Text mining resources for the life sciences

Exploratory bibliometric analysis and text mining to reveal research trends in cardiac aging

Objectives

We conducted a text mining analysis of 40 years of literature on cardiac aging from PubMed to investigate the current understanding on cardiac aging and its mechanisms. This study aimed to embody what most researchers consider cardiac aging to be.

Methods

We used multiple text mining and machine learning tools to extract important information from a large amount of text.

Results

Analysis revealed that the terms most frequently associated with cardiac aging include "diastolic," "hypertrophy," "fibrosis," "apoptosis," "mitochondrial," "oxidative," and "autophagy." These terms suggest that cardiac aging is characterized by mitochondrial dysfunction, oxidative stress, and impairment of autophagy, especially mitophagy. We also revealed an increase in the frequency of occurrence of "autophagy" in recent years, suggesting that research on autophagy has made a breakthrough in the field of cardiac aging. Additionally, the frequency of occurrence of "mitophagy" has increased significantly since 2019, suggesting that mitophagy is an important factor in cardiac aging.

Conclusions

Cardiac aging is a complex process that involves mitochondrial dysfunction, oxidative stress, and impairment of autophagy, especially mitophagy. Further research is warranted to elucidate the mechanisms of cardiac aging and develop strategies to mitigate its detrimental effects.

GlucoKinaseDB: A comprehensive, curated resource of glucokinase modulators for clinical and molecular research

Glucokinase (GK), an isoform of hexokinase expressed predominantly in liver, pancreas and hypothalamus is crucial to blood glucose management. It is a critical component of the glucose-sensing mechanism of the pancreatic islet cells and glycogen regulation in hepatocytes. GK modulators such as allosteric GKAs (glucokinase activators) and GK-GKRP (glucokinase regulatory protein) disruptors have found potential applications as safer antihyperglycemics. Recent studies have also demonstrated the potential of GK modulators as antiparasitic agents. Researchers targeting GK often undertake the time-consuming task of independently collecting and compiling modulator information due to the lack of any dedicated single-platform resource. Towards this, in the present study we demonstrate the design and development of GlucoKinaseDB (GKDB), a comprehensive, curated, online resource of GK modulators. GKDB contains experimentally derived structural and bioactivity information of 1723 modulators along with their detailed molecular descriptors. The web-interface is user-friendly with features such as in-browser visualization, advanced search queries, cross-links to other databases and original reference etc. The bioactivity and descriptor data can be downloaded in bulk (for entire database) or for individual modulators. The 3D structures are also downloadable in multiple formats. GKDB employs a PHP-based web design with Bootstrap styling and a MySQL database backend. GKDB can be utilized for clinical and molecular research via development of pharmacophore hypotheses, QSAR/QSPR models, predictive machine learning models etc. GKDB is freely accessible online at https://glucokinasedb.in.

Darling: A Web Application for Detecting Disease-Related Biomedical Entity Associations with Literature Mining

Finding, exploring and filtering frequent sentence-based associations between a disease and a biomedical entity, co-mentioned in disease-related PubMed literature, is a challenge, as the volume of publications increases. Darling is a web application, which utilizes Name Entity Recognition to identify human-related biomedical terms in PubMed articles, mentioned in OMIM, DisGeNET and Human Phenotype Ontology (HPO) disease records, and generates an interactive biomedical entity association network. Nodes in this network represent genes, proteins, chemicals, functions, tissues, diseases, environments and phenotypes. Users can search by identifiers, terms/entities or free text and explore the relevant abstracts in an annotated format.

An Application of Machine Learning Techniques to Analyze Patient Information to Improve Oral Health Outcomes

Objective

Various health-related fields have applied Machine learning (ML) techniques such as text mining, topic modeling (TM), and artificial neural networks (ANN) to automate tasks otherwise completed by humans to enhance patient care. However, research in dentistry on the integration of these techniques into the clinic arena has yet to exist. Thus, the purpose of this study was to: introduce a method of automating the reviewing patient chart information using ML, provide a step-by-step description of how it was conducted, and demonstrate this method's potential to identify predictive relationships between patient chart information and important oral health-related contributors.

