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

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.

Download Full-text

Extracting kinetic information from literature with KineticRE

Journal of Integrative Bioinformatics ◽

10.1515/jib-2015-282 ◽

2015 ◽

Vol 12 (4) ◽

pp. 56-68

Author(s):

Ana Alão Freitas ◽

Hugo Costa ◽

Isabel Rocha

Keyword(s):

Text Mining ◽

Metabolic Networks ◽

Scientific Literature ◽

Kluyveromyces Lactis ◽

Relevant Information ◽

Text Documents ◽

Kinetic Information ◽

Mining Tool ◽

Text Mining Tool

Summary To better understand the dynamic behavior of metabolic networks in a wide variety of conditions, the field of Systems Biology has increased its interest in the use of kinetic models. The different databases, available these days, do not contain enough data regarding this topic. Given that a significant part of the relevant information for the development of such models is still wide spread in the literature, it becomes essential to develop specific and powerful text mining tools to collect these data. In this context, this work has as main objective the development of a text mining tool to extract, from scientific literature, kinetic parameters, their respective values and their relations with enzymes and metabolites. The approach proposed integrates the development of a novel plug-in over the text mining framework @Note2. In the end, the pipeline developed was validated with a case study on Kluyveromyces lactis, spanning the analysis and results of 20 full text documents.

Download Full-text

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

BMC Bioinformatics ◽

10.1186/s12859-019-2607-x ◽

2019 ◽

Vol 19 (S13) ◽

Cited By ~ 9

Author(s):

Christian Simon ◽

Kristian Davidsen ◽

Christina Hansen ◽

Emily Seymour ◽

Mike Bogetofte Barnkob ◽

...

Keyword(s):

Text Mining ◽

Biomedical Literature ◽

Mining Tool ◽

Text Mining Tool

Download Full-text

ProPheno: An online dataset for completely characterizing the human protein-phenotype landscape in biomedical literature

10.7287/peerj.preprints.27479v1 ◽

2019 ◽

Author(s):

Morteza Pourreza Shahri ◽

Indika Kahanda

Keyword(s):

Text Mining ◽

Predictive Models ◽

Complex Diseases ◽

Biomedical Literature ◽

Human Protein ◽

Mining Tool ◽

Text Mining Tool

Identifying protein-phenotype relations is of paramount importance for applications such as uncovering rare and complex diseases. One of the best resources that captures the protein-phenotype relationships is the biomedical literature. In this work, we introduce ProPheno, a comprehensive online dataset composed of human protein/phenotype mentions extracted from the complete corpora of Medline and PubMed. Moreover, it includes co-occurrences of protein-phenotype pairs within different spans of text such as sentences and paragraphs. We use ProPheno for completely characterizing the human protein-phenotype landscape in biomedical literature. ProPheno, the reported findings and the gained insight has implications for (1) biocurators for expediting their curation efforts, (2) researches for quickly finding relevant articles, and (3) text mining tool developers for training their predictive models. The RESTful API of ProPheno is freely available at http://propheno.cs.montana.edu.

Download Full-text

Creating efficiencies in the extraction of data from randomized trials: a prospective evaluation of a machine learning and text mining tool

BMC Medical Research Methodology ◽

10.1186/s12874-021-01354-2 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Allison Gates ◽

Michelle Gates ◽

Shannon Sim ◽

Sarah A. Elliott ◽

Jennifer Pillay ◽

...

Keyword(s):

Machine Learning ◽

Text Mining ◽

Randomized Trials ◽

Data Extraction ◽

Learning Tools ◽

Time Savings ◽

Mining Tool ◽

Data Elements ◽

Text Mining Tool ◽

Manual Extraction

Abstract Background Machine learning tools that semi-automate data extraction may create efficiencies in systematic review production. We evaluated a machine learning and text mining tool’s ability to (a) automatically extract data elements from randomized trials, and (b) save time compared with manual extraction and verification. Methods For 75 randomized trials, we manually extracted and verified data for 21 data elements. We uploaded the randomized trials to an online machine learning and text mining tool, and quantified performance by evaluating its ability to identify the reporting of data elements (reported or not reported), and the relevance of the extracted sentences, fragments, and overall solutions. For each randomized trial, we measured the time to complete manual extraction and verification, and to review and amend the data extracted by the tool. We calculated the median (interquartile range [IQR]) time for manual and semi-automated data extraction, and overall time savings. Results The tool identified the reporting (reported or not reported) of data elements with median (IQR) 91% (75% to 99%) accuracy. Among the top five sentences for each data element at least one sentence was relevant in a median (IQR) 88% (83% to 99%) of cases. Among a median (IQR) 90% (86% to 97%) of relevant sentences, pertinent fragments had been highlighted by the tool; exact matches were unreliable (median (IQR) 52% [33% to 73%]). A median 48% of solutions were fully correct, but performance varied greatly across data elements (IQR 21% to 71%). Using ExaCT to assist the first reviewer resulted in a modest time savings compared with manual extraction by a single reviewer (17.9 vs. 21.6 h total extraction time across 75 randomized trials). Conclusions Using ExaCT to assist with data extraction resulted in modest gains in efficiency compared with manual extraction. The tool was reliable for identifying the reporting of most data elements. The tool’s ability to identify at least one relevant sentence and highlight pertinent fragments was generally good, but changes to sentence selection and/or highlighting were often required. Protocol https://doi.org/10.7939/DVN/RQPJKS

