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Fabiano N, Gupta A, Bhambra N, Luu B, Wong S, Maaz M, Fiedorowicz JG, Smith AL, Solmi M. How to optimize the systematic review process using AI tools. JCPP ADVANCES 2024; 4:e12234. [PMID: 38827982 PMCID: PMC11143948 DOI: 10.1002/jcv2.12234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 12/18/2023] [Indexed: 06/05/2024] Open
Abstract
Systematic reviews are a cornerstone for synthesizing the available evidence on a given topic. They simultaneously allow for gaps in the literature to be identified and provide direction for future research. However, due to the ever-increasing volume and complexity of the available literature, traditional methods for conducting systematic reviews are less efficient and more time-consuming. Numerous artificial intelligence (AI) tools are being released with the potential to optimize efficiency in academic writing and assist with various stages of the systematic review process including developing and refining search strategies, screening titles and abstracts for inclusion or exclusion criteria, extracting essential data from studies and summarizing findings. Therefore, in this article we provide an overview of the currently available tools and how they can be incorporated into the systematic review process to improve efficiency and quality of research synthesis. We emphasize that authors must report all AI tools that have been used at each stage to ensure replicability as part of reporting in methods.
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Affiliation(s)
| | - Arnav Gupta
- Department of MedicineUniversity of CalgaryCalgaryAlbertaCanada
- College of Public HealthKent State UniversityKentOhioUSA
| | - Nishaant Bhambra
- Department of Family MedicineUniversity of OttawaOttawaOntarioCanada
| | - Brandon Luu
- Department of MedicineUniversity of TorontoTorontoOntarioCanada
| | - Stanley Wong
- Department of PsychiatryUniversity of TorontoTorontoOntarioCanada
| | - Muhammad Maaz
- Faculty of MedicineUniversity of TorontoTorontoOntarioCanada
- Department of Mechanical and Industrial EngineeringUniversity of TorontoTorontoOntarioCanada
| | - Jess G. Fiedorowicz
- Department of PsychiatryUniversity of OttawaOttawaOntarioCanada
- Department of Mental HealthThe Ottawa HospitalOttawaOntarioCanada
- Ottawa Hospital Research Institute (OHRI) Clinical Epidemiology ProgramUniversity of OttawaOttawaOntarioCanada
- School of Epidemiology and Public HealthFaculty of MedicineUniversity of OttawaOttawaOntarioCanada
| | - Andrew L. Smith
- Department of PsychiatryUniversity of OttawaOttawaOntarioCanada
- Department of Mental HealthThe Ottawa HospitalOttawaOntarioCanada
| | - Marco Solmi
- Department of PsychiatryUniversity of OttawaOttawaOntarioCanada
- Department of Mental HealthThe Ottawa HospitalOttawaOntarioCanada
- Ottawa Hospital Research Institute (OHRI) Clinical Epidemiology ProgramUniversity of OttawaOttawaOntarioCanada
- School of Epidemiology and Public HealthFaculty of MedicineUniversity of OttawaOttawaOntarioCanada
- Department of Child and Adolescent PsychiatryCharité ‐ Universitätsmedizin BerlinBerlinGermany
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2
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Gharavi E, LeRoy NJ, Zheng G, Zhang A, Brown DE, Sheffield NC. Joint Representation Learning for Retrieval and Annotation of Genomic Interval Sets. Bioengineering (Basel) 2024; 11:263. [PMID: 38534537 DOI: 10.3390/bioengineering11030263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 02/20/2024] [Accepted: 02/22/2024] [Indexed: 03/28/2024] Open
Abstract
As available genomic interval data increase in scale, we require fast systems to search them. A common approach is simple string matching to compare a search term to metadata, but this is limited by incomplete or inaccurate annotations. An alternative is to compare data directly through genomic region overlap analysis, but this approach leads to challenges like sparsity, high dimensionality, and computational expense. We require novel methods to quickly and flexibly query large, messy genomic interval databases. Here, we develop a genomic interval search system using representation learning. We train numerical embeddings for a collection of region sets simultaneously with their metadata labels, capturing similarity between region sets and their metadata in a low-dimensional space. Using these learned co-embeddings, we develop a system that solves three related information retrieval tasks using embedding distance computations: retrieving region sets related to a user query string, suggesting new labels for database region sets, and retrieving database region sets similar to a query region set. We evaluate these use cases and show that jointly learned representations of region sets and metadata are a promising approach for fast, flexible, and accurate genomic region information retrieval.
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Affiliation(s)
- Erfaneh Gharavi
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- School of Data Science, University of Virginia, Charlottesville, VA 22904, USA
| | - Nathan J LeRoy
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biomedical Engineering, School of Medicine, University of Virginia, Charlottesville, VA 22904, USA
| | - Guangtao Zheng
- Department of Computer Science, School of Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Aidong Zhang
- School of Data Science, University of Virginia, Charlottesville, VA 22904, USA
- Department of Biomedical Engineering, School of Medicine, University of Virginia, Charlottesville, VA 22904, USA
- Department of Computer Science, School of Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Donald E Brown
- School of Data Science, University of Virginia, Charlottesville, VA 22904, USA
- Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Nathan C Sheffield
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- School of Data Science, University of Virginia, Charlottesville, VA 22904, USA
- Department of Biomedical Engineering, School of Medicine, University of Virginia, Charlottesville, VA 22904, USA
- Department of Computer Science, School of Engineering, University of Virginia, Charlottesville, VA 22908, USA
- Department of Public Health Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Child Health Research Center, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
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3
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Wang L, Ambite JL, Appaji A, Bijsterbosch J, Dockes J, Herrick R, Kogan A, Lander H, Marcus D, Moore SM, Poline JB, Rajasekar A, Sahoo SS, Turner MD, Wang X, Wang Y, Turner JA. NeuroBridge: a prototype platform for discovery of the long-tail neuroimaging data. Front Neuroinform 2023; 17:1215261. [PMID: 37720825 PMCID: PMC10500076 DOI: 10.3389/fninf.2023.1215261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 08/01/2023] [Indexed: 09/19/2023] Open
Abstract
Introduction Open science initiatives have enabled sharing of large amounts of already collected data. However, significant gaps remain regarding how to find appropriate data, including underutilized data that exist in the long tail of science. We demonstrate the NeuroBridge prototype and its ability to search PubMed Central full-text papers for information relevant to neuroimaging data collected from schizophrenia and addiction studies. Methods The NeuroBridge architecture contained the following components: (1) Extensible ontology for modeling study metadata: subject population, imaging techniques, and relevant behavioral, cognitive, or clinical data. Details are described in the companion paper in this special issue; (2) A natural-language based document processor that leveraged pre-trained deep-learning models on a small-sample document corpus to establish efficient representations for each article as a collection of machine-recognized ontological terms; (3) Integrated search using ontology-driven similarity to query PubMed Central and NeuroQuery, which provides fMRI activation maps along with PubMed source articles. Results The NeuroBridge prototype contains a corpus of 356 papers from 2018 to 2021 describing schizophrenia and addiction neuroimaging studies, of which 186 were annotated with the NeuroBridge ontology. The search portal on the NeuroBridge website https://neurobridges.org/ provides an interactive Query Builder, where the user builds queries by selecting NeuroBridge ontology terms to preserve the ontology tree structure. For each return entry, links to the PubMed abstract as well as to the PMC full-text article, if available, are presented. For each of the returned articles, we provide a list of clinical assessments described in the Section "Methods" of the article. Articles returned from NeuroQuery based on the same search are also presented. Conclusion The NeuroBridge prototype combines ontology-based search with natural-language text-mining approaches to demonstrate that papers relevant to a user's research question can be identified. The NeuroBridge prototype takes a first step toward identifying potential neuroimaging data described in full-text papers. Toward the overall goal of discovering "enough data of the right kind," ongoing work includes validating the document processor with a larger corpus, extending the ontology to include detailed imaging data, and extracting information regarding data availability from the returned publications and incorporating XNAT-based neuroimaging databases to enhance data accessibility.