Methods

A secondary data analysis was conducted to demonstrate the approach on a set of anonymized patient charts collected from a dental clinic. Two ML applications for patient chart review were demonstrated: (1) text mining and Latent Dirichlet Allocation (LDA) were used to preprocess, model, and cluster data in a narrative format and extract common topics for further analysis, (2) Ordinal logistic regression (OLR) and ANN were used to determine predictive relationships between the extracted patient chart data topics and oral health-related contributors. All analysis was conducted in R and SPSS (IBM, SPSS, statistics 22).

Results

Data from 785 patient charts were analyzed. Preprocessing of raw data (data cleaning and categorizing) identified 66 variables, of which 45 were included for analysis. Using LDA, 10 radiographic findings topics and 8 treatment planning topics were extracted from the data. OLR showed that caries risk, occlusal risk, biomechanical risk, gingival recession, periodontitis, gingivitis, assisted mouth opening, and muscle tenderness were highly predictable using the extracted radiographic and treatment planning topics and chart information. Using the statistically significant predictors obtained from OLR, ANN analysis showed that the model can correctly predict >72% of all variables except for bruxism and tooth crowding (63.1 and 68.9%, respectively).

Conclusion

Our study presents a novel approach to address the need for data-enabled innovations in the field of dentistry and creates new areas of research in dental analytics. Utilizing ML methods and its application in dental practice has the potential to improve clinicians' and patients' understanding of the major factors that contribute to oral health diseases/conditions.

Knowledge-based approaches to drug discovery for rare diseases

The conventional drug discovery pipeline has proven to be unsustainable for rare diseases. Herein, we discuss recent advances in biomedical knowledge mining applied to discovering therapeutics for rare diseases. We summarize current chemogenomics data of relevance to rare diseases and provide a perspective on the effectiveness of machine learning (ML) and biomedical knowledge graph mining in rare disease drug discovery. We illustrate the power of these methodologies using a chordoma case study. We expect that a broader application of knowledge graph mining and artificial intelligence (AI) approaches will expedite the discovery of viable drug candidates against both rare and common diseases.

Contribution of radiation education to anxiety reduction among Fukushima Daiichi Nuclear Power Plant workers: a cross sectional study using a text mining method

The purpose of this study is to investigate the frequency of education, knowledge of radiation and workplace anxiety of Fukushima Daiichi Nuclear Power Plant (FDNPP) workers and to analyze what type of words are used for anxiety with a text mining method. An original questionnaire survey was given to FDNPP workers, and a text mining method was used to extract information from free-entry fields. The questionnaires were collected from 1135 workers (response rate: 70.8%). It was found that when workers receive education on radiation, the increased knowledge helps to reduce their anxiety. Among the 1135 workers, 92 of 127 completed the free-entry field with valid entries. Seventy-one words were extracted by the text mining method. The words used differed depending on the degree of anxiety. The text mining method revealed information about the presence or absence of radiation anxiety and the subjects' working environment and background.

Recent innovations and in-depth aspects of post-genome wide association study (Post-GWAS) to understand the genetic basis of complex phenotypes

In the past decade, the high throughput and low cost of sequencing/genotyping approaches have led to the accumulation of a large amount of data from genome-wide association studies (GWASs). The first aim of this review is to highlight how post-GWAS analysis can be used make sense of the obtained associations. Novel directions for integrating GWAS results with other resources, such as somatic mutation, metabolite-transcript, and transcriptomic data, are also discussed; these approaches can help us move beyond each individual data point and provide valuable information about complex trait genetics. In addition, cross-phenotype association tests, when the loci detected by GWASs have significant associations with multiple traits, are reviewed to provide biologically informative results for use in real-time applications. This review also discusses the challenges of identifying interactions between genetic mutations (epistasis) and mutations of loci affecting more than one trait (pleiotropy) as underlying causes of cross-phenotype associations; these challenges can be overcome using post-GWAS analysis. Genetic similarities between phenotypes that can be revealed using post-GWAS analysis are also discussed. In summary, different methodologies of post-GWAS analysis are now available, enhancing the value of information obtained from GWAS results, and facilitating application in both humans and nonhuman species. However, precise methods still need to be developed to overcome challenges in the field and uncover the genetic underpinnings of complex traits.

