scholarly journals Biomarker identification of hepatocellular carcinoma using a methodical literature mining strategy

Database ◽  
2017 ◽  
Vol 2017 ◽  
Author(s):  
Nai-Wen Chang ◽  
Hong-Jie Dai ◽  
Yung-Yu Shih ◽  
Chi-Yang Wu ◽  
Mira Anne C Dela Rosa ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Text Mining ◽  
Literature Mining ◽  
Biomedical Text Mining ◽  
Biomedical Knowledge ◽  
Named Entities ◽  
Biomarker Identification ◽  
Wide Range ◽  
Common Causes ◽  
Journal Impact

Abstract Hepatocellular carcinoma (HCC), one of the most common causes of cancer-related deaths, carries a 5-year survival rate of 18%, underscoring the need for robust biomarkers. In spite of the increased availability of HCC related literatures, many of the promising biomarkers reported have not been validated for clinical use. To narrow down the wide range of possible biomarkers for further clinical validation, bioinformaticians need to sort them out using information provided in published works. Biomedical text mining is an automated way to obtain information of interest within the massive collection of biomedical knowledge, thus enabling extraction of data for biomarkers associated with certain diseases. This method can significantly reduce both the time and effort spent on studying important maladies such as liver diseases. Herein, we report a text mining-aided curation pipeline to identify potential biomarkers for liver cancer. The curation pipeline integrates PubMed E-Utilities to collect abstracts from PubMed and recognize several types of named entities by machine learning-based and pattern-based methods. Genes/proteins from evidential sentences were classified as candidate biomarkers using a convolutional neural network. Lastly, extracted biomarkers were ranked depending on several criteria, such as the frequency of keywords and articles and the journal impact factor, and then integrated into a meaningful list for bioinformaticians. Based on the developed pipeline, we constructed MarkerHub, which contains 2128 candidate biomarkers extracted from PubMed publications from 2008 to 2017. Database URL: http://markerhub.iis.sinica.edu.tw

A RE-EVALUATION OF BIOMEDICAL NAMED ENTITY–TERM RELATIONS

2010 ◽  
Vol 08 (05) ◽  
pp. 917-928 ◽  
Author(s):  
TOMOKO OHTA ◽  
SAMPO PYYSALO ◽  
JIN-DONG KIM ◽  
JUN'ICHI TSUJII
Keyword(s):  
Text Mining ◽  
Biomedical Text Mining ◽  
Corpus Annotation ◽  
Named Entities ◽  
Detailed Model ◽  
Named Entity ◽  
Textual Data ◽  
Recent Developments ◽  
Relevant Domain ◽  
Domain Information

Text mining can support the interpretation of the enormous quantity of textual data produced in biomedical field. Recent developments in biomedical text mining include advances in the reliability of the recognition of named entities (NEs) such as specific genes and proteins, as well as movement toward richer representations of the associations of NEs. We argue that this shift in representation should be accompanied by the adoption of a more detailed model of the relations holding between NEs and other relevant domain terms. As a step toward this goal, we study NE–term relations with the aim of defining a detailed, broadly applicable set of relation types based on accepted domain standard concepts for use in corpus annotation and domain information extraction approaches.

scholarly journals Experience of effective antiviral therapy in a liver recipient with recurrent HCV infection genotype 1

2019 ◽  
Vol 20 (4) ◽  
pp. 54-60
Author(s):  
D. V. Umrik ◽  
O. M. Tsiroulnikova ◽  
I. A. Miloserdov ◽  
R. A. Latypov ◽  
E. T. Egorova
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Transplantation ◽  
Antiviral Therapy ◽  
Liver Diseases ◽  
Hcv Infection ◽  
Rapid Progression ◽  
Genotype 1 ◽  
Wide Range ◽  
Common Causes ◽  
Before And After

HCV infection is one of the most common causes leading to the development of terminal liver diseases – cirrhosis and hepatocellular carcinoma, the main treatment for which is orthotopic liver transplantation. However, with continued virus replication, 100% reinfection occurs, which leads to the rapid progression of cirrhosis of the graft and the loss of its function. Standard interferon-containing therapy is ineffective for HCV infection, especially genotype 1, both before and after transplantation, and also has a wide range of adverse events. The article presents the successful experience of treating the recurrence of HCV infection 1 genotype in a patient who underwent liver transplantation and several courses of ineffective antiviral therapy.

