Toro: A Web-based Tool to Search, Explore, Screen, Compare and Visualize Literature

We present TORO (https://www.toro.ac.nz/), a web-based application that aims to simplify the time-consuming literature research process by providing an easy way of searching, exploring, screening, and comparing literature inside a visual paper graph. Unlike many automated tools, the user actively explores papers and constructs the graph. Users can add papers by searching across different publishers or pasting DOI links and bib-files. Users can analyse relations between papers in one view, look up common keywords, access paper details and categorize them easily. Expanding references allows exploring interesting streams of literature and performing search strategies like snowballing easily across platforms. To manage large numbers of papers and identify interesting work, users can define filters and highlighting criteria. We present our initial design and implementation and discuss the results of a preliminary user study with 18 researchers. The results indicate that TORO already helps researchers\u27 exploration process and highlights opportunities for future work

Predictive models for hotel booking cancellation: a semi-automated analysis of the literature

In reservation-based industries, an accurate booking cancellation forecast is of foremost importance to estimate demand. By combining data science tools and capabilities with human judgement and interpretation, this paper aims to demonstrate how the semiautomatic analysis of the literature can contribute to synthesizing research findings and identify research topics about booking cancellation forecasting. Furthermore, this works aims, by detailing the full experimental procedure of the analysis, to encourage other authors to conduct automated literature analysis as a means to understand current research in their working fields. The data used was obtained through a keyword search in Scopus and Web of Science databases. The methodology presented not only diminishes human bias, but also enhances the fact that data visualisation and text mining techniques facilitate abstraction, expedite analysis, and contribute to the improvement of reviews. Results show that despite the importance of bookings’ cancellation forecast in terms of understanding net demand, improving cancellation, and overbooking policies, further research on the subject is still needed.info:eu-repo/semantics/publishedVersio

Visual text mining: ensuring the presence of relevant studies in systematic literature reviews

One of the activities associated with the Systematic Literature Review (SLR) process is the\ud selection review of primary studies. When the researcher faces large volumes of primary studies\ud to be analyzed, the process used to select studies can be arduous. In a previous experiment, we\ud conducted a pilot test to compare the performance and accuracy of PhD students in conducting\ud the selection review activity manually and using Visual Text Mining (VTM) techniques. The\ud goal of this paper is to describe a replication study involving PhD and Master students. The\ud replication study uses the same experimental design and materials of the original experiment.\ud This study also aims to investigate whether the researcher's level of experience with conducting\ud SLRs and research in general impacts the outcome of the primary study selection step of the\ud SLR process. The replication results have con¯rmed the outcomes of the original experiment,\ud i.e., VTM is promising and can improve the performance of the selection review of primary\ud studies. We also observed that both accuracy and performance increase in function of the\ud researcher's experience level in conducting SLRs. The use of VTM can indeed be bene¯cial\ud during the selection review activity.FAPES

Data extraction methods for systematic review (semi)automation: A living systematic review [version 1; peer review: awaiting peer review]

Background: The reliable and usable (semi)automation of data extraction can support the field of systematic review by reducing the workload required to gather information about the conduct and results of the included studies. This living systematic review examines published approaches for data extraction from reports of clinical studies. Methods: We systematically and continually search MEDLINE, Institute of Electrical and Electronics Engineers (IEEE), arXiv, and the dblp computer science bibliography databases. Full text screening and data extraction are conducted within an open-source living systematic review application created for the purpose of this review. This iteration of the living review includes publications up to a cut-off date of 22 April 2020. Results: In total, 53 publications are included in this version of our review. Of these, 41 (77%) of the publications addressed extraction of data from abstracts, while 14 (26%) used full texts. A total of 48 (90%) publications developed and evaluated classifiers that used randomised controlled trials as the main target texts. Over 30 entities were extracted, with PICOs (population, intervention, comparator, outcome) being the most frequently extracted. A description of their datasets was provided by 49 publications (94%), but only seven (13%) made the data publicly available. Code was made available by 10 (19%) publications, and five (9%) implemented publicly available tools. Conclusions: This living systematic review presents an overview of (semi)automated data-extraction literature of interest to different types of systematic review. We identified a broad evidence base of publications describing data extraction for interventional reviews and a small number of publications extracting epidemiological or diagnostic accuracy data. The lack of publicly available gold-standard data for evaluation, and lack of application thereof, makes it difficult to draw conclusions on which is the best-performing system for each data extraction target. With this living review we aim to review the literature continually