Download Full-text

Creating Efficiencies in the Extraction of Data From Randomized Trials: A Prospective Evaluation of a Machine Learning and Text Mining Tool

10.23970/ahrqepcmethodscreatingefficiencies ◽

2021 ◽

Author(s):

Allison Gates ◽

Michelle Gates ◽

Shannon Sim ◽

Sarah A. Elliott ◽

Jennifer Pillay ◽

...

Keyword(s):

Machine Learning ◽

Text Mining ◽

Randomized Trials ◽

Data Extraction ◽

Data Element ◽

Time Savings ◽

Mining Tool ◽

Data Elements ◽

Text Mining Tool ◽

Manual Extraction

Background. Machine learning tools that semi-automate data extraction may create efficiencies in systematic review production. We prospectively evaluated an online machine learning and text mining tool’s ability to (a) automatically extract data elements from randomized trials, and (b) save time compared with manual extraction and verification. Methods. For 75 randomized trials published in 2017, we manually extracted and verified data for 21 unique data elements. We uploaded the randomized trials to ExaCT, an online machine learning and text mining tool, and quantified performance by evaluating the tool’s ability to identify the reporting of data elements (reported or not reported), and the relevance of the extracted sentences, fragments, and overall solutions. For each randomized trial, we measured the time to complete manual extraction and verification, and to review and amend the data extracted by ExaCT (simulating semi-automated data extraction). We summarized the relevance of the extractions for each data element using counts and proportions, and calculated the median and interquartile range (IQR) across data elements. We calculated the median (IQR) time for manual and semiautomated data extraction, and overall time savings. Results. The tool identified the reporting (reported or not reported) of data elements with median (IQR) 91 percent (75% to 99%) accuracy. Performance was perfect for four data elements: eligibility criteria, enrolment end date, control arm, and primary outcome(s). Among the top five sentences for each data element at least one sentence was relevant in a median (IQR) 88 percent (83% to 99%) of cases. Performance was perfect for four data elements: funding number, registration number, enrolment start date, and route of administration. Among a median (IQR) 90 percent (86% to 96%) of relevant sentences, pertinent fragments had been highlighted by the system; exact matches were unreliable (median (IQR) 52 percent [32% to 73%]). A median 48 percent of solutions were fully correct, but performance varied greatly across data elements (IQR 21% to 71%). Using ExaCT to assist the first reviewer resulted in a modest time savings compared with manual extraction by a single reviewer (17.9 vs. 21.6 hours total extraction time across 75 randomized trials). Conclusions. Using ExaCT to assist with data extraction resulted in modest gains in efficiency compared with manual extraction. The tool was reliable for identifying the reporting of most data elements. The tool’s ability to identify at least one relevant sentence and highlight pertinent fragments was generally good, but changes to sentence selection and/or highlighting were often required.

Download Full-text

ProPheno 1.0: An online dataset for accelerating the complete characterization of the human protein-phenotype landscape in biomedical literature

10.7287/peerj.preprints.27479 ◽

2019 ◽

Author(s):

Morteza Pourreza Shahri ◽

Indika Kahanda

Keyword(s):

Open Access ◽

Text Mining ◽

Predictive Models ◽

Complete Characterization ◽

Biomedical Literature ◽

Human Protein ◽

Pubmed Central ◽

Mining Tool ◽

Text Mining Tool

Identifying protein-phenotype relations is of paramount importance for applications such as uncovering rare and complex diseases. One of the best resources that captures the protein-phenotype relationships is the biomedical literature. In this work, we introduce ProPheno, a comprehensive online dataset composed of human protein/phenotype mentions extracted from the complete corpora of Medline and PubMed Central Open Access. Moreover, it includes co-occurrences of protein-phenotype pairs within different spans of text such as sentences and paragraphs. We use ProPheno for completely characterizing the human protein-phenotype landscape in biomedical literature. ProPheno, the reported findings and the gained insight has implications for (1) biocurators for expediting their curation efforts, (2) researches for quickly finding relevant articles, and (3) text mining tool developers for training their predictive models. The RESTful API of ProPheno is freely available at http://propheno.cs.montana.edu.