Preliminary Study on the Mechanism of Carvacrol Regulating Hepatocellular Carcinoma Based on Network Pharmacology

2019 ◽  
Vol 16 (11) ◽  
pp. 1286-1295
Author(s):  
Sha Li ◽  
Haixia Zhao ◽  
Lidao Bao
Keyword(s):  
Hepatocellular Carcinoma ◽  
Western Blot ◽  
Target Prediction ◽  
Network Pharmacology ◽  
Literature Mining ◽  
Active Compounds ◽  
Prediction Server ◽  
Preliminary Study ◽  
Alignment Analysis ◽  
Endogenous Interactions

Objective: To predict and analyze the target of anti-Hepatocellular Carcinoma (HCC) in the active constituents of Safflower by using network pharmacology. Methods: The active compounds of safflower were collected by TCMSP, TCM-PTD database and literature mining methods. The targets of active compounds were predicted by Swiss Target Prediction server, and the target of anti-HCC drugs was collected by DisGeNET database. The target was subjected to an alignment analysis to screen out Carvacrol, a target of safflower against HCC. The mouse HCC model was established and treated with Carvacrol. The anti-HCC target DAPK1 and PPP2R2A were verified by Western blot and co-immunoprecipitation. Results: A total of 21 safflower active ingredients were predicted. Carvacrol was identified as a possible active ingredient according to the five principles of drug-like medicine. According to Carvacrol's possible targets and possible targets of HCC, three co-targets were identified, including cancer- related are DAPK1 and PPP2R2A. After 20 weeks of Carvacrol treated, Carvacrol group significantly increased on DAPK1 levels and decreased PPP2R2A levels in the model mice by Western blot. Immunoprecipitation confirmed the endogenous interaction between DAPK1 and PPP2R2A. Conclusion: Safflower can regulate the development of HCC through its active component Carvacrol, which can affect the expression of DAPK1 and PPP2R2A proteins, and the endogenous interactions of DAPK1 and PPP2R2A proteins.

scholarly journals Large-scale literature mining to assess the relation between anti-cancer drugs and cancer types

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Chris Bauer ◽  
Ralf Herwig ◽  
Matthias Lienhard ◽  
Paul Prasse ◽  
Tobias Scheffer ◽  
...  
Keyword(s):  
Text Mining ◽  
Knowledge Base ◽  
Survival Data ◽  
Scientific Literature ◽  
Entity Recognition ◽  
Literature Mining ◽  
Cancer Drugs ◽  
Classical Text ◽  
Anti Cancer ◽  
Cancer Types

Abstract Background There is a huge body of scientific literature describing the relation between tumor types and anti-cancer drugs. The vast amount of scientific literature makes it impossible for researchers and physicians to extract all relevant information manually. Methods In order to cope with the large amount of literature we applied an automated text mining approach to assess the relations between 30 most frequent cancer types and 270 anti-cancer drugs. We applied two different approaches, a classical text mining based on named entity recognition and an AI-based approach employing word embeddings. The consistency of literature mining results was validated with 3 independent methods: first, using data from FDA approvals, second, using experimentally measured IC-50 cell line data and third, using clinical patient survival data. Results We demonstrated that the automated text mining was able to successfully assess the relation between cancer types and anti-cancer drugs. All validation methods showed a good correspondence between the results from literature mining and independent confirmatory approaches. The relation between most frequent cancer types and drugs employed for their treatment were visualized in a large heatmap. All results are accessible in an interactive web-based knowledge base using the following link: https://knowledgebase.microdiscovery.de/heatmap. Conclusions Our approach is able to assess the relations between compounds and cancer types in an automated manner. Both, cancer types and compounds could be grouped into different clusters. Researchers can use the interactive knowledge base to inspect the presented results and follow their own research questions, for example the identification of novel indication areas for known drugs.

scholarly journals Lightly supervised acquisition of named entities and linguistic patterns for multilingual text mining