Bibliometric Method for Mapping the State-of-the-Art and Identifying Research Gaps and Trends in Literature: An Essential Instrument to Support the Development of Scientific Projects

Bibliometric analysis is an indispensable statistic tool to map the state of the art in a given area of scientific knowledge and identify essential information for various purposes, such as prospecting research opportunities and substantiating scientific researches. Therefore, the objective of this chapter is to present a method of bibliometric analysis for mapping the state of the art and identifying gaps and trends of research. The method encompasses instruments to identify and analyze the scientific performance of articles, authors, institutions, countries, and journals based on the number of citations, to reveal the trends of the field studied through the analysis of keywords, and to identify and cluster scientific gaps from most recent publications. This method enables to expand in a scientific way the boundaries of science by investigating and identifying relevant and avant-garde research topics. It is an essential element that provides researchers means to identify and support paths towards the development of scientific projects

An integrative methodology based on protein-protein interaction networks for identification and functional annotation of disease-relevant genes applied to channelopathies

Biologically data-driven networks have become powerful analytical tools that handle massive, heterogeneous datasets generated from biomedical fields. Protein-protein interaction networks can identify the most relevant structures directly tied to biological functions. Functional enrichments can then be performed based on these structural aspects of gene relationships for the study of channelopathies. Channelopathies refer to a complex group of disorders resulting from dysfunctional ion channels with distinct polygenic manifestations. This study presents a semi-automatic workflow using protein-protein interaction networks that can identify the most relevant genes and their biological processes and pathways in channelopathies to better understand their etiopathogenesis. In addition, the clinical manifestations that are strongly associated with these genes are also identified as the most characteristic in this complex group of diseases. This research provides a systems biology approach to extract information from interaction networks of gene expression. We show how large-scale computational integration of heterogeneous datasets, PPI network analyses, functional databases and published literature may support the detection and assessment of possible potential therapeutic targets in the disease. Applying our workflow makes it feasible to spot the most relevant genes and unknown relationships in channelopathies and shows its potential as a first-step approach to identify both genes and functional interactions in clinical-knowledge scenarios of target diseases.This work was supported by funds from MINECO-FEDER (TIN2016–81041-R to E.R.), European Human Brain Project SGA2 (H2020-RIA 785907 to M.J.S.), Junta de Andalucía (BIO-302 to F.J.E.) and MEIC (Systems Medicine Excellence Network, SAF2015–70270-REDT to F.J.E.)

Data extraction methods for systematic review (semi)automation: Update of a living systematic review [version 2; peer review: 3 approved]

Background: The reliable and usable (semi)automation of data extraction can support the field of systematic review by reducing the workload required to gather information about the conduct and results of the included studies. This living systematic review examines published approaches for data extraction from reports of clinical studies. Methods: We systematically and continually search PubMed, ACL Anthology, arXiv, OpenAlex via EPPI-Reviewer, and the dblp computer science bibliography. Full text screening and data extraction are conducted within an open-source living systematic review application created for the purpose of this review. This living review update includes publications up to December 2022 and OpenAlex content up to March 2023. Results: 76 publications are included in this review. Of these, 64 (84%) of the publications addressed extraction of data from abstracts, while 19 (25%) used full texts. A total of 71 (93%) publications developed classifiers for randomised controlled trials. Over 30 entities were extracted, with PICOs (population, intervention, comparator, outcome) being the most frequently extracted. Data are available from 25 (33%), and code from 30 (39%) publications. Six (8%) implemented publicly available tools Conclusions: This living systematic review presents an overview of (semi)automated data-extraction literature of interest to different types of literature review. We identified a broad evidence base of publications describing data extraction for interventional reviews and a small number of publications extracting epidemiological or diagnostic accuracy data. Between review updates, trends for sharing data and code increased strongly: in the base-review, data and code were available for 13 and 19% respectively, these numbers increased to 78 and 87% within the 23 new publications. Compared with the base-review, we observed another research trend, away from straightforward data extraction and towards additionally extracting relations between entities or automatic text summarisation. With this living review we aim to review the literature continually