Download Full-text

On expert curation and sustainability: UniProtKB/Swiss-Prot as a case study

10.1101/094011 ◽

2016 ◽

Cited By ~ 3

Author(s):

Sylvain Poux ◽

Cecilia N. Arighi ◽

Michele Magrane ◽

Alex Bateman ◽

Chih-Hsuan Wei ◽

...

Keyword(s):

Text Mining ◽

Scientific Community ◽

Large Fraction ◽

Scientific Research ◽

Biomedical Literature ◽

Complete Picture ◽

Essential Component ◽

Mining Tool ◽

Text Mining Tool

AbstractMOTIVATIONBiological knowledgebases, such as UniProtKB/Swiss-Prot, constitute an essential component of daily scientific research by offering distilled, summarized, and computable knowledge extracted from the literature by expert curators. While knowledgebases play an increasingly important role in the scientific community, the question of their sustainability is raised due to the growth of biomedical literature.RESULTSBy using UniProtKB/Swiss-Prot as a case study, we address this question by using different literature triage approaches. With the assistance of the PubTator text-mining tool, we tagged more than 10,000 articles to assess the ratio of papers relevant for curation. We first show that curators read and evaluate many more papers than they curate, and that measuring the number of curated publications is insufficient to provide a complete picture. We show that a large fraction of published papers found in PubMed is not relevant for curation in UniProtKB/Swiss-Prot and demonstrate that, despite appearances, expert curation is sustainable.AVAILABILITYUniProt is freely available at http://www.uniprot.org/[email protected]

Download Full-text

ProPheno 1.0: An online dataset for accelerating the complete characterization of the human protein-phenotype landscape in biomedical literature

10.7287/peerj.preprints.27479v2 ◽

2019 ◽

Cited By ~ 2

Author(s):

Morteza Pourreza Shahri ◽

Indika Kahanda

Keyword(s):

Open Access ◽

Text Mining ◽

Predictive Models ◽

Complete Characterization ◽

Biomedical Literature ◽

Human Protein ◽

Pubmed Central ◽

Mining Tool ◽

Text Mining Tool

Identifying protein-phenotype relations is of paramount importance for applications such as uncovering rare and complex diseases. One of the best resources that captures the protein-phenotype relationships is the biomedical literature. In this work, we introduce ProPheno, a comprehensive online dataset composed of human protein/phenotype mentions extracted from the complete corpora of Medline and PubMed Central Open Access. Moreover, it includes co-occurrences of protein-phenotype pairs within different spans of text such as sentences and paragraphs. We use ProPheno for completely characterizing the human protein-phenotype landscape in biomedical literature. ProPheno, the reported findings and the gained insight has implications for (1) biocurators for expediting their curation efforts, (2) researches for quickly finding relevant articles, and (3) text mining tool developers for training their predictive models. The RESTful API of ProPheno is freely available at http://propheno.cs.montana.edu.

Download Full-text

The Treasury Chest of Text Mining: Piling Available Resources for Powerful Biomedical Text Mining

BioChem ◽

10.3390/biochem1020007 ◽

2021 ◽

Vol 1 (2) ◽

pp. 60-80

Author(s):

Nícia Rosário-Ferreira ◽

Catarina Marques-Pereira ◽

Manuel Pires ◽

Daniel Ramalhão ◽

Nádia Pereira ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Text Mining ◽

Clinical Data ◽

Scientific Literature ◽

Structured Data ◽

Biomedical Literature ◽

Biomedical Text ◽

Biomedical Text Mining ◽

Step Process

Text mining (TM) is a semi-automatized, multi-step process, able to turn unstructured into structured data. TM relevance has increased upon machine learning (ML) and deep learning (DL) algorithms’ application in its various steps. When applied to biomedical literature, text mining is named biomedical text mining and its specificity lies in both the type of analyzed documents and the language and concepts retrieved. The array of documents that can be used ranges from scientific literature to patents or clinical data, and the biomedical concepts often include, despite not being limited to genes, proteins, drugs, and diseases. This review aims to gather the leading tools for biomedical TM, summarily describing and systematizing them. We also surveyed several resources to compile the most valuable ones for each category.

Download Full-text

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

10.31232/osf.io/4pxq2 ◽

2020 ◽

Author(s):

Saeed Nosratabadi ◽

Amir Mosavi ◽

Puhong Duan ◽

Pedram Ghamisi ◽

Ferdinand Filip ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Data Science ◽

State Of The Art ◽

Science Methods ◽

Learning Models ◽

Diverse Range ◽

Hybrid Machine ◽

Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Download Full-text