BCISeach: A Searching Platform of Breast Cancer Text Mining for Biomedical Literature

2016 ◽  
Author(s):  
Lejun Gong ◽  
Ronggen Yang ◽  
Haoyu Yang ◽  
Kaiyu Jiang ◽  
Zhenjiang Dong ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Text Mining ◽  
Biomedical Literature

scholarly journals Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends

2016 ◽  
Vol 9 (1) ◽  
Author(s):  
Gabriela Jurca ◽  
Omar Addam ◽  
Alper Aksac ◽  
Shang Gao ◽  
Tansel Özyer ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Network Analysis ◽  
Text Mining ◽  
Cancer Trends

scholarly journals A Novel Text-Mining Approach for Retrieving Pharmacogenomics Associations From the Literature

2020 ◽  
Vol 11 ◽  
Author(s):  
Maria-Theodora Pandi ◽  
Peter J. van der Spek ◽  
Maria Koromina ◽  
George P. Patrinos
Keyword(s):  
Text Mining ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Biomedical Literature ◽  
Linear Kernel ◽  
R Programming Language ◽  
Research Areas ◽  
Text Classifiers ◽  
R Programming ◽  
Further Development

Text mining in biomedical literature is an emerging field which has already been shown to have a variety of implementations in many research areas, including genetics, personalized medicine, and pharmacogenomics. In this study, we describe a novel text-mining approach for the extraction of pharmacogenomics associations. The code that was used toward this end was implemented using R programming language, either through custom scripts, where needed, or through utilizing functions from existing libraries. Articles (abstracts or full texts) that correspond to a specified query were extracted from PubMed, while concept annotations were derived by PubTator Central. Terms that denote a Mutation or a Gene as well as Chemical compound terms corresponding to drug compounds were normalized and the sentences containing the aforementioned terms were filtered and preprocessed to create appropriate training sets. Finally, after training and adequate hyperparameter tuning, four text classifiers were created and evaluated (FastText, Linear kernel SVMs, XGBoost, Lasso, and Elastic-Net Regularized Generalized Linear Models) with regard to their performance in identifying pharmacogenomics associations. Although further improvements are essential toward proper implementation of this text-mining approach in the clinical practice, our study stands as a comprehensive, simplified, and up-to-date approach for the identification and assessment of research articles enriched in clinically relevant pharmacogenomics relationships. Furthermore, this work highlights a series of challenges concerning the effective application of text mining in biomedical literature, whose resolution could substantially contribute to the further development of this field.

scholarly journals BioReader: a text mining tool for performing classification of biomedical literature

2019 ◽  
Vol 19 (S13) ◽  
Author(s):  
Christian Simon ◽  
Kristian Davidsen ◽  
Christina Hansen ◽  
Emily Seymour ◽  
Mike Bogetofte Barnkob ◽  
...  
Keyword(s):  
Text Mining ◽  
Biomedical Literature ◽  
Mining Tool ◽  
Text Mining Tool

scholarly journals Best Paper Selection

2019 ◽  
Vol 28 (01) ◽  
pp. 179-180
Keyword(s):  
Breast Cancer ◽  
Mental Health ◽  
Text Mining ◽  
Drug Treatment ◽  
Topic Model ◽  
Web Based ◽  
Novel Approach ◽  
Health Communities ◽  
Model Approach

Abdellaoui R, Foulquié P, Texier N, Faviez C, Burgun A, Schück S. Detection of Cases of Noncompliance to Drug Treatment in Patient Forum Posts: Topic Model Approach. J Med Internet Res 2018;20(3):e85 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5874436/ Jones J, Pradhan M, Hosseini M, Kulanthaivel A, Hosseini M. Novel Approach to Cluster Patient-Generated Data Into Actionable Topics: Case Study of a Web-Based Breast Cancer. JMIR Med Inform 2018;6(4):e45 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6293240/ Park A, Conway M, Chen AT. Examining Thematic Similarity, Difference, and Membership in Three Online Mental Health Communities from Reddit: A Text Mining and Visualization Approach. Comput Human Behav 2018 Jan;78:98-112 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5810583/

scholarly journals Optimizing Literature Search: TEMAS, A New Text-Mining Algorithm-Assisted Search Tool

