W1):W530-W536.
Wang X, Peng Y, Lu L, Lu Z, Summers RM.  2018.  Method and system of building hospital-scale chest x-ray database for entity extraction and weakly-supervised classification and localization of common thorax diseases .
Peng Y, Wang X, Lu L, Bagheri M, Summers R, Lu Z.  2018.  NegBio: a high-performance tool for negation and uncertainty detection in radiology reports. AMIA Jt Summits Transl Sci Proc. 2017:188-196.
Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, Ferrero E, Agapow P-M, Zietz M, Hoffman MM et al..  2018.  Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface. 15(141)
Wang X, Peng Y, Lu L, Lu Z, Summers RM.  2018.  TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-Rays. IEEE/CVF Conference on Computer Vision and Pattern Recognition. :9049-9058.
2017
Doğan RIslamaj, Chatr-aryamontri A, Kim S, Wei C-H, Peng Y, Comeau D, Lu Z.  2017.  BioCreative VI Precision Medicine Track: creating a training corpus for mining protein-protein interactions affected by mutations. BioNLP 2017. :171–175.
Peng Y, Rios A, Kavuluru R, Lu Z.  2017.  Chemical-protein relation extraction with ensembles of SVM, CNN, and RNN models. Proceedings of the BioCreative VI Workshop. :148-151.
Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM.  2017.  ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). :2097-2106.
Peng Y, Lu Z.  2017.  Deep learning for extracting protein-protein interactions from biomedical literature. BioNLP 2017. :29–38.
2016
Wei C-H, Peng Y, Leaman R, Davis APeter, Mattingly CJ, Li J, Wiegers TC, Lu Z.  2016.  Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task. Database (Oxford). 2016
Peng Y, Arighi C, Wu CH, Vijay-Shanker K.  2016.  BioC-compatible full-text passage detection for protein-protein interactions using extended dependency graph. Database (Oxford). 2016
Kim S, Doğan RIslamaj, Chatr-Aryamontri A, Chang CS, Oughtred R, Rust J, Batista-Navarro R, Carter J, Ananiadou S, Matos S et al..  2016.  BioCreative V BioC track overview: collaborative biocurator assistant task for BioGRID. Database (Oxford). 2016
Peng Y, Wei C-H, Lu Z.  2016.  Improving chemical disease relation extraction with rich features and weakly labeled data. J Cheminform. 8:53.
2015
Peng Y, Arighi C, Wu CH,.Vijay-Shanker K.  2015.  Extended dependency graph for BioC-compatible protein-protein interaction (PPI) passage detection in full-text articles. Proceedings of the BioCreative V Workshop. :30-35.
Peng Y, Gupta S, Wu C, Shanker V.  2015.  An extended dependency graph for relation extraction in biomedical texts. Proceedings of BioNLP 15. :21–30.
Li G, Ross KE, Arighi CN, Peng Y, Wu CH, Vijay-Shanker K.  2015.  miRTex: A Text Mining System for miRNA-Gene Relation Extraction. PLoS Comput Biol. 11(9):e1004391.
Wei C-H, Peng Y, Leaman R, Davis APeter, Mattingly CJ, Li J, Wiegers TC, Lu Z.  2015.  Overview of the Biocreative V chemical disease relation (CDR) task. Proceedings of the BioCreative V Workshop. :154-166.
2014
Comeau DC, Batista-Navarro RTheresa, Dai H-J, Doğan RIslamaj, Yepes AJimeno, Khare R, Lu Z, Marques H, Mattingly CJ, Neves M et al..  2014.  BioC interoperability track overview. Database (Oxford). 2014
Peng Y, Torii M, Wu CH, Vijay-Shanker K.  2014.  A generalizable NLP framework for fast development of pattern-based biomedical relation extraction systems. BMC Bioinformatics. 15:285.
Peng Y, Tudor CO, Torii M, Wu CH, Vijay-Shanker K.  2014.  iSimp in BioC standard format: enhancing the interoperability of a sentence simplification system. Database (Oxford). 2014