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Affiliation(s)
- Lei Wang
- Psychiatry and Behavioral Health Department, The Ohio State University Wexner Medical Center, Columbus, OH, United States
| | - José Luis Ambite
- Information Sciences Institute and Computer Science, University of Southern California, Los Angeles, CA, United States
| | - Abhishek Appaji
- Department of Medical Electronics Engineering, BMS College of Engineering, Bangalore, India
| | - Janine Bijsterbosch
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, United States
| | - Jerome Dockes
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Rick Herrick
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, United States
| | - Alex Kogan
- Psychiatry and Behavioral Health Department, The Ohio State University Wexner Medical Center, Columbus, OH, United States
| | - Howard Lander
- Renaissance Computing Institute, Chapel Hill, NC, United States
| | - Daniel Marcus
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, United States
| | - Stephen M. Moore
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, United States
| | - Jean-Baptiste Poline
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Arcot Rajasekar
- Renaissance Computing Institute, Chapel Hill, NC, United States
- School of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Satya S. Sahoo
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Matthew D. Turner
- Psychiatry and Behavioral Health Department, The Ohio State University Wexner Medical Center, Columbus, OH, United States
| | - Xiaochen Wang
- College of Information Sciences and Technology, Pennsylvania State University, State College, PA, United States
| | - Yue Wang
- School of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Jessica A. Turner
- Psychiatry and Behavioral Health Department, The Ohio State University Wexner Medical Center, Columbus, OH, United States
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4
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Guo M, Zhou Z, Gotz D, Wang Y. GRAFS: Graphical Faceted Search System to Support Conceptual Understanding in Exploratory Search. ACM T INTERACT INTEL 2023. [DOI: 10.1145/3588319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Abstract
When people search for information about a new topic within large document collections, they implicitly construct a mental model of the unfamiliar information space to represent what they currently know and guide their exploration into the unknown. Building this mental model can be challenging as it requires not only finding relevant documents, but also synthesizing important concepts and the relationships that connect those concepts both within and across documents. This paper describes a novel interactive approach designed to help users construct a mental model of an unfamiliar information space during exploratory search. We propose a new semantic search system to organize and visualize important concepts and their relations for a set of search results. A user study (
n
= 20) was conducted to compare the proposed approach against a baseline faceted search system on exploratory literature search tasks. Experimental results show that the proposed approach is more effective in helping users recognize relationships between key concepts, leading to a more sophisticated understanding of the search topic while maintaining similar functionality and usability as a faceted search system.
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5
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Wu H, Wang M, Wu J, Francis F, Chang YH, Shavick A, Dong H, Poon MTC, Fitzpatrick N, Levine AP, Slater LT, Handy A, Karwath A, Gkoutos GV, Chelala C, Shah AD, Stewart R, Collier N, Alex B, Whiteley W, Sudlow C, Roberts A, Dobson RJB. A survey on clinical natural language processing in the United Kingdom from 2007 to 2022. NPJ Digit Med 2022; 5:186. [PMID: 36544046 PMCID: PMC9770568 DOI: 10.1038/s41746-022-00730-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022] Open
Abstract
Much of the knowledge and information needed for enabling high-quality clinical research is stored in free-text format. Natural language processing (NLP) has been used to extract information from these sources at scale for several decades. This paper aims to present a comprehensive review of clinical NLP for the past 15 years in the UK to identify the community, depict its evolution, analyse methodologies and applications, and identify the main barriers. We collect a dataset of clinical NLP projects (n = 94; £ = 41.97 m) funded by UK funders or the European Union's funding programmes. Additionally, we extract details on 9 funders, 137 organisations, 139 persons and 431 research papers. Networks are created from timestamped data interlinking all entities, and network analysis is subsequently applied to generate insights. 431 publications are identified as part of a literature review, of which 107 are eligible for final analysis. Results show, not surprisingly, clinical NLP in the UK has increased substantially in the last 15 years: the total budget in the period of 2019-2022 was 80 times that of 2007-2010. However, the effort is required to deepen areas such as disease (sub-)phenotyping and broaden application domains. There is also a need to improve links between academia and industry and enable deployments in real-world settings for the realisation of clinical NLP's great potential in care delivery. The major barriers include research and development access to hospital data, lack of capable computational resources in the right places, the scarcity of labelled data and barriers to sharing of pretrained models.