2012 ◽  
Vol 35 (1) ◽  
pp. 87-109 ◽  
Author(s):  
César de Pablo-Sánchez ◽  
Isabel Segura-Bedmar ◽  
Paloma Martínez ◽  
Ana Iglesias-Maqueda
Keyword(s):  
Text Mining ◽  
Named Entities ◽  
Linguistic Patterns ◽  
Multilingual Text

scholarly journals Perceptions and Attitudes towards Mules in a Group of Sodiers

Animals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 1009
Author(s):  
Javiera Lagos ◽  
Manuel Rojas ◽  
Joao B. Rodrigues ◽  
Tamara Tadich
Keyword(s):  
Text Mining ◽  
Pain Perception ◽  
Health Needs ◽  
Emotional Needs ◽  
Mountainous Areas ◽  
Working Capacity ◽  
Perceptions And Attitudes ◽  
Wide Range ◽  
Positive Correlations ◽  
Word Attention

Mules are essential for pack work in mountainous areas, but there is a lack of research on this species. This study intends to assess the perceptions, attitudes, empathy and pain perception of soldiers about mules, to understand the type of human–mule relationship. For this, a survey was applied with closed-ended questions where the empathy and pain perception tools were included and later analyzed through correlations. Open-ended questions were analyzed through text mining. A total of 73 soldiers were surveyed. They had a wide range of ages and years of experience working with equids. Significant positive correlations were found between human empathy, animal empathy and pain perception. Soldiers show a preference for working with mules over donkeys and horses. Text mining analysis shows three clusters associated with the mules’ nutritional, environmental and health needs. In the same line, relevant relations were found for the word “attention” with “load”, “food”, and “harness”. When asked what mules signify for them, two clusters were found, associated with mules’ working capacity and their role in the army. Relevant relations were found between the terms “mountain”, “support”, and “logistics”, and also between “intelligent” and “noble”. To secure mules’ behavioral and emotional needs, future training strategies should include behavior and welfare concepts.

scholarly journals Prognostic Biomarker Identification Through Integrating the Gene Signatures of Hepatocellular Carcinoma Properties

EBioMedicine ◽  
2017 ◽  
Vol 19 ◽  
pp. 18-30 ◽  
Author(s):  
Jialin Cai ◽  
Bin Li ◽  
Yan Zhu ◽  
Xuqian Fang ◽  
Mingyu Zhu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Biomarker ◽  
Biomarker Identification ◽  
Gene Signatures

scholarly journals Genomic heterogeneity: next-generation sequencing enables biomarker identification for hepatocellular carcinoma

2017 ◽  
Vol 11 (7) ◽  
pp. 515-518 ◽  
Author(s):  
Demosthenes E Ziogas ◽  
Efstathios G Lykoudis ◽  
Dimitrios H Roukos ◽  
Georgios K Glantzounis
Keyword(s):  
Hepatocellular Carcinoma ◽  
Next Generation Sequencing ◽  
Next Generation ◽  
Biomarker Identification ◽  
Generation Sequencing

scholarly journals A Review of Recent Advancement in Integrating Omics Data with Literature Mining towards Biomedical Discoveries

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Kalpana Raja ◽  
Matthew Patrick ◽  
Yilin Gao ◽  
Desmond Madu ◽  
Yuyang Yang ◽  
...  
Keyword(s):  
Text Mining ◽  
High Throughput ◽  
Literature Mining ◽  
Biological Knowledge ◽  
Omics Data ◽  
Huge Number ◽  
Automated Generation ◽  
The Past ◽  
Independent Information ◽  
Recent Advancement

In the past decade, the volume of “omics” data generated by the different high-throughput technologies has expanded exponentially. The managing, storing, and analyzing of this big data have been a great challenge for the researchers, especially when moving towards the goal of generating testable data-driven hypotheses, which has been the promise of the high-throughput experimental techniques. Different bioinformatics approaches have been developed to streamline the downstream analyzes by providing independent information to interpret and provide biological inference. Text mining (also known as literature mining) is one of the commonly used approaches for automated generation of biological knowledge from the huge number of published articles. In this review paper, we discuss the recent advancement in approaches that integrate results from omics data and information generated from text mining approaches to uncover novel biomedical information.

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