A Comparative Analysis of Active Learning for Biomedical Text Mining by Usman Naseem, Matloob Khushi, Shah Khalid Khan, Kamran Shaukat, Mohammad Ali Moni

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Web Interface of NER and RE with BERT for Biomedical Text Mining by Yeon-Ji Park, Min-a Lee, Geun-Je Yang, Soo Jun Park, Chae-Bong Sohn

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A syndrome differentiation model of TCM based on multi-label deep forest using biomedical text mining by Lejun Gong, Lejun Gong, Jindou Jiang, Shiqi Chen, Mingming Qi

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A Web Application for Biomedical Text Mining of Scientific Literature Associated with Coronavirus-Related Syndromes: Coronavirus Finder by Dagoberto Armenta-Medina, Aniel Jessica Leticia Brambila-Tapia, Sabino Miranda-Jiménez, Edel Rafael Rodea-Montero

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Serial KinderMiner (SKiM) discovers and annotates biomedical knowledge using co-occurrence and transformer models by Robert J. Millikin, Kalpana Raja, John Steill, Cannon Lock, Xuancheng Tu, Ian Ross, Lam C. Tsoi, Finn Kuusisto, Zijian Ni, Miron Livny, Brian Bockelman, James Thomson, Ron Stewart

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A natural language processing system for the efficient updating of highly curated pathophysiology mechanism knowledge graphs by Negin Sadat Babaiha, Hassan Elsayed, Bide Zhang, Abish Kaladharan, Priya Sethumadhavan, Bruce Schultz, Jürgen Klein, Bruno Freudensprung, Vanessa Lage-Rupprecht, Alpha Tom Kodamullil, Marc Jacobs, Stefan Geissler, Sumit Madan, Martin Hofmann-Apitius

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Rationalism in the face of GPT hypes: Benchmarking the output of large language models against human expert-curated biomedical knowledge graphs by Negin Sadat Babaiha, Sathvik Guru Rao, Jürgen Klein, Bruce Schultz, Marc Jacobs, Martin Hofmann-Apitius

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Large-Scale Biomedical Relation Extraction Across Diverse Relation Types: Model Development and Usability Study on COVID-19 by Zeyu Zhang, Meng Fang, Rebecca Wu, Hui Zong, Honglian Huang, Yuantao Tong, Yujia Xie, Shiyang Cheng, Ziyi Wei, M James C Crabbe, Xiaoyan Zhang, Ying Wang

“…Optimized RE models and tools for diverse relation types were developed, which can be widely used in biomedical text mining. Our usability studies provided a proof-of-concept demonstration of how large-scale RE can be leveraged to facilitate novel research.…”
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Medical Named Entity Recognition (MedNER): Deep learning model for recognizing medical entities (drug, disease) from scientific texts by Miah, Md Saef Ullah, Junaida, Sulaiman, Talha, Sarwar, Islam, Saima Sharleen, Rahman, Mizanur, Haque, Md Samiul

“…Medical Named Entity Recognition (MedNER) is an indispensable task in biomedical text mining. NER aims to recognize and categorize named entities in scientific literature, such as genes, proteins, diseases, and medications. …”
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Text mining in bioinformatics: past, present and future by Faiiazee, Hadee, Syed Mohamed, Syed Abdul Rahman Al-Haddad, Abdullah, Rusli, Samsudin, Khairulmizam

“…In this paper, we identify the current heavily used methods for biomedical text mining, their capabilities and developments, some proposed solution and how to evaluate performance. …”
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Using bottleneck adapters to identify cancer in clinical notes under low-resource constraints by Rohanian, O, Jauncey, H, Nouriborji, M, Chauhan, VK, Gonçalves, BP, Kartsonaki, C, Merson, L, Clifton, D

“…Based on our findings, we suggest that using bottleneck adapters in low-resource situations with limited access to labelled data or processing capacity could be a viable strategy in biomedical text mining.…”