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Affiliation(s)
- Honghan Wu
- Institute of Health Informatics, University College London, London, UK.
| | - Minhong Wang
- Institute of Health Informatics, University College London, London, UK
| | - Jinge Wu
- Institute of Health Informatics, University College London, London, UK
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Farah Francis
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Yun-Hsuan Chang
- Institute of Health Informatics, University College London, London, UK
| | - Alex Shavick
- Research Department of Pathology, UCL Cancer Institute, University College London, London, UK
| | - Hang Dong
- Usher Institute, University of Edinburgh, Edinburgh, UK
- Department of Computer Science, University of Oxford, Oxford, UK
| | | | | | - Adam P Levine
- Research Department of Pathology, UCL Cancer Institute, University College London, London, UK
| | - Luke T Slater
- Institute of Cancer and Genomics, University of Birmingham, Birmingham, UK
| | - Alex Handy
- Institute of Health Informatics, University College London, London, UK
- University College London Hospitals NHS Trust, London, UK
| | - Andreas Karwath
- Institute of Cancer and Genomics, University of Birmingham, Birmingham, UK
| | - Georgios V Gkoutos
- Institute of Cancer and Genomics, University of Birmingham, Birmingham, UK
| | - Claude Chelala
- Centre for Tumour Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | - Anoop Dinesh Shah
- Institute of Health Informatics, University College London, London, UK
| | - Robert Stewart
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - Nigel Collier
- Theoretical and Applied Linguistics, Faculty of Modern & Medieval Languages & Linguistics, University of Cambridge, Cambridge, UK
| | - Beatrice Alex
- Edinburgh Futures Institute, University of Edinburgh, Edinburgh, UK
| | | | - Cathie Sudlow
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Angus Roberts
- Department of Biostatistics & Health Informatics, King's College London, London, UK
| | - Richard J B Dobson
- Institute of Health Informatics, University College London, London, UK
- Department of Biostatistics & Health Informatics, King's College London, London, UK
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6
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Focsa M, Tan C, Chen M, Yan M, Zhang N, Huang S, Liu X. State-of-the-Art Evidence Retriever for Precision Medicine: Algorithm Development and Validation. JMIR Med Inform 2022; 10:e40743. [PMID: 36409468 PMCID: PMC9801267 DOI: 10.2196/40743] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 11/13/2022] [Accepted: 11/16/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Under the paradigm of precision medicine (PM), patients with the same disease can receive different personalized therapies according to their clinical and genetic features. These therapies are determined by the totality of all available clinical evidence, including results from case reports, clinical trials, and systematic reviews. However, it is increasingly difficult for physicians to find such evidence from scientific publications, whose size is growing at an unprecedented pace. OBJECTIVE In this work, we propose the PM-Search system to facilitate the retrieval of clinical literature that contains critical evidence for or against giving specific therapies to certain cancer patients. METHODS The PM-Search system combines a baseline retriever that selects document candidates at a large scale and an evidence reranker that finely reorders the candidates based on their evidence quality. The baseline retriever uses query expansion and keyword matching with the ElasticSearch retrieval engine, and the evidence reranker fits pretrained language models to expert annotations that are derived from an active learning strategy. RESULTS The PM-Search system achieved the best performance in the retrieval of high-quality clinical evidence at the Text Retrieval Conference PM Track 2020, outperforming the second-ranking systems by large margins (0.4780 vs 0.4238 for standard normalized discounted cumulative gain at rank 30 and 0.4519 vs 0.4193 for exponential normalized discounted cumulative gain at rank 30). CONCLUSIONS We present PM-Search, a state-of-the-art search engine to assist the practicing of evidence-based PM. 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. Our analyses show that evidence quality is a distinct aspect from general relevance, and specific modeling of evidence quality beyond general relevance is required for a PM search engine.
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Affiliation(s)
| | | | | | | | | | | | - Xiaozhong Liu
- Indiana University Bloomington, Bloomington, IN, United States
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7
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Xu Q, Liu Y, Hu J, Duan X, Song N, Zhou J, Zhai J, Su J, Liu S, Chen F, Zheng W, Guo Z, Li H, Zhou Q, Niu B. OncoPubMiner: a platform for mining oncology publications. Brief Bioinform 2022; 23:6691792. [PMID: 36058206 DOI: 10.1093/bib/bbac383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 08/08/2022] [Accepted: 08/09/2022] [Indexed: 11/12/2022] Open
Abstract
Updated and expert-quality knowledge bases are fundamental to biomedical research. A knowledge base established with human participation and subject to multiple inspections is needed to support clinical decision making, especially in the growing field of precision oncology. The number of original publications in this field has risen dramatically with the advances in technology and the evolution of in-depth research. Consequently, the issue of how to gather and mine these articles accurately and efficiently now requires close consideration. In this study, we present OncoPubMiner (https://oncopubminer.chosenmedinfo.com), a free and powerful system that combines text mining, data structure customisation, publication search with online reading and project-centred and team-based data collection to form a one-stop 'keyword in-knowledge out' oncology publication mining platform. The platform was constructed by integrating all open-access abstracts from PubMed and full-text articles from PubMed Central, and it is updated daily. OncoPubMiner makes obtaining precision oncology knowledge from scientific articles straightforward and will assist researchers in efficiently developing structured knowledge base systems and bring us closer to achieving precision oncology goals.
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Affiliation(s)
- Quan Xu
- ChosenMed Technology (Beijing) Company Limited, Jinghai Industrial Park, Economic and Technological Development Area, Beijing 100176, China
| | - Yueyue Liu
- ChosenMed Technology (Beijing) Company Limited, Jinghai Industrial Park, Economic and Technological Development Area, Beijing 100176, China.,ChosenMed Gene Technology Co. Ltd., Nanjing, China
| | - Jifang Hu
- ChosenMed Technology (Beijing) Company Limited, Jinghai Industrial Park, Economic and Technological Development Area, Beijing 100176, China.,Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100190, China
| | - Xiaohong Duan
- ChosenMed Technology (Beijing) Company Limited, Jinghai Industrial Park, Economic and Technological Development Area, Beijing 100176, China.,ChosenMed Gene Technology Co. Ltd., Nanjing, China
| | - Niuben Song
- ChosenMed Technology (Beijing) Company Limited, Jinghai Industrial Park, Economic and Technological Development Area, Beijing 100176, China
| | - Jiale Zhou
- ChosenMed Technology (Beijing) Company Limited, Jinghai Industrial Park, Economic and Technological Development Area, Beijing 100176, China
| | - Jincheng Zhai