Mining Early Life Risk and Resiliency Factors and Their Influences in Human Populations from PubMed: A Machine Learning Approach to Discover DOHaD Evidence

The Developmental Origins of Health and Disease (DOHaD) framework aims to understand how early life exposures shape lifecycle health. To date, no comprehensive list of these exposures and their interactions has been developed, which limits our ability to predict trajectories of risk and resiliency in humans. To address this gap, we developed a model that uses text-mining, machine learning, and natural language processing approaches to automate search, data extraction, and content analysis from DOHaD-related research articles available in PubMed. Our first model captured 2469 articles, which were subsequently categorised into topics based on word frequencies within the titles and abstracts. A manual screening validated 848 of these as relevant, which were used to develop a revised model that finally captured 2098 articles that largely fell under the most prominently researched domains related to our specific DOHaD focus. The articles were clustered according to latent topic extraction, and 23 experts in the field independently labelled the perceived topics. Consensus analysis on this labelling yielded mostly from fair to substantial agreement, which demonstrates that automated models can be developed to successfully retrieve and classify research literature, as a first step to gather evidence related to DOHaD risk and resilience factors that influence later life human health.

OnTheFly2.0: a text-mining web application for automated biomedical entity recognition, document annotation, network and functional enrichment analysis

Extracting and processing information from documents is of great importance as lots of experimental results and findings are stored in local files. Therefore, extracting and analyzing biomedical terms from such files in an automated way is absolutely necessary. In this article, we present OnTheFly^2.0, a web application for extracting biomedical entities from individual files such as plain texts, office documents, PDF files or images. OnTheFly^2.0 can generate informative summaries in popup windows containing knowledge related to the identified terms along with links to various databases. It uses the EXTRACT tagging service to perform named entity recognition (NER) for genes/proteins, chemical compounds, organisms, tissues, environments, diseases, phenotypes and gene ontology terms. Multiple files can be analyzed, whereas identified terms such as proteins or genes can be explored through functional enrichment analysis or be associated with diseases and PubMed entries. Finally, protein-protein and protein-chemical networks can be generated with the use of STRING and STITCH services. To demonstrate its capacity for knowledge discovery, we interrogated published meta-analyses of clinical biomarkers of severe COVID-19 and uncovered inflammatory and senescence pathways that impact disease pathogenesis. OnTheFly^2.0 currently supports 197 species and is available at http://bib.fleming.gr:3838/OnTheFly/ and http://onthefly.pavlopouloslab.info.

Towards a Consensus on Alzheimer's Disease Comorbidity?

Alzheimer's disease (AD) is often comorbid with other pathologies. First, we review shortly the diseases most associated with AD in the clinic. Then we query PubMed citations for the co-occurrence of AD with other diseases, using a list of 400 common pathologies. Significantly, AD is found to be associated with schizophrenia and psychosis, sleep insomnia and apnea, type 2 diabetes, atherosclerosis, hypertension, cardiovascular diseases, obesity, fibrillation, osteoporosis, arthritis, glaucoma, metabolic syndrome, pain, herpes, HIV, alcoholism, heart failure, migraine, pneumonia, dyslipidemia, COPD and asthma, hearing loss, and tobacco smoking. Trivially, AD is also found to be associated with several neurodegenerative diseases, which are disregarded. Notably, our predicted results are consistent with the previously published clinical data and correlate nicely with individual publications. Our results emphasize risk factors and promulgate diseases often associated with AD. Interestingly, the comorbid diseases are often degenerative diseases exacerbated by reactive oxygen species, thus underlining the potential role of antioxidants in the treatment of AD and comorbid diseases.

Reconstruction of the Cytokine Signaling in Lysosomal Storage Diseases by Literature Mining and Network Analysis

Lysosomal storage diseases (LSDs) are characterized by the abnormal accumulation of substrates in tissues due to the deficiency of lysosomal proteins. Among the numerous clinical manifestations, chronic inflammation has been consistently reported for several LSDs. However, the molecular mechanisms involved in the inflammatory response are still not completely understood. In this study, we performed text-mining and systems biology analyses to investigate the inflammatory signals in three LSDs characterized by sphingolipid accumulation: Gaucher disease, Acid Sphingomyelinase Deficiency (ASMD), and Fabry Disease. We first identified the cytokines linked to the LSDs, and then built on the extracted knowledge to investigate the inflammatory signals. We found numerous transcription factors that are putative regulators of cytokine expression in a cell-specific context, such as the signaling axes controlled by STAT2, JUN, and NR4A2 as candidate regulators of the monocyte Gaucher disease cytokine network. Overall, our results suggest the presence of a complex inflammatory signaling in LSDs involving many cellular and molecular players that could be further investigated as putative targets of anti-inflammatory therapies.

