Using the BITOLA system to identify candidate genes for Parkinson's disease

Karić, Amela; Karic, Alen

doi:10.17305/bjbms.2011.2572

Cited by 9 publications

(6 citation statements)

References 19 publications

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“…text mining can pose a hypothetical question or find potential relationships (that may otherwise be ignored) between entities of interest. One relevant area of research is literature-based discovery (LBD); some researchers have successfully used this strategy to promote new discoveries for diseases (Hristovski, Rindflesch & Peterlin, 2013;Karic et al, 2011;Swanson, 1986;Karić & Karić, 2011;Zhan et al, 2017;Swanson, 1988). One of the most well-known examples of the successful use of LBD is the discovery that patients with Raynaud's disease could benefit from the consumption of fish oils (Swanson, 1986).…”

Section: Introductionmentioning

confidence: 99%

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Identification of biological pathways and genes associated with neurogenic heterotopic ossification by text mining

Zhang

Zhan

Kou

et al. 2020

PeerJ

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Background Neurogenic heterotopic ossification is a disorder of aberrant bone formation affecting one in five patients sustaining a spinal cord injury or traumatic brain injury (SCI-TBI-HO). However, the underlying mechanisms of SCI-TBI-HO have proven difficult to elucidate. The aim of the present study is to identify the most promising candidate genes and biological pathways for SCI-TBI-HO. Methods In this study, we used text mining to generate potential explanations for SCI-TBI-HO. Moreover, we employed several additional datasets, including gene expression profile data, drug data and tissue-specific gene expression data, to explore promising genes that associated with SCI-TBI-HO. Results We identified four SCI-TBI-HO-associated genes, including GDF15, LDLR, CCL2, and CLU. Finally, using enrichment analysis, we identified several pathways, including integrin signaling, insulin pathway, internalization of ErbB1, urokinase-type plasminogen activator and uPAR-mediated signaling, PDGFR-beta signaling pathway, EGF receptor (ErbB1) signaling pathway, and class I PI3K signaling events, which may be associated with SCI-TBI-HO. Conclusions These results enhance our understanding of the molecular mechanisms of SCI-TBI-HO and offer new leads for researchers and innovative therapeutic strategies.

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Section: Introductionmentioning

confidence: 99%

“…Hristovski, Rindflesch & Peterlin (2013) and Weeber, Kors & Mons (2005) have provided a detailed review of the different functions of mining tools available in the literature. Some researchers have successfully used text mining tools to identify candidate genes for diseases (Hristovski, Rindflesch & Peterlin, 2013;Karić & Karić, 2011).…”

Section: Introductionmentioning

confidence: 99%

Identification of biological pathways and genes associated with neurogenic heterotopic ossification by text mining

Zhang

Zhan

Kou

et al. 2020

PeerJ

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“…The biomedical support discovery system (BITOLA) is a sophisticated bioinformatics tool that enables new discoveries, such as mining new information from the literature without using patient tissue samples, especially for identification of key candidates, and finding potentially new relationships among various biomedical concepts [11, 12]. Some researchers have used the text mining tools to identify candidate genes for diseases [13], such as multiple sclerosis and bilateral polymicrogyria [12, 14, 15]. In addition, using the BITOLA system, genes neural cell adhesion molecule 1 ( NCAM1 ) and CD4 were identified as potential candidate genes in the interaction between depression and oral lichen planus [16].…”

Section: Introductionmentioning

confidence: 99%

Using literature-based discovery to identify candidate genes for the interaction between myocardial infarction and depression

Dai

Yang

et al. 2019

BMC Med Genet

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Background A multidirectional relationship has been demonstrated between myocardial infarction (MI) and depression. However, the causal genetic factors and molecular mechanisms underlying this interaction remain unclear. The main purpose of this study was to identify potential candidate genes for the interaction between the two diseases. Methods Using a bioinformatics approach and existing gene expression data in the biomedical discovery support system (BITOLA), we defined the starting concept X as “Myocardial Infarction” and end concept Z as “Major Depressive Disorder” or “Depressive disorder”. All intermediate concepts relevant to the “Gene or Gene Product” for MI and depression were searched. Gene expression data and tissue-specific expression of potential candidate genes were evaluated using the Human eFP (electronic Fluorescent Pictograph) Browser, and intermediate concepts were filtered by manual inspection. Results Our analysis identified 128 genes common to both the “MI” and “depression” text mining concepts. Twenty-three of the 128 genes were selected as intermediates for this study, 9 of which passed the manual filtering step. Among the 9 genes, LCAT , CD4 , SERPINA1 , IL6 , and PPBP failed to pass the follow-up filter in the Human eFP Browser, due to their low levels in the heart tissue. Finally, four genes ( GNB3 , CNR1 , MTHFR , and NCAM1 ) remained. Conclusions GNB3 , CNR1 , MTHFR , and NCAM1 are putative new candidate genes that may influence the interactions between MI and depression, and may represent potential targets for therapeutic intervention.