- ChosenMed Technology (Beijing) Company Limited, Jinghai Industrial Park, Economic and Technological Development Area, Beijing 100176, China
| | - Junyan Su
- ChosenMed Technology (Beijing) Company Limited, Jinghai Industrial Park, Economic and Technological Development Area, Beijing 100176, China
| | - Siyao Liu
- ChosenMed Technology (Beijing) Company Limited, Jinghai Industrial Park, Economic and Technological Development Area, Beijing 100176, China
| | - Fan Chen
- ChosenMed Technology (Beijing) Company Limited, Jinghai Industrial Park, Economic and Technological Development Area, Beijing 100176, China.,ChosenMed Gene Technology Co. Ltd., Nanjing, China
| | - Wei Zheng
- The Department of Nephrology and Hypertension Medicine, Beijing Electric Power Hospital, Beijing 100073, China
| | - Zhongjia Guo
- ChosenMed Technology (Beijing) Company Limited, Jinghai Industrial Park, Economic and Technological Development Area, Beijing 100176, China
| | - Hexiang Li
- ChosenMed Technology (Beijing) Company Limited, Jinghai Industrial Park, Economic and Technological Development Area, Beijing 100176, China
| | - Qiming Zhou
- ChosenMed Technology (Beijing) Company Limited, Jinghai Industrial Park, Economic and Technological Development Area, Beijing 100176, China.,ChosenMed Gene Technology Co. Ltd., Nanjing, China
| | - Beifang Niu
- ChosenMed Technology (Beijing) Company Limited, Jinghai Industrial Park, Economic and Technological Development Area, Beijing 100176, China.,Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100190, China
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8
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Waldrop AM, Cheadle JB, Bradford K, Preiss A, Chew R, Holt JR, Kebede Y, Braswell N, Watson M, Hench V, Crerar A, Ball CM, Schreep C, Linebaugh PJ, Hiles H, Boyles R, Bizon C, Krishnamurthy A, Cox S. Dug: a semantic search engine leveraging peer-reviewed knowledge to query biomedical data repositories. Bioinformatics 2022; 38:3252-3258. [PMID: 35441678 PMCID: PMC9991886 DOI: 10.1093/bioinformatics/btac284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 03/04/2022] [Accepted: 04/15/2022] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION As the number of public data resources continues to proliferate, identifying relevant datasets across heterogenous repositories is becoming critical to answering scientific questions. To help researchers navigate this data landscape, we developed Dug: a semantic search tool for biomedical datasets utilizing evidence-based relationships from curated knowledge graphs to find relevant datasets and explain why those results are returned. RESULTS Developed through the National Heart, Lung and Blood Institute's (NHLBI) BioData Catalyst ecosystem, Dug has indexed more than 15 911 study variables from public datasets. On a manually curated search dataset, Dug's total recall (total relevant results/total results) of 0.79 outperformed default Elasticsearch's total recall of 0.76. When using synonyms or related concepts as search queries, Dug (0.36) far outperformed Elasticsearch (0.14) in terms of total recall with no significant loss in the precision of its top results. AVAILABILITY AND IMPLEMENTATION Dug is freely available at https://github.com/helxplatform/dug. An example Dug deployment is also available for use at https://search.biodatacatalyst.renci.org/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Alexander M Waldrop
- Center for Genomics, Bioinformatics, and Translational Research, RTI International, Research Triangle Park, NC 27709-2194, USA
| | - John B Cheadle
- Research Computing Division, RTI International, Research Triangle Park, NC 27709-2194, USA
| | - Kira Bradford
- Renaissance Computing Institute, University of Chapel Hill, North Carolina, Chapel Hill, NC 27599-7568, USA
| | - Alexander Preiss
- Center for Data Science, RTI International, Research Triangle Park, NC 27709-2194, USA
| | - Robert Chew
- Center for Data Science, RTI International, Research Triangle Park, NC 27709-2194, USA
| | - Jonathan R Holt
- Center for Data Science, RTI International, Research Triangle Park, NC 27709-2194, USA
| | - Yaphet Kebede
- Renaissance Computing Institute, University of Chapel Hill, North Carolina, Chapel Hill, NC 27599-7568, USA
| | - Nathan Braswell
- Research Computing Division, RTI International, Research Triangle Park, NC 27709-2194, USA
| | - Matt Watson
- Renaissance Computing Institute, University of Chapel Hill, North Carolina, Chapel Hill, NC 27599-7568, USA
| | - Virginia Hench
- Center for Genomics, Bioinformatics, and Translational Research, RTI International, Research Triangle Park, NC 27709-2194, USA
| | - Andrew Crerar
- Center for Genomics, Bioinformatics, and Translational Research, RTI International, Research Triangle Park, NC 27709-2194, USA
| | - Chris M Ball
- Research Computing Division, RTI International, Research Triangle Park, NC 27709-2194, USA
| | - Carl Schreep
- Renaissance Computing Institute, University of Chapel Hill, North Carolina, Chapel Hill, NC 27599-7568, USA
| | - P J Linebaugh
- Renaissance Computing Institute, University of Chapel Hill, North Carolina, Chapel Hill, NC 27599-7568, USA
| | - Hannah Hiles
- Renaissance Computing Institute, University of Chapel Hill, North Carolina, Chapel Hill, NC 27599-7568, USA
| | - Rebecca Boyles
- Research Computing Division, RTI International, Research Triangle Park, NC 27709-2194, USA
| | - Chris Bizon
- Renaissance Computing Institute, University of Chapel Hill, North Carolina, Chapel Hill, NC 27599-7568, USA
| | - Ashok Krishnamurthy
- Renaissance Computing Institute, University of Chapel Hill, North Carolina, Chapel Hill, NC 27599-7568, USA.,Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7548, USA
| | - Steve Cox
- Renaissance Computing Institute, University of Chapel Hill, North Carolina, Chapel Hill, NC 27599-7568, USA
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9
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Gunturkun MH, Flashner E, Wang T, Mulligan MK, Williams RW, Prins P, Chen H. GeneCup: mining PubMed and GWAS catalog for gene-keyword relationships. G3 (BETHESDA, MD.) 2022; 12:jkac059. [PMID: 35285473 PMCID: PMC9073678 DOI: 10.1093/g3journal/jkac059] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 03/04/2022] [Indexed: 11/13/2022]
Abstract
Interpreting and integrating results from omics studies typically requires a comprehensive and time consuming survey of extant literature. GeneCup is a literature mining web service that retrieves sentences containing user-provided gene symbols and keywords from PubMed abstracts. The keywords are organized into an ontology and can be extended to include results from human genome-wide association studies. We provide a drug addiction keyword ontology that contains over 300 keywords as an example. The literature search is conducted by querying the PubMed server using a programming interface, which is followed by retrieving abstracts from a local copy of the PubMed archive. The main results presented to the user are sentences where gene symbol and keywords co-occur. These sentences are presented through an interactive graphical interface or as tables. All results are linked to the original abstract in PubMed. In addition, a convolutional neural network is employed to distinguish sentences describing systemic stress from those describing cellular stress. The automated and comprehensive search strategy provided by GeneCup facilitates the integration of new discoveries from omic studies with existing literature. GeneCup is free and open source software. The source code of GeneCup and the link to a running instance is available at https://github.com/hakangunturkun/GeneCup.
