LitVar: a semantic search engine for linking genomic variant data in PubMed and PMC

Allot, Alexis; Peng, Yifan; Wei, Chih-Hsuan; Lee, Kyubum; Phan, Lon; Lu, Zhiyong

doi:10.1093/nar/gky355

Cited by 111 publications

(90 citation statements)

References 27 publications

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“…After invoking variant2literature on 2,277 variants against the 206 papers, 3,011 pairs were reported as positives, resulting a recall rate of 90.86% and a precision rate of 97.08%. For comparison, we performed the same query of variant set on LitVar [4]. By uploading the 2,277 variants to LitVar, 1,138 pairs were reported as positives, resulting a recall rate of 32.76% and a precision rate of 92.62%.…”

Section: Resultsmentioning

confidence: 99%

“…The annotation of variants involves a critical step that finds literature to explain the potential influence of a discovered variant on protein functions, cell behaviors, organ normality, etc. In this regard, many efforts have been made to improve the search performance [4,5]. Even though, we observed that the sensitivity of variant queries still has space to increase.…”

Section: Introductionmentioning

confidence: 84%

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variant2literature: full text literature search for genetic variants

Lin¹,

Lu²,

Chen³

et al. 2019

Preprint

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Motivation: Whole genome sequencing (WGS) by next-generation sequencing produces millions of variants for an individual. The retrieval of biomedical literature for such a large number of genetic variants remains challenging, because in many cases the variants are only present in tables as images, or in the supplementary documents of which the file formats are diverse. Results: The proposed tool named variant2literature from the TaiGenomics (Toolkits for AI genomics) resolves the problem by incorporating text recognition with image processing. In addition to the adoption of advanced text retrieval, the recall rate of finding the literature containing the variants of interest is further improved by employing the skill of variant normalization. Different variant presentations are transformed into chromosome coordinates (standard VCF format) such that false negatives can be largely avoided. variant2literature is available in two ways. First, a web-based interface is provided to search all the literature in PMC Open Access Subset. Second, the command-line executable can be downloaded such that the users are free to search all the files in a specified directory locally.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 84%

variant2literature: full text literature search for genetic variants

Lin¹,

Lu²,

Chen³

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…LitVar a semantic search engine is proposed by Allott et al [12]. The search engine is used to search the genome related data in the database.…”

Section: Related Workmentioning

confidence: 99%

Enhancing the Performance of Semantic Search in Bengali using Neural Net and other Classification Techniques

Das¹,

Saha²

2020

IJEAT

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To know the information from the internet searching is one of the most important part for any user. In case of ‘Syntactic Search’ keyword based matching technique is used. Search accuracy is improved applying the filter like location, preference, user-history etc. However, it can happen that the user query or question and the best available answer or result in the internet domain has no terms in common or ignorable number of terms is common. In such case syntactic search cannot give the desired output. The role of ‘Semantic Search’ becomes prevalent in this scenario. The execution of semantic search faces challenge due to unavailability of resources like WordNet, Ontology, Annotation etc. An end to end algorithm is described to improve the accuracy of the semantic search in this work. Four classification techniques are used. They are ANN, Decision Tree, SVM and Naïve Bayes. Dataset is provided from the TDIL project of the Ministry of Electronics and IT, Govt. of India. The repository contains 86 categories of text having more than a million sentences. After getting the impressive result for the Bengali language test run was done for other Indian languages and a very good result is achieved. This research is extremely useful for the automatic question answering system, semantic similarity analysis, e-governance and m- governance.

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“…NER is followed by a normalization step mapping the entities to a fixed set of identifiers, such as HGNC gene symbols [Yates et al, 2017] or Disease Ontology terms [Kibbe et al, 2015]. General approaches such as LINNAEUS [Gerner et al, 2010], Tagger [Pafilis et al, 2013], taggerOne [Leaman and Lu, 2016], or OGER [Basaldella et al, 2017] recognize diverse biomedical entities in text, while specialized tools recognize mentions of genetic variants [Allot et al, 2018] or chemicals [Jessop et al, 2011].…”

Section: Introductionmentioning

confidence: 99%

CoCoScore: Context-aware co-occurrence scoring for text mining applications using distant supervision

Junge

Jensen

2018

Preprint

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Information extraction by mining the scientific literature is key to uncovering relations between biomedical entities. Most existing approaches based on natural language processing extract relations from single sentence-level co-mentions, ignoring co-occurrence statistics over the whole corpus. Existing approaches counting entity co-occurrences ignore the textual context of each co-occurrence. We propose a novel corpus-wide co-occurrence scoring approach to relation extraction that takes the textual context of each co-mention into account. Our method, called CoCoScore, scores the certainty of stating an association for each sentence that co-mentions two entities. CoCoScore is trained using distant supervision based on a gold-standard set of associations between entities of interest. Instead of requiring a manually annotated training corpus, co-mentions are labeled as positives/negatives according to their presence/absence in the gold standard. We show that CoCoScore outperforms previous approaches in identifying human disease-gene and tissue-gene associations as well as in identifying physical and functional protein-protein associations in different species. CoCoScore is a versatile text-mining tool to uncover pairwise associations via co-occurrence mining, within and beyond biomedical applications. CoCoScore is available at: https://github.com/JungeAlexander/cocoscore

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LitVar: a semantic search engine for linking genomic variant data in PubMed and PMC

Cited by 111 publications

References 27 publications

variant2literature: full text literature search for genetic variants

variant2literature: full text literature search for genetic variants

Enhancing the Performance of Semantic Search in Bengali using Neural Net and other Classification Techniques

CoCoScore: Context-aware co-occurrence scoring for text mining applications using distant supervision

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