Extended distributed prototypical for biomedical named entity recognition by Maan Tareq Abd, Masnizah Mohd

“…Biomedical Named Entity Recognition (Bio-NER) is an essential step of biomedical information extraction and biomedical text mining. Although, a lot of researches have been made in the design of rule-based and supervised tools for general NER, Bio-NER still remains a challenge and an area of active research, as still there is huge difference in F-score of 10 points between general newswire NER and Bio-NER. …”
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Automated Confirmation of Protein Annotation Using NLP and the UniProtKB Database by Jin Tao, Kelly A. Brayton, Shira L. Broschat

“…Our ensemble model achieves 91.25% recall, 71.26% accuracy, 65.19% precision, and an F1 score of 76.05% and outperforms the Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (BioBERT) model with fine-tuning using the same data.…”
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A Novel Multi-View Ensemble Learning Architecture to Improve the Structured Text Classification by Carlos Adriano Gonçalves, Adrián Seara Vieira, Célia Talma Gonçalves, Rui Camacho, Eva Lorenzo Iglesias, Lourdes Borrajo Diz

“…Experimental results lead to the sustained conclusion that the application of multi-view techniques to full texts significantly improves the task of text classification, providing a significant contribution for the biomedical text mining research. We also have evidence to conclude that enriched datasets with text from certain sections are better than using only titles and abstracts.…”
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Joint Biomedical Entity and Relation Extraction Based on Feature Filter Table Labeling by Zhaojie Sun, Linlin Xing, Longbo Zhang, Hongzhen Cai, Maozu Guo

“…Joint biomedical entity and relation extraction is essential in biomedical text mining. It automatically identifies entities and uncovers the relation between them from biomedical texts. …”
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State-of-the-Art Evidence Retriever for Precision Medicine: Algorithm Development and Validation by Qiao Jin, Chuanqi Tan, Mosha Chen, Ming Yan, Ningyu Zhang, Songfang Huang, Xiaozhong Liu

“…PM-Search uses a novel Bidirectional Encoder Representations from Transformers for Biomedical Text Mining–based active learning strategy that models evidence quality and improves the model performance. …”
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MARIE: A Context-Aware Term Mapping with String Matching and Embedding Vectors by Han Kyul Kim, Sae Won Choi, Ye Seul Bae, Jiin Choi, Hyein Kwon, Christine P. Lee, Hae-Young Lee, Taehoon Ko

“…By incorporating both string matching methods and term embedding vectors generated by BioBERT (bidirectional encoder representations from transformers for biomedical text mining), it utilizes both structural and contextual information to calculate similarity measures between source and target terms. …”
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HESML: a real-time semantic measures library for the biomedical domain with a reproducible survey by Juan J. Lastra-Díaz, Alicia Lara-Clares, Ana Garcia-Serrano

“…Abstract Background Ontology-based semantic similarity measures based on SNOMED-CT, MeSH, and Gene Ontology are being extensively used in many applications in biomedical text mining and genomics respectively, which has encouraged the development of semantic measures libraries based on the aforementioned ontologies. …”
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Concept recognition as a machine translation problem by Mayla R. Boguslav, Negacy D. Hailu, Michael Bada, William A. Baumgartner, Lawrence E. Hunter

“…Results Bidirectional encoder representations from transformers for biomedical text mining (BioBERT) for span detection along with the open-source toolkit for neural machine translation (OpenNMT) for concept normalization achieve state-of-the-art performance for most ontologies annotated in the CRAFT Corpus. …”
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Few-Shot Learning for Clinical Natural Language Processing Using Siamese Neural Networks: Algorithm Development and Validation Study by David Oniani, Premkumar Chandrasekar, Sonish Sivarajkumar, Yanshan Wang

“…The clinical NLP task is benchmarked using the following 4 pretrained language models: bidirectional encoder representations from transformers (BERT), BERT for biomedical text mining (BioBERT), BioBERT trained on clinical notes (BioClinicalBERT), and generative pretrained transformer 2 (GPT-2). …”
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