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Affiliation(s)
- Mustafa H Gunturkun
- Department of Pharmacology, Addiction Science and Toxicology, University of Tennessee Health Science, Memphis, TN 38103, USA
| | - Efraim Flashner
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science, Memphis, TN 38103, USA
| | - Tengfei Wang
- Department of Pharmacology, Addiction Science and Toxicology, University of Tennessee Health Science, Memphis, TN 38103, USA
| | - Megan K Mulligan
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science, Memphis, TN 38103, USA
| | - Robert W Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science, Memphis, TN 38103, USA
| | - Pjotr Prins
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science, Memphis, TN 38103, USA
| | - Hao Chen
- Department of Pharmacology, Addiction Science and Toxicology, University of Tennessee Health Science, Memphis, TN 38103, USA
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10
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Restrepo S, ter Horst E, Zambrano JD, Gunn LH, Molina G, Salazar CA. Hierarchical Bayesian classification methods to identify topics by journal quartile with an application in biological sciences. EDUCATION FOR INFORMATION 2022. [DOI: 10.3233/efi-211546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
This manuscript builds on a novel, automatic, freely-available Bayesian approach to extract information in abstracts and titles to classify research topics by quartile. This approach is demonstrated for all N= 149,129 ISI-indexed publications in biological sciences journals during 2017. A Bayesian multinomial inverse regression approach is used to extract rankings of topics without the need of a pre-defined dictionary. Bigrams are used for extraction of research topics across manuscripts, and rankings of research topics are constructed by quartile. Worldwide and local results (e.g., comparison between two peer/aspirational research institutions in Colombia) are provided, and differences are explored both at the global and local levels. Some topics persist across quartiles, while the relevance of others is quartile-specific. Challenges in sustainable development appear as more prevalent in top quartile journals across institutions, while the two Colombian institutions favour plant and microorganism research. This approach can reduce information inequities, by allowing young/incipient researchers in biological sciences, especially within lower income countries or universities with limited resources, to freely assess the state of the literature and the relative likelihood of publication in higher impact journals by research topic. This can also serve institutions of higher education to identify missing research topics and areas of competitive advantage.
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Affiliation(s)
| | | | | | - Laura H. Gunn
- University of North Carolina at Charlotte & Imperial College London, USA
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11
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Zerva C, Taylor S, Soto AJ, Nguyen NTH, Ananiadou S. A term-based and citation network-based search system for COVID-19. JAMIA Open 2021; 4:ooab104. [PMID: 34927002 PMCID: PMC8672931 DOI: 10.1093/jamiaopen/ooab104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 11/15/2021] [Accepted: 11/24/2021] [Indexed: 11/14/2022] Open
Abstract
The COVID-19 pandemic resulted in an unprecedented production of scientific
literature spanning several fields. To facilitate navigation of the scientific
literature related to various aspects of the pandemic, we developed an
exploratory search system. The system is based on automatically identified
technical terms, document citations, and their visualization, accelerating
identification of relevant documents. It offers a multi-view interactive search
and navigation interface, bringing together unsupervised approaches of term
extraction and citation analysis. We conducted a user evaluation with domain
experts, including epidemiologists, biochemists, medicinal chemists, and
medicine students. In general, most users were satisfied with the relevance and
speed of the search results. More interestingly, participants mostly agreed on
the capacity of the system to enable exploration and discovery of the search
space using the graph visualization and filters. The system is updated on a
weekly basis and it is publicly available at http://www.nactem.ac.uk/cord/. In this article, we present a search system and exploratory tool built on the
documents of the COVID-19 Open Research Dataset, which is a large and open
collection of scholarly articles related to COVID-19 (Coronavirus disease 2019),
SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus-2), and related
coronaviruses. The search system aims to facilitate navigation of the scientific
literature related to various aspects of the pandemic. Specifically, we identify
3 types of core information per paper to be used as navigation facets including
technical terminologies, citation/reference links from 1 paper to others, and
bibliometric data. Unlike other exploratory-based search engines, our system
allows users to combine information from text mining and bibliometrics analysis
to explore the data in a more versatile manner tailored to their needs. The
system is automatically updated on a weekly basis to ensure timely and updated
access to recent information. We also conducted a user evaluation that included
epidemiologists, biochemists, medicinal chemists, and medicine students. In
general, most users were satisfied with the relevance and speed of the search
results. More interestingly, participants mostly agreed on the capacity of the
system to enable exploration and discovery of the search space using the graph
visualization and filters.
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Affiliation(s)
- Chrysoula Zerva
- Department of Computer Science, National Centre for Text Mining, Manchester Interdisciplinary Biocentre, The University of Manchester, Manchester, UK.,Chrysoula Zerva's affiliation at the time of submission/publicationis is Instituto de Telecomunicações (IT), Lisbon, Portugal. All work was carried out while the author was employed at the University of Manchester, UK
| | - Samuel Taylor
- Department of Computer Science, National Centre for Text Mining, Manchester Interdisciplinary Biocentre, The University of Manchester, Manchester, UK
| | - Axel J Soto
- Department of Computer Science and Engineering, Universidad Nacional del Sur & Institute for Computer Science and Engineering (ICIC, UNS-CONICET), Bahia Blanca, Argentina
| | - Nhung T H Nguyen
- Department of Computer Science, National Centre for Text Mining, Manchester Interdisciplinary Biocentre, The University of Manchester, Manchester, UK
| | - Sophia Ananiadou
- Department of Computer Science, National Centre for Text Mining, Manchester Interdisciplinary Biocentre, The University of Manchester, Manchester, UK.,The Alan Turing Institute, London, UK
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12
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Mante J, Hao Y, Jett J, Joshi U, Keating K, Lu X, Nakum G, Rodriguez NE, Tang J, Terry L, Wu X, Yu E, Downie JS, McInnes BT, Nguyen MH, Sepulvado B, Young EM, Myers CJ. Synthetic Biology Knowledge System. ACS Synth Biol 2021; 10:2276-2285. [PMID: 34387462 DOI: 10.1021/acssynbio.1c00188] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The Synthetic Biology Knowledge System (SBKS) is an instance of the SynBioHub repository that includes text and data information that has been mined from papers published in ACS Synthetic Biology. This paper describes the SBKS curation framework that is being developed to construct the knowledge stored in this repository. The text mining pipeline performs automatic annotation of the articles using natural language processing techniques to identify salient content such as key terms, relationships between terms, and main topics. The data mining pipeline performs automatic annotation of the sequences extracted from the supplemental documents with the genetic parts used in them. Together these two pipelines link genetic parts to papers describing the context in which they are used. Ultimately, SBKS will reduce the time necessary for synthetic biologists to find the information necessary to complete their designs.