Extraction of Data on Parent Compounds and Their Metabolites from Texts of Scientific Abstracts

The growing amount of experimental data on chemical objects includes properties of small molecules, results of studies of their interaction with human and animal proteins, and methods of synthesis of organic compounds (OCs). The data obtained can be used to identify the names of OCs automatically, including all possible synonyms and relevant data on the molecular properties and biological activity. Utilization of different synonymic names of chemical compounds allows researchers to increase the completeness of data on their properties available from publications. Enrichment of the data on the names of chemical compounds by information about their possible metabolites can help estimate the biological effects of parent compounds and their metabolites more thoroughly. Therefore, an attempt at automated extraction of the names of parent compounds and their metabolites from the texts is a rather important task. In our study, we aimed at developing a method that provides the extraction of the named entities (NEs) of parent compounds and their metabolites from abstracts of scientific publications. Based on the application of the conditional random fields' algorithm, we extracted the NEs of chemical compounds. We developed a set of rules allowing identification of parent compound NEs and their metabolites in the texts. We evaluated the possibility of extracting the names of potential metabolites based on cosine similarity between strings representing names of parent compounds and all other chemical NEs found in the text. Additionally, we used conditional random fields to fetch the names of parent compounds and their metabolites from the texts based on the corpus of texts labeled manually. Our computational experiments showed that usage of rules in combination with cosine similarity could increase the accuracy of recognition of the names of metabolites compared to the rule-based algorithm and application of a machine-learning algorithm (conditional random fields).

SARSCOVIDB-A New Platform for the Analysis of the Molecular Impact of SARS-CoV-2 Viral Infection

The COVID-19 pandemic caused by the new coronavirus (SARS-CoV-2) has become a global emergency issue for public health. This threat has led to an acceleration in related research and, consequently, an unprecedented volume of clinical and experimental data that include changes in gene expression resulting from infection. The SARS-CoV-2 infection database (SARSCOVIDB: https://sarscovidb.org/) was created to mitigate the difficulties related to this scenario. The SARSCOVIDB is an online platform that aims to integrate all differential gene expression data, at messenger RNA and protein levels, helping to speed up analysis and research on the molecular impact of COVID-19. The database can be searched from different experimental perspectives and presents all related information from published data, such as viral strains, hosts, methodological approaches (proteomics or transcriptomics), genes/proteins, and samples (clinical or experimental). All information was taken from 24 articles related to analyses of differential gene expression out of 5,554 COVID-19/SARS-CoV-2-related articles published so far. The database features 12,535 genes whose expression has been identified as altered due to SARS-CoV-2 infection. Thus, the SARSCOVIDB is a new resource to support the health workers and the scientific community in understanding the pathogenesis and molecular impact caused by SARS-CoV-2.

Common Genes Involved in Autophagy, Cellular Senescence and the Inflammatory Response in AMD and Drug Discovery Identified via Biomedical Databases

Purpose

Retinal pigment epithelial cell autophagy dysfunction, cellular senescence, and the retinal inflammatory response are key pathogenic factors in age-related macular degeneration (AMD), which has been reviewed in our previously work in 2019. This study aims to identify genes collectively involved in these three biological processes and target drugs in AMD.

Methods

The pubmed2ensembl database was used to perform text mining. The GeneCodis database was applied to analyze gene ontology biological process and the KEGG pathway. The STRING database was used to analyze protein–protein interaction analysis and hub genes were identified by the Cytoscape software. The Drug Gene Interaction Database was used to perform drug–gene interactions.

Results

We identified 62 genes collectively involved in AMD, autophagy, cellular senescence, and inflammatory response, 19 biological processes including 42 genes, 11 enriched KEGG pathways including 37 genes, and 12 hub genes step by step via the above biomedical databases. Finally, five hub genes (IL-6, VEGF-A, TP53, IL-1β, and transforming growth factor [TGF]-β1) and their specific interaction modes were identified, corresponding with 24 target drugs with therapeutic potential for AMD.

