Dr. Ling Luo is a professor in School of Computer Science and Technology at Dalian University of Technology. His research interests include Natural Language Processing, Biomedical Text Mining, and Deep Learning for Healthcare. He has contributed to biomedical text mining research by developing novel deep learning methods to unstructured text data in the biomedical literature, especially for the tasks of document classification, named entity recognition and relation extraction. Dr. Luo's long-term research goal is to develop computational methods to better understand the natural language in biomedical text in order to accelerate knowledge discovery and improve health. Over the years, Dr. Luo has co-authored over 40 papers in leading journals and conferences such as Briefings in Bioinformatics, Bioinformatics, Nucleic Acids Research, and BIBM. Dr. Luo won the first place in multiple tasks at the famous international biomedical text mining challenge BioCreative.

(See https://lingluodlut.github.io/ for more information).

Research Projects

1. Young Scientists Fund of the National Natural Science Foundation of China (principal investigator), Research on patient-centric personalized information extraction from biomedical literature, 62302076, 2024-2026.

2. NIH intermural research project (Participation), Named Entity Recognition and Relationship Extraction in Biomedicine, 1ZIALM091813, 2020-2023.

3. National Key Research and Development Program of China, Construction of Knowledgebase of Precision Medicine for Disease Studies, No. 2016YFC0901902, 2016-2020.

Selected Publications：

1. L Luo, CH Wei, PT Lai, R Leaman, Q Chen, Z Lu. AIONER: all-in-one scheme-based biomedical named entity recognition using deep learning [J]. Bioinformatics, 2023, 39(5): btad310. (JCR Q1, IF: 6.931)

2. L Luo, PT Lai, CH Wei, CN Arighi, Z Lu. BioRED: a rich biomedical relation extraction dataset [J]. Briefings in Bioinformatics, 2022, bbac282. (JCR Q1, IF: 13.994)

3. L Luo, CH Wei, PT Lai, Q Chen, R Islamaj, Z Lu. Assigning species information to corresponding genes by a sequence labeling framework [J]. Database-The Journal of Biological Databases and Curation, 2022, 2022: baac090. (JCR Q1, IF: 4.462)

4. L Luo, S Yan, PT Lai, D Veltri, A Oler, S Xirasagar, R Ghosh, M Similuk, P Robinson, Z Lu. PhenoTagger: A Hybrid Method for Phenotype Concept Recognition using Human Phenotype Ontology [J]. Bioinformatics, 2021, 37(13):1884-1890. (JCR Q1, IF: 6.931)

5. L Luo, Z Yang, M Cao, L Wang, Y Zhang, H Lin. A neural network-based joint learning approach for biomedical entity and relation extraction from biomedical literature [J]. Journal of Biomedical Informatics, 2020, 103: 103384. (JCR Q1, IF: 8.000)

6. L Luo, Zhihao Yang, Yawen Song, Nan Li and Hongfei Lin. Chinese Clinical Named Entity Recognition Based on Stroke ELMo and Multi-Task Learning [J] . Chinese Journal of Computers, 2020, 43(10):1943-1957. (In Chinese, CCF-A )

7. L Luo, Z Yang, P Yang, Y Zhang, L Wang, H Lin, J Wang. An attentionbased BiLSTM-CRF approach to document-level chemical named entity recognition [J]. Bioinformatics, 2018, 34(8): 1381-1388. (JCR Q1, IF: 6.931)

8. L Luo, Z Yang, L Wang, Y Zhang, H Lin, J Wang, L Yang, K Xu, Y Zhang. Protein-Protein Interaction Article Classification: A Knowledge-enriched Self-Attention Convolutional Neural Network Approach [C]. Procceding of 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2018. (CCF-B)

9. L Luo, Z Yang, P Yang, Y Zhang, L Wang, J Wang, H Lin. A neural network approach to chemical and gene/protein entity recognition in patents [J]. Journal of Cheminformatics, 2018, 10: 65. (JCR Q1, IF: 8.489)

Challenges：

1. BioCreative VII Challenge: Text mining drug and chemical-protein interactions (DrugProt) Track, The Second Place

2. The 2019 China Conference on Knowledge Graph and Semantic Computing (CCKS 2019) Challenge: Chinese Clinical Named Entity Recognition Task, The Third Place 

3. The 2018 China Conference on Knowledge Graph and Semantic Computing (CCKS 2018) Challenge: Chinese Clinical Named Entity Recognition Task, The Third Place 

4. BioCrative VI Precision Medicine Track: Document Triage Task, The Second Place 

5. BioCreative V.5 Challenge: The CEMP (Chemical Entity Mention in Patents) Task, The First Place 

6. BioCreative V.5 Challenge: The GPRO (Gene and Protein Related Object) Task, The First Place