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Affiliation(s)
- Jeanet Mante
- University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Yikai Hao
- University of California San Diego, La Jolla, California 92093, United States
| | - Jacob Jett
- University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Udayan Joshi
- University of California San Diego, La Jolla, California 92093, United States
| | - Kevin Keating
- Worcester Polytechnic Institute, Worcester, Massachusettes 01609, United States
| | - Xiang Lu
- University of California San Diego, La Jolla, California 92093, United States
| | - Gaurav Nakum
- University of California San Diego, La Jolla, California 92093, United States
| | | | - Jiawei Tang
- University of California San Diego, La Jolla, California 92093, United States
| | - Logan Terry
- University of Utah, Salt Lake City, Utah 84112, United States
| | - Xuanyu Wu
- University of California San Diego, La Jolla, California 92093, United States
| | - Eric Yu
- University of Utah, Salt Lake City, Utah 84112, United States
| | - J. Stephen Downie
- University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Bridget T. McInnes
- Virginia Commonwealth University, Richmond, Virginia 23284, United States
| | - Mai H. Nguyen
- University of California San Diego, La Jolla, California 92093, United States
| | - Brandon Sepulvado
- NORC at the University of Chicago Bethesda, Chicago, Illinois 60637, United States
| | - Eric M. Young
- Worcester Polytechnic Institute, Worcester, Massachusettes 01609, United States
| | - Chris J. Myers
- University of Colorado Boulder, Boulder, Colorado 80309, United States
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13
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Robledo S, Grisales Aguirre AM, Hughes M, Eggers F. “Hasta la vista, baby” – will machine learning terminate human literature reviews in entrepreneurship? JOURNAL OF SMALL BUSINESS MANAGEMENT 2021. [DOI: 10.1080/00472778.2021.1955125] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Sebastian Robledo
- Faculty of Management Science, Universidad Católica Luis Amigó, Program of Management, Colombia
- Centro de Bioinformática y Biología Computacional de Colombia, BIOS, Ecoparque Los Yarumos, Edificio BIOS Manizales, Colombia
| | - Andrés Mauricio Grisales Aguirre
- Basic Sciences, Faculty of Management Science, Program of Management, Universidad Católica Luis Amigó, Colombia
- Mathematical Sciences, Caldas University, Colombia
| | - Mathew Hughes
- School of Business and Economics, Loughborough University, UK
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14
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Cognitive analysis of metabolomics data for systems biology. Nat Protoc 2021; 16:1376-1418. [PMID: 33483720 DOI: 10.1038/s41596-020-00455-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 10/27/2020] [Indexed: 01/30/2023]
Abstract
Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. Most available platforms have been developed for biomedical researchers, but new NLP tools are emerging for biologists in other fields and an important example is metabolomics. NLP provides literature-based contextualization of metabolic features that decreases the time and expert-level subject knowledge required during the prioritization, identification and interpretation steps in the metabolomics data analysis pipeline. Here, we describe and demonstrate four workflows that combine metabolomics data with NLP-based literature searches of scientific databases to aid in the analysis of metabolomics data and their biological interpretation. The four procedures can be used in isolation or consecutively, depending on the research questions. The first, used for initial metabolite annotation and prioritization, creates a list of metabolites that would be interesting for follow-up. The second workflow finds literature evidence of the activity of metabolites and metabolic pathways in governing the biological condition on a systems biology level. The third is used to identify candidate biomarkers, and the fourth looks for metabolic conditions or drug-repurposing targets that the two diseases have in common. The protocol can take 1-4 h or more to complete, depending on the processing time of the various software used.
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15
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Wang QC, Wang ZY. Big Data and Atrial Fibrillation: Current Understanding and New Opportunities. J Cardiovasc Transl Res 2020; 13:944-952. [PMID: 32378163 DOI: 10.1007/s12265-020-10008-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 04/16/2020] [Indexed: 10/24/2022]
Abstract
Atrial fibrillation (AF) is the most common arrhythmia with diverse etiology that remarkably relates to high morbidity and mortality. With the advancements in intensive clinical and basic research, the understanding of electrophysiological and pathophysiological mechanism, as well as treatment of AF have made huge progress. However, many unresolved issues remain, including the core mechanisms and key intervention targets. Big data approach has produced new insights into the improvement of the situation. A large amount of data have been accumulated in the field of AF research, thus using the big data to achieve prevention and precise treatment of AF may be the direction of future development. In this review, we will discuss the current understanding of big data and explore the potential applications of big data in AF research, including predictive models of disease processes, disease heterogeneity, drug safety and development, precision medicine, and the potential source for big data acquisition. Grapical abstract.
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Affiliation(s)
- Qian-Chen Wang
- Department of Cardiovascular Medicine, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, China
| | - Zhen-Yu Wang
- Department of Cardiovascular Medicine, the Second Xiangya Hospital, Central South University, No.139 Renmin Road, Changsha, Hunan, China.
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16
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Abstract
OBJECTIVE To determine how the representation of women's health has changed in clinical studies over the course of 70 years. DESIGN Observational study of 71 866 research articles published between 1948 and 2018 in The BMJ. MAIN OUTCOME MEASURES The incidence of women-specific health topics over time. General linear, additive and segmented regression models were used to estimate trends. RESULTS Over 70 years, the overall odds that a word in a BMJ research article was 'woman' or 'women' increased by an annual factor of 1.023, but this rate of increase varied by clinical specialty with some showing little or no change. The odds that an article was about some aspect of women-specific health increased much more slowly, by an annual factor of 1.004. The incidence of articles about particular areas of women-specific medicine such as pregnancy did not show a general increase, but rather fluctuated over time. The incidence of articles making any mention of women, gender or sex declined between 1948 and 2005, after which it rose steeply so that by 2018 few papers made no mention of them at all. CONCLUSIONS Over time women have become ever more prominent in BMJ research articles. However, the importance of women-specific health topics has waxed and waned as researchers responded ephemerally to medical advances, public health programmes, and sociolegal changes. The appointment of a woman editor-inchief in 2005 may have had a dramatic effect on whether women were mentioned in research articles.