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“…These terms (https://www.nlm.nih.gov/pubs/factsheets/mesh.html) summarize key biomedical concepts for each paper, and to expand coverage and refine relevance, they are revised annually (or daily for SCRs) [26] (https://www.nlm.nih.gov/pubs/factsheets/mesh.html). The co-occurrence of MeSH terms with text-mined gene names was used to cross-reference genes and predict diseases that shared disease characteristics and chromosomal locations [27, 28].…”

Section: Introductionmentioning

confidence: 99%

Automated literature mining and hypothesis generation through a network of Medical Subject Headings

Wilson

et al. 2018

Preprint

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25 The scientific literature is vast, growing, and increasingly specialized, making it difficult to 26 connect disparate observations across subfields. To address this problem, we sought to develop 27 automated hypothesis generation by networking at scale the MeSH terms curated by the National 28 Library of Medicine. The result is a Mesh Term Objective Reasoning (MeTeOR) approach that 29 tallies associations among genes, drugs and diseases from PubMed and predicts new ones.30 Comparisons to reference databases and algorithms show MeTeOR tends to be more reliable. We 31 also show that many predictions based on the literature prior to 2014 were published 32 subsequently. In a practical application, we validated experimentally a surprising new 33 association found by MeTeOR between novel Epidermal Growth Factor Receptor (EGFR) 34 associations and CDK2. We conclude that MeTeOR generates useful hypotheses from the 35 literature (http://meteor.lichtargelab.org/). AUTHOR SUMMARY37 The large size and exponential expansion of the scientific literature forms a bottleneck to 38 accessing and understanding published findings. Manual curation and Natural Language 39 Processing (NLP) aim to address this bottleneck by summarizing and disseminating the 40 knowledge within articles as key relationships (e.g. TP53 relates to Cancer). However, these 41 methods compromise on either coverage or accuracy, respectively. To mitigate this compromise, 42 we proposed using manually-assigned keywords (MeSH terms) to extract relationships from the 43 publications and demonstrated a comparable coverage but higher accuracy than current NLP 44 methods. Furthermore, we combined the extracted knowledge with semi-supervised machine 45 learning to create hypotheses to guide future work and discovered a direct interaction between 46 two important cancer genes. 47 48 49 3 50 INTRODUCTION 51 It is difficult to keep abreast of new publications. Currently, PubMed contains over 28 million 52 papers (http://www.ncbi.nlm.nih.gov/pubmed)-3 million more than three years ago. This steady 53 accumulation of findings gives rise to a large number of latent connections that Literature-Based 54 Discovery (LBD) seeks to systematically recognize and integrate [1], such as Swanson's original 55 finding linking fish oil to the treatment of Raynaud's disease [2]. Since this original analysis, 56 LBD has been extensively replicated, automated and expanded [3-10], leading to new patterns of 57 inference -e.g. locating opposing actions of a disease and a drug on given physiological 58 functions [11] -and to new discoveries [12]. Successes include the automated discovery of 59 protein functions [13, 14] and of the genetic bases of disease [15, 16], as well as the stratification 60 of patient phenotypes [17] and outcomes [18]. 61A limitation of LBD, however, is its dependence on knowledge extraction. It either relies 62 on human curation, which is not scalable, or on comprehensive text-mining, for which 63 algorithms are less accurate [19, 20]. One of the largest curated m...

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Using the BITOLA system to identify candidate genes for Parkinson's disease

Cited by 9 publications

References 19 publications

Identification of biological pathways and genes associated with neurogenic heterotopic ossification by text mining

Identification of biological pathways and genes associated with neurogenic heterotopic ossification by text mining

Using literature-based discovery to identify candidate genes for the interaction between myocardial infarction and depression

Automated literature mining and hypothesis generation through a network of Medical Subject Headings

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