Conclusions

IL-6, VEGF-A, TP53, IL-1β, and TGF-β1 are pivotal in autophagy, cellular senescence, and the inflammatory response in AMD, corresponding with 24 drugs with therapeutic potential for AMD, providing definite molecular mechanisms for further research and new possibilities for AMD treatment in the future.

Translational Relevance

IL-6, VEGF-A, TP53, IL-1β, and TGF-β1 may be new targets for AMD gene therapy and drug development.

Integrative Analysis of Membrane Proteome and MicroRNA Reveals Novel Lung Cancer Metastasis Biomarkers

Lung cancer is one of the most common human cancers both in incidence and mortality, with prognosis particularly poor in metastatic cases. Metastasis in lung cancer is a multifarious process driven by a complex regulatory landscape involving many mechanisms, genes, and proteins. Membrane proteins play a crucial role in the metastatic journey both inside tumor cells and the extra-cellular matrix and are a viable area of research focus with the potential to uncover biomarkers and drug targets. In this work we performed membrane proteome analysis of highly and poorly metastatic lung cells which integrated genomic, proteomic, and transcriptional data. A total of 1,762 membrane proteins were identified, and within this set, there were 163 proteins with significant changes between the two cell lines. We applied the Tied Diffusion through Interacting Events method to integrate the differentially expressed disease-related microRNAs and functionally dys-regulated membrane protein information to further explore the role of key membrane proteins and microRNAs in multi-omics context. Has-miR-137 was revealed as a key gene involved in the activity of membrane proteins by targeting MET and PXN, affecting membrane proteins through protein-protein interaction mechanism. Furthermore, we found that the membrane proteins CDH2, EGFR, ITGA3, ITGA5, ITGB1, and CALR may have significant effect on cancer prognosis and outcomes, which were further validated in vitro. Our study provides multi-omics-based network method of integrating microRNAs and membrane proteome information, and uncovers a differential molecular signatures of highly and poorly metastatic lung cancer cells; these molecules may serve as potential targets for giant-cell lung metastasis treatment and prognosis.

Drug Abuse Research Trend Investigation with Text Mining

Drug abuse poses great physical and psychological harm to humans, thereby attracting scholarly attention. It often requires experience and time for a researcher, just entering this field, to find an appropriate method to study drug abuse issue. It is crucial for researchers to rapidly understand the existing research on a particular topic and be able to propose an effective new research method. Text mining analysis has been widely applied in recent years, and this study integrated the text mining method into a review of drug abuse research. Through searches for keywords related to the drug abuse, all related publications were identified and downloaded from PubMed. After removing the duplicate and incomplete literature, the retained data were imported for analysis through text mining. A total of 19,843 papers were analyzed, and the text mining technique was used to search for keyword and questionnaire types. The results showed the associations between these questionnaires, with the top five being the Addiction Severity Index (16.44%), the Quality of Life survey (5.01%), the Beck Depression Inventory (3.24%), the Addiction Research Center Inventory (2.81%), and the Profile of Mood States (1.10%). Specifically, the Addiction Severity Index was most commonly used in combination with Quality of Life scales. In conclusion, association analysis is useful to extract core knowledge. Researchers can learn and visualize the latest research trend.

ZIKAVID-Zika virus infection database: a new platform to analyze the molecular impact of Zika virus infection

The recent outbreak of Zika virus (ZIKV) in Brazil and other countries globally demonstrated the relevance of ZIKV studies. During and after this outbreak, there was an intense increase in scientific production on ZIKV infections, especially toward alterations promoted by the infection and related to clinical outcomes. Considering this massive amount of new data, mainly thousands of genes and proteins whose expression is impacted by ZIKV infection, the ZIKA Virus Infection Database (ZIKAVID) was created. ZIKAVID is an online database that comprises all genes or proteins, and associated information, for which expression was experimentally measured and found to be altered after ZIKV infection. The database, available at https://zikavid.org, contains 16,984 entries of gene expression measurements from a total of 7348 genes. It allows users to easily perform searches for different experimental hosts (cell lines, tissues, and animal models), ZIKV strains (African, Asian, and Brazilian), and target molecules (messenger RNA [mRNA] and protein), among others, used in differential expression studies regarding ZIKV infection. In this way, the ZIKAVID will serve as an additional and important resource to improve the characterization of the molecular impact and pathogenesis associated with ZIKV infection.