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Affiliation(s)
- Eva N Hamulyák
- Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Austin J Brockmeier
- Department of Electrical & Computer Engineering, University of Delaware, Newark, Delaware, USA
| | - Johanna D Killas
- Health Studies Programme, University of Toronto, Toronto, Ontario, Canada
| | - Sophia Ananiadou
- Department of Computer Science, The University of Manchester National Centre for Text Mining, Manchester, UK
- The Alan Turing Institute, London, UK
| | - Saskia Middeldorp
- Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Armand M Leroi
- Department of Life Sciences, Imperial College London, South Kensington Campus, London, UK
- Data Science Institute, Imperial College London, South Kensington Campus, London, UK
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17
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Wei CH, Allot A, Leaman R, Lu Z. PubTator central: automated concept annotation for biomedical full text articles. Nucleic Acids Res 2020; 47:W587-W593. [PMID: 31114887 DOI: 10.1093/nar/gkz389] [Citation(s) in RCA: 188] [Impact Index Per Article: 47.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2019] [Revised: 04/08/2019] [Accepted: 04/30/2019] [Indexed: 11/12/2022] Open
Abstract
PubTator Central (https://www.ncbi.nlm.nih.gov/research/pubtator/) is a web service for viewing and retrieving bioconcept annotations in full text biomedical articles. PubTator Central (PTC) provides automated annotations from state-of-the-art text mining systems for genes/proteins, genetic variants, diseases, chemicals, species and cell lines, all available for immediate download. PTC annotates PubMed (29 million abstracts) and the PMC Text Mining subset (3 million full text articles). The new PTC web interface allows users to build full text document collections and visualize concept annotations in each document. Annotations are downloadable in multiple formats (XML, JSON and tab delimited) via the online interface, a RESTful web service and bulk FTP. Improved concept identification systems and a new disambiguation module based on deep learning increase annotation accuracy, and the new server-side architecture is significantly faster. PTC is synchronized with PubMed and PubMed Central, with new articles added daily. The original PubTator service has served annotated abstracts for ∼300 million requests, enabling third-party research in use cases such as biocuration support, gene prioritization, genetic disease analysis, and literature-based knowledge discovery. We demonstrate the full text results in PTC significantly increase biomedical concept coverage and anticipate this expansion will both enhance existing downstream applications and enable new use cases.
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Affiliation(s)
- Chih-Hsuan Wei
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Alexis Allot
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Robert Leaman
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA
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18
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Bugnon LA, Yones C, Raad J, Gerard M, Rubiolo M, Merino G, Pividori M, Di Persia L, Milone DH, Stegmayer G. DL4papers: a deep learning approach for the automatic interpretation of scientific articles. Bioinformatics 2020; 36:3499-3506. [DOI: 10.1093/bioinformatics/btaa111] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 12/27/2019] [Accepted: 02/14/2020] [Indexed: 01/26/2023] Open
Abstract
Abstract
Motivation
In precision medicine, next-generation sequencing and novel preclinical reports have led to an increasingly large amount of results, published in the scientific literature. However, identifying novel treatments or predicting a drug response in, for example, cancer patients, from the huge amount of papers available remains a laborious and challenging work. This task can be considered a text mining problem that requires reading a lot of academic documents for identifying a small set of papers describing specific relations between key terms. Due to the infeasibility of the manual curation of these relations, computational methods that can automatically identify them from the available literature are urgently needed.
Results
We present DL4papers, a new method based on deep learning that is capable of analyzing and interpreting papers in order to automatically extract relevant relations between specific keywords. DL4papers receives as input a query with the desired keywords, and it returns a ranked list of papers that contain meaningful associations between the keywords. The comparison against related methods showed that our proposal outperformed them in a cancer corpus. The reliability of the DL4papers output list was also measured, revealing that 100% of the first two documents retrieved for a particular search have relevant relations, in average. This shows that our model can guarantee that in the top-2 papers of the ranked list, the relation can be effectively found. Furthermore, the model is capable of highlighting, within each document, the specific fragments that have the associations of the input keywords. This can be very useful in order to pay attention only to the highlighted text, instead of reading the full paper. We believe that our proposal could be used as an accurate tool for rapidly identifying relationships between genes and their mutations, drug responses and treatments in the context of a certain disease. This new approach can certainly be a very useful and valuable resource for the advancement of the precision medicine field.
Availability and implementation
A web-demo is available at: http://sinc.unl.edu.ar/web-demo/dl4papers/. Full source code and data are available at: https://sourceforge.net/projects/sourcesinc/files/dl4papers/.
Contact
lbugnon@sinc.unl.edu.ar
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- L A Bugnon
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH/UNL-CONICET, Ciudad Universitaria, Santa Fe 3000, Argentina
| | - C Yones
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH/UNL-CONICET, Ciudad Universitaria, Santa Fe 3000, Argentina
| | - J Raad
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH/UNL-CONICET, Ciudad Universitaria, Santa Fe 3000, Argentina
| | - M Gerard
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH/UNL-CONICET, Ciudad Universitaria, Santa Fe 3000, Argentina
| | - M Rubiolo
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH/UNL-CONICET, Ciudad Universitaria, Santa Fe 3000, Argentina
| | - G Merino
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH/UNL-CONICET, Ciudad Universitaria, Santa Fe 3000, Argentina
- Bioengineering and Bioinformatics Research and Development Institute, IBB, FIUNER-CONICET, Ruta Prov 11, Km 10.5, Oro Verde 3100, Argentina
| | - M Pividori
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH/UNL-CONICET, Ciudad Universitaria, Santa Fe 3000, Argentina
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - L Di Persia
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH/UNL-CONICET, Ciudad Universitaria, Santa Fe 3000, Argentina
| | - D H Milone
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH/UNL-CONICET, Ciudad Universitaria, Santa Fe 3000, Argentina
| | - G Stegmayer
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH/UNL-CONICET, Ciudad Universitaria, Santa Fe 3000, Argentina
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19
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Marshall IJ, Johnson BT, Wang Z, Rajasekaran S, Wallace BC. Semi-Automated evidence synthesis in health psychology: current methods and future prospects. Health Psychol Rev 2020; 14:145-158. [PMID: 31941434 DOI: 10.1080/17437199.2020.1716198] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The evidence base in health psychology is vast and growing rapidly. These factors make it difficult (and sometimes practically impossible) to consider all available evidence when making decisions about the state of knowledge on a given phenomenon (e.g., associations of variables, effects of interventions on particular outcomes). Systematic reviews, meta-analyses, and other rigorous syntheses of the research mitigate this problem by providing concise, actionable summaries of knowledge in a given area of study. Yet, conducting these syntheses has grown increasingly laborious owing to the fast accumulation of new evidence; existing, manual methods for synthesis do not scale well. In this article, we discuss how semi-automation via machine learning and natural language processing methods may help researchers and practitioners to review evidence more efficiently. We outline concrete examples in health psychology, highlighting practical, open-source technologies available now. We indicate the potential of more advanced methods and discuss how to avoid the pitfalls of automated reviews.
