SparkText: Biomedical Text Mining on Big Data Framework

Ye, Zhan; Tafti, Ahmad P.; He, Karen Y.; Wang, Kai; He, Max M.

doi:10.1371/journal.pone.0162721

Cited by 34 publications

(18 citation statements)

References 19 publications

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“…Finally, in order to predict document's subject area based on its abstract, text mining-based classification is used [46]. For this purposes, binary logistic regression is selected as a prediction model.…”

Section: Methodsmentioning

confidence: 99%

A Bibliometric Analysis of COVID-19 across Science and Social Science Research Landscape

Aristovnik

Ravšelj

Umek

2020

Preprint

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The COVID-19 pandemic caused by the novel coronavirus emerged in Wuhan City, Hubei province of China at the end of 2019, has radically transformed the lives of people around the world. Due to its fast spreading, it is currently considered as a worldwide health, social and economic concern. The lack of knowledge on this area has encouraged academic sphere for extensive research, which is reflected in exponentially growing scientific literature in this area. However, current state of COVID-19 research reveals only early development of knowledge, while a comprehensive and in-depth overview remains neglected. Accordingly, the main aim of this paper is to fill the aforementioned gap in the literature and provide an extensive bibliometric analysis of COVID-19 research across science and social science research landscape, using innovative and sophisticated bibliometric approaches (e.g. Venn diagram, Biblioshiny descriptive statistics, VOSviewer co-occurrence network analysis, Jaccard distance cluster analysis, text mining based on logistic regression). The bibliometric analysis is based on the Scopus database including all relevant and latest information on COVID-19 related publications (n=16,866) in the first half of 2020. The empirical results indicate that there is still a lack of publications of COVID-19 and its implications in less-explored (non-health) sciences, especially in social sciences. Accordingly, the findings emphasize an importance of a comprehensive and in-depth approach considering different scientific disciplines in COVID-19 research. The understanding of the evolution of emerging scientific knowledge on COVID-19 is beneficial not only for scientific community but also for evidence-based policymaking in order to prevent and address the COVID-19 pandemic.

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Section: Methodsmentioning

confidence: 99%

A Bibliometric Analysis of COVID-19 across Science and Social Science Research Landscape

Aristovnik

Ravšelj

Umek

2020

Preprint

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“…For example, iHOP uses a text mining approach wherein genes and proteins are used as hyperlinks between sentences and PubMed abstracts and then uses the textmined information to produce network representations that users can browse [36]. Other tools include Twister that is aimed at reducing the screening time of systematic literature reviews [37]; SWIFT-Review, which is a workbench for systematic review based on NLP [38]; SparkText, which is a big data framework for mining biomedical literature [39]; and GIS, which is an NLP-based framework for gene discovery from scientific literature [40]. In addition to these tools, several frameworks for mining biomedical literature have been developed [41][42][43][44][45][46][47].…”

Section: Exploring Voluminous Informationmentioning

confidence: 99%

DES-ROD: Exploring Literature to Develop New Links between RNA Oxidation and Human Diseases

Essack

Salhi

Neste

et al. 2020

Oxidative Medicine and Cellular Longevity

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Normal cellular physiology and biochemical processes require undamaged RNA molecules. However, RNAs are frequently subjected to oxidative damage. Overproduction of reactive oxygen species (ROS) leads to RNA oxidation and disturbs redox (oxidation-reduction reaction) homeostasis. When oxidation damage affects RNA carrying protein-coding information, this may result in the synthesis of aberrant proteins as well as a lower efficiency of translation. Both of these, as well as imbalanced redox homeostasis, may lead to numerous human diseases. The number of studies on the effects of RNA oxidative damage in mammals is increasing by year due to the understanding that this oxidation fundamentally leads to numerous human diseases. To enable researchers in this field to explore information relevant to RNA oxidation and effects on human diseases, we developed DES-ROD, an online knowledgebase that contains processed information from 298,603 relevant documents that consist of PubMed abstracts and PubMed Central full-text articles. The system utilizes concepts/terms from 38 curated thematic dictionaries mapped to the analyzed documents. Researchers can explore enriched concepts, as well as enriched pairs of putatively associated concepts. In this way, one can explore mutual relationships between any combinations of two concepts from used dictionaries. Dictionaries cover a wide range of biomedical topics, such as human genes and proteins, pathways, Gene Ontology categories, mutations, noncoding RNAs, enzymes, toxins, metabolites, and diseases. This makes insights into different facets of the effects of RNA oxidation and the control of this process possible. The usefulness of the DES-ROD system is demonstrated by case studies on some known information, as well as potentially novel information involving RNA oxidation and diseases. DES-ROD is the first knowledgebase based on text and data mining that focused on the exploration of RNA oxidation and human diseases.

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“…23 Ye et al used support vector machines to predict cancer type in full-text articles. 24 ES is defined as how much you should think of a disorder given a finding or manifestation. This embodies a sense of how important it is not to miss this disorder.…”

Section: Background and Significancementioning

confidence: 99%

Artificial Intelligence: Bayesian versus Heuristic Method for Diagnostic Decision Support

et al. 2018

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Evoking strength is one of the important contributions of the field of Biomedical Informatics to the discipline of Artificial Intelligence. The University at Buffalo's Orthopedics Department wanted to create an expert system to assist patients with self-diagnosis of knee problems and to thereby facilitate referral to the right orthopedic subspecialist. They had two independent sports medicine physicians review 469 cases. A board-certified orthopedic sports medicine practitioner, L.B., reviewed any disagreements until a gold standard diagnosis was reached. For each case, the patients entered 126 potential answers to 26 questions into a Web interface. These were modeled by an expert sports medicine physician and the answers were reviewed by L.B. For each finding, the clinician specified the sensitivity (term frequency) and both specificity (Sp) and the heuristic evoking strength (ES). Heuristics are methods of reasoning with only partial evidence. An expert system was constructed that reflected the posttest odds of disease-ranked list for each case. We compare the accuracy of using Sp to that of using ES (original model, < 0.0008; term importance * disease importance [DItimesTI] model, < 0.0001: Wilcoxon ranked sum test). For patient referral assignment, Sp in the DItimesTI model was superior to the use of ES. By the fifth diagnosis, the advantage was lost and so there is no difference between the techniques when serving as a reminder system.

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SparkText: Biomedical Text Mining on Big Data Framework

Cited by 34 publications

References 19 publications

A Bibliometric Analysis of COVID-19 across Science and Social Science Research Landscape

A Bibliometric Analysis of COVID-19 across Science and Social Science Research Landscape

DES-ROD: Exploring Literature to Develop New Links between RNA Oxidation and Human Diseases

Artificial Intelligence: Bayesian versus Heuristic Method for Diagnostic Decision Support

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