A Lightweight API-Based Approach for Building Flexible Clinical NLP Systems

Natural language processing (NLP) has become essential for secondary use of clinical data. Over the last two decades, many clinical NLP systems were developed in both academia and industry. However, nearly all existing systems are restricted to specific clinical settings mainly because they were developed for and tested with specific datasets, and they often fail to scale up. Therefore, using existing NLP systems for one's own clinical purposes requires substantial resources and long-term time commitments for customization and testing. Moreover, the maintenance is also troublesome and time-consuming. This research presents a lightweight approach for building clinical NLP systems with limited resources. Following the design science research approach, we propose a lightweight architecture which is designed to be composable, extensible, and configurable. It takes NLP as an external component which can be accessed independently and orchestrated in a pipeline via web APIs. To validate its feasibility, we developed a web-based prototype for clinical concept extraction with six well-known NLP APIs and evaluated it on three clinical datasets. In comparison with available benchmarks for the datasets, three high F1 scores (0.861, 0.724, and 0.805) were obtained from the evaluation. It also gained a low F1 score (0.373) on one of the tests, which probably is due to the small size of the test dataset. The development and evaluation of the prototype demonstrates that our approach has a great potential for building effective clinical NLP systems with limited resources.

A Guide to Dictionary-Based Text Mining

PubMed contains more than 27 million documents, and this number is growing at an estimated 4% per year. Even within specialized topics, it is no longer possible for a researcher to read any field in its entirety, and thus nobody has a complete picture of the scientific knowledge in any given field at any time. Text mining provides a means to automatically read this corpus and to extract the relations found therein as structured information. Having data in a structured format is a huge boon for computational efforts to access, cross reference, and mine the data stored therein. This is increasingly useful as biological research is becoming more focused on systems and multi-omics integration. This chapter provides an overview of the steps that are required for text mining: tokenization, named entity recognition, normalization, event extraction, and benchmarking. It discusses a variety of approaches to these tasks and then goes into detail on how to prepare data for use specifically with the JensenLab tagger. This software uses a dictionary-based approach and provides the text mining evidence for STRING and several other databases.

Socio-Environmental Vulnerability Mapping for Environmental and Flood Resilience Assessment: The Case of Ageing and Poverty in the City of Wrocław, Poland

The phenomena of urbanization and climate change interact with the growing number of older people living in cities. One of the effects of climate change is an increased riverine flooding hazard, and when floods occur this has a severe impact on human lives and comes with vast economic losses. Flood resilience management procedures should be supported by a combination of complex social and environmental vulnerability assessments. Therefore, new methodologies and tools should be developed for this purpose. One way to achieve such inclusive procedures is by incorporating a social vulnerability evaluation methodology for environmental and flood resilience assessment. These are illustrated for application in the Polish city of Wrocław. Socio-environmental vulnerability mapping, based on spatial analyses using the poverty risk index, data on the ageing population, as well as the distribution of the areas vulnerable to floods, was conducted with use of a location intelligence system combining Geographic Information System (GIS) and Business Intelligence (BI) tools. The new methodology allows for the identification of areas populated by social groups that are particularly vulnerable to the negative effects of flooding. Integr Environ Assess Manag 2018;14:592-597. © 2018 SETAC.

Design of a generic, open platform for machine learning-assisted indexing and clustering of articles in PubMed, a biomedical bibliographic database

Many investigators have carried out text mining of the biomedical literature for a variety of purposes, ranging from the assignment of indexing terms to the disambiguation of author names. A common approach is to define positive and negative training examples, extract features from article metadata, and employ machine learning algorithms. At present, each research group tackles each problem from scratch, and in isolation of other projects, which causes redundancy and great waste of effort. Here, we propose and describe the design of a generic platform for biomedical text mining, which can serve as a shared resource for machine learning projects, and can serve as a public repository for their outputs. We will initially focus on a specific goal, namely, classifying articles according to Publication Type, and emphasize how feature sets can be made more powerful and robust through the use of multiple, heterogeneous similarity measures as input to machine learning models. We then discuss how the generic platform can be extended to include a wide variety of other machine learning based goals and projects, and can be used as a public platform for disseminating the results of NLP tools to end-users as well.