2020 ◽  
Author(s):  
Emmanuel Bonnet ◽  
Daurès Jean-Pierre ◽  
Landais Paul
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Text Mining ◽  
Literature Search ◽  
Hierarchical Classification ◽  
Recall Rate ◽  
Average Precision ◽  
Mining Algorithm ◽  
Search Tool ◽  
Meta Analyses

Abstract Background: Literature search is challenging when thousands of articles are potentially involved. To facilitate literature search we created TEMAS a Text Mining Algorithm-assisted Search tool that we compared to a PubMed reference search (RS) in the context of etiological epidemiology.Methods: The 4 steps of TEMAS are: 1) a classic PubMed global search 2) a first sort removing articles without abstracts or containing off-topic terms 3) a clustering step with a descending hierarchical classification regrouping articles in independent classes 4) a final sort extracting from the targeted class the abstracts containing the terms of interest, with a link to the corresponding PubMed articles. Validation was performed for risk factors of breast cancer. We estimated the precision and recall rate compared to RS. Average precision and discounted cumulative gain (DCG) were also computed to perform a ranking-based evaluation. We also compared TEMAS results with articles selected in two meta-analyses.Results: For risk factors of breast cancer, breastfeeding, mammographic density, oral contraceptive, and menarche were explored. TEMAS consistently increased precision vs RS (from 23% to 32%), with a recall rate from 95% to 97%, and divided the number of selected articles to read from 2.3 to 4.8 times. Mean average precision for 100 articles was 47.4% for TEMAS vs 20.9% for PubMed ranked by best match, and DCG showed a consistent improvement for TEMAS compared to PubMed best match.Discussion: TEMAS divided the results of a literature search by 3.2, and improved the precision rate, the average precision, and the DCG compared to RS for epidemiological studies. Reducing the number of selected articles inevitably impacted the recall rate. However, it remained satisfactory and did not bias the corpus of information. Moreover, the recall rate was 100% for the two meta-analyses we analyzed, which suggests that the loss of recall rate observed above concerned articles not relevant enough to be included in the meta-analyses.Conclusion: TEMAS provides a user-friendly interface for non-specialists of literature search confronted with thousands of articles and appeared useful for meta-analyses.

scholarly journals MACE2K: A Text-Mining Tool to Extract Literature-based Evidence for Variant Interpretation using Machine Learning

2020 ◽  
Author(s):  
Samir Gupta ◽  
Shruti Rao ◽  
Trisha Miglani ◽  
Yasaswini Iyer ◽  
Junxia Lin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Text Mining ◽  
Genomic Medicine ◽  
Relevant Information ◽  
Biomedical Literature ◽  
Variant Interpretation ◽  
Learning Models ◽  
Mining Tool ◽  
Text Mining Tool

AbstractInterpretation of a given variant’s pathogenicity is one of the most profound challenges to realizing the promise of genomic medicine. A large amount of information about associations between variants and diseases used by curators and researchers for interpreting variant pathogenicity is buried in biomedical literature. The development of text-mining tools that can extract relevant information from the literature will speed up and assist the variant interpretation curation process. In this work, we present a text-mining tool, MACE2k that extracts evidence sentences containing associations between variants and diseases from full-length PMC Open Access articles. We use different machine learning models (classical and deep learning) to identify evidence sentences with variant-disease associations. Evaluation shows promising results with the best F1-score of 82.9% and AUC-ROC of 73.9%. Classical ML models had a better recall (96.6% for Random Forest) compared to deep learning models. The deep learning model, Convolutional Neural Network had the best precision (75.6%), which is essential for any curation task.

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