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Affiliation(s)
- Iain J Marshall
- Population Health Sciences, King's College London - Strand Campus, London, United Kingdom of Great Britain and Northern Ireland
| | - Blair T Johnson
- Psychological Sciences, University of Connecticut, Storrs, CT, USA
| | - Zigeng Wang
- Computer Sciences, University of Connecticut, Storrs, CT, USA
| | | | - Byron C Wallace
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
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20
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Zeiss CJ, Shin D, Vander Wyk B, Beck AP, Zatz N, Sneiderman CA, Kilicoglu H. Menagerie: A text-mining tool to support animal-human translation in neurodegeneration research. PLoS One 2019; 14:e0226176. [PMID: 31846471 PMCID: PMC6917268 DOI: 10.1371/journal.pone.0226176] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 11/19/2019] [Indexed: 02/06/2023] Open
Abstract
Discovery studies in animals constitute a cornerstone of biomedical research, but suffer from lack of generalizability to human populations. We propose that large-scale interrogation of these data could reveal patterns of animal use that could narrow the translational divide. We describe a text-mining approach that extracts translationally useful data from PubMed abstracts. These comprise six modules: species, model, genes, interventions/disease modifiers, overall outcome and functional outcome measures. Existing National Library of Medicine natural language processing tools (SemRep, GNormPlus and the Chemical annotator) underpin the program and are further augmented by various rules, term lists, and machine learning models. Evaluation of the program using a 98-abstract test set achieved F1 scores ranging from 0.75-0.95 across all modules, and exceeded F1 scores obtained from comparable baseline programs. Next, the program was applied to a larger 14,481 abstract data set (2008-2017). Expected and previously identified patterns of species and model use for the field were obtained. As previously noted, the majority of studies reported promising outcomes. Longitudinal patterns of intervention type or gene mentions were demonstrated, and patterns of animal model use characteristic of the Parkinson's disease field were confirmed. The primary function of the program is to overcome low external validity of animal model systems by aggregating evidence across a diversity of models that capture different aspects of a multifaceted cellular process. Some aspects of the tool are generalizable, whereas others are field-specific. In the initial version presented here, we demonstrate proof of concept within a single disease area, Parkinson's disease. However, the program can be expanded in modular fashion to support a wider range of neurodegenerative diseases.
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Affiliation(s)
- Caroline J. Zeiss
- Department of Comparative Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
- * E-mail:
| | - Dongwook Shin
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, Maryland, United States of America
| | - Brent Vander Wyk
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
| | - Amanda P. Beck
- Department of Pathology, Albert Einstein College of Medicine, New York, United States of America
| | - Natalie Zatz
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut, United States of America
| | - Charles A. Sneiderman
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, Maryland, United States of America
| | - Halil Kilicoglu
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, Maryland, United States of America
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21
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Brockmeier AJ, Ju M, Przybyła P, Ananiadou S. Improving reference prioritisation with PICO recognition. BMC Med Inform Decis Mak 2019; 19:256. [PMID: 31805934 PMCID: PMC6896258 DOI: 10.1186/s12911-019-0992-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Accepted: 11/22/2019] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Machine learning can assist with multiple tasks during systematic reviews to facilitate the rapid retrieval of relevant references during screening and to identify and extract information relevant to the study characteristics, which include the PICO elements of patient/population, intervention, comparator, and outcomes. The latter requires techniques for identifying and categorising fragments of text, known as named entity recognition. METHODS A publicly available corpus of PICO annotations on biomedical abstracts is used to train a named entity recognition model, which is implemented as a recurrent neural network. This model is then applied to a separate collection of abstracts for references from systematic reviews within biomedical and health domains. The occurrences of words tagged in the context of specific PICO contexts are used as additional features for a relevancy classification model. Simulations of the machine learning-assisted screening are used to evaluate the work saved by the relevancy model with and without the PICO features. Chi-squared and statistical significance of positive predicted values are used to identify words that are more indicative of relevancy within PICO contexts. RESULTS Inclusion of PICO features improves the performance metric on 15 of the 20 collections, with substantial gains on certain systematic reviews. Examples of words whose PICO context are more precise can explain this increase. CONCLUSIONS Words within PICO tagged segments in abstracts are predictive features for determining inclusion. Combining PICO annotation model into the relevancy classification pipeline is a promising approach. The annotations may be useful on their own to aid users in pinpointing necessary information for data extraction, or to facilitate semantic search.
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Affiliation(s)
- Austin J. Brockmeier
- National Centre of Text Mining, School of Computer Science, University of Manchester, Princess Street, Manchester, M1 7DN UK
- University of Delaware, 139 The Green, Newark, Delaware, 19716 USA
| | - Meizhi Ju
- National Centre of Text Mining, School of Computer Science, University of Manchester, Princess Street, Manchester, M1 7DN UK
| | - Piotr Przybyła
- National Centre of Text Mining, School of Computer Science, University of Manchester, Princess Street, Manchester, M1 7DN UK
- Linguistic Engineering Group, Institute of Computer Science, Polish Academy of Sciences, Jana Kazimierza 5, Warszawa, 01-248 Poland
| | - Sophia Ananiadou
- National Centre of Text Mining, School of Computer Science, University of Manchester, Princess Street, Manchester, M1 7DN UK
- The Alan Turing Institute, 96 Euston Road, London, NW1 2DB UK
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Marshall IJ, Wallace BC. Toward systematic review automation: a practical guide to using machine learning tools in research synthesis. Syst Rev 2019; 8:163. [PMID: 31296265 PMCID: PMC6621996 DOI: 10.1186/s13643-019-1074-9] [Citation(s) in RCA: 202] [Impact Index Per Article: 40.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Accepted: 06/24/2019] [Indexed: 11/10/2022] Open
Abstract
Technologies and methods to speed up the production of systematic reviews by reducing the manual labour involved have recently emerged. Automation has been proposed or used to expedite most steps of the systematic review process, including search, screening, and data extraction. However, how these technologies work in practice and when (and when not) to use them is often not clear to practitioners. In this practical guide, we provide an overview of current machine learning methods that have been proposed to expedite evidence synthesis. We also offer guidance on which of these are ready for use, their strengths and weaknesses, and how a systematic review team might go about using them in practice.
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Affiliation(s)
- Iain J Marshall
- School of Population Health & Environmental Sciences, Faculty of Life Sciences and Medicine, King's College London, 3rd Floor, Addison House, Guy's Campus, London, SE1 1UL, UK.
| | - Byron C Wallace
- Khoury College of Computer Sciences, Northeastern University, 202 WVH, 360 Huntington Avenue, Boston, MA, 02115, USA
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