Life-iNet: A Structured Network-Based Knowledge Exploration and
            Analytics System for Life Sciences

Ren, Xiang; Shen, Jiaming; Qu, Meng; Wang, Xuan; Wu, Zeqiu; Zhu, Qi; Jiang, Meng; Tao, Fangbo; Sinha, Saurabh; Liem, David A.; Ping, Peipei; Weinshilboum, Richard M.

doi:10.18653/v1/p17-4010

Cited by 13 publications

(8 citation statements)

References 13 publications

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“…While there have been many algorithms proposed around the concept of co-occurrence counting, we found only three tools comparable to KinderMiner available: FACTA+ 14 , Polysearch2 15 , and BEST 16 . There are other similar sounding tools like DeepLife 17 and Life-iNet 18 , but DeepLife serves more as a general biomedical web search than a term ranking tool and Life-iNet does not appear to have any code or application available for use. All three of FACTA+, Polysearch2, and BEST allow the user to rank a list of biomedical entities (analogous to the KinderMiner target terms) by their association with a query entity (analogous to the KinderMiner key phrase), and all allow general text entry for the query entity.…”

Section: Resultsmentioning

confidence: 99%

KinderMiner Web: a simple web tool for ranking pairwise associations in biomedical applications

Kuusisto

Steill

et al. 2021

F1000Res

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Many important scientific discoveries require lengthy experimental processes of trial and error and could benefit from intelligent prioritization based on deep domain understanding. While exponential growth in the scientific literature makes it difficult to keep current in even a single domain, that same rapid growth in literature also presents an opportunity for automated extraction of knowledge via text mining. We have developed a web application implementation of the KinderMiner algorithm for proposing ranked associations between a list of target terms and a key phrase. Any key phrase and target term list can be used for biomedical inquiry. We built the web application around a text index derived from PubMed. It is the first publicly available implementation of the algorithm, is fast and easy to use, and includes an interactive analysis tool. The KinderMiner web application is a public resource offering scientists a cohesive summary of what is currently known about a particular topic within the literature, and helping them to prioritize experiments around that topic. It performs comparably or better to similar state-of-the-art text mining tools, is more flexible, and can be applied to any biomedical topic of interest. It is also continually improving with quarterly updates to the underlying text index and through response to suggestions from the community. The web application is available at https://www.kinderminer.org.

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Section: Resultsmentioning

confidence: 99%

KinderMiner Web: a simple web tool for ranking pairwise associations in biomedical applications

Kuusisto

Steill

et al. 2021

F1000Res

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“…To extend this pipeline to other domains, we need to use an extractor that can effectively recognize entities and relations tailored for those domains. For example, to work on the life science domain, we should use an extractor that recognizes concepts of drugs and diseases rather than tasks and methods (Ren et al, 2017).…”

Section: Knowledge Graph Constructionmentioning

confidence: 99%

“…Besides the mentioned platforms, in the biomedical domain, there are many efforts to integrate knowledge bases into literature analysis systems. Similar to our fact list feature, Life-iNet (Ren et al, 2017) and BioTextQuest+ (Papanikolaou et al, 2014) are platforms that focus on exploring factual knowledge of a queried entity in the knowledge base and providing a list of supported documents. DeepLife (Ernst et al, 2016) and SetSearch+ (Shen et al, 2018) are entity-aware literature search engines that broaden results by expanding the query with related entities in the knowledge base.…”

Section: Scientific Literature Discovery Platformsmentioning

confidence: 99%

ESRA: Explainable Scientific Research Assistant

Hongwimol¹,

Kehasukcharoen²,

Laohawarutchai³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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We introduce Explainable Scientific Research Assistant (ESRA), a literature discovery platform that augments search results with relevant details and explanations, aiding users in understanding more about their queries and the returned papers beyond existing literature search systems. Enabled by a knowledge graph we extracted from abstracts of 23k papers on the arXiv's cs.CL category, ESRA provides three main features: explanation (for why a paper is returned to the user), list of facts (that are relevant to the query), and graph visualization (drawing connections between the query and each paper with surrounding related entities). The experimental results with humans involved show that ESRA can accelerate the users' search process with paper explanations and helps them better explore the landscape of the topics of interest by exploiting the underlying knowledge graph. We provide the ESRA web application at http://esra.cp. eng.chula.ac.th/.

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“…As a result, researchers may choose other academic search platforms, such as CiteSeerX [34], AMiner [31], PubMed [21], Microsoft Academic Search [30] and Semantic Scholar [39]. Research efforts of many such systems focus on the analytical tasks of scholar data such as author name disambiguation [31], paper importance modeling [29], and entity-based distinctive summarization [27]. However, this work focuses on ad-hoc document retrieval and ranking in academic search.…”

Section: Academic Searchmentioning

confidence: 99%

Entity Set Search of Scientific Literature

Shen

Xiao

et al. 2018

The 41st International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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Literature search is critical for any scientific research. Different from Web or general domain search, a large portion of queries in scientific literature search are entity-set queries, that is, multiple entities of possibly different types. Entity-set queries reflect user's need for finding documents that contain multiple entities and reveal inter-entity relationships and thus pose non-trivial challenges to existing search algorithms that model each entity separately. However, entity-set queries are usually sparse (i.e., not so repetitive), which makes ineffective many supervised ranking models that rely heavily on associated click history. To address these challenges, we introduce SetRank, an unsupervised ranking framework that models inter-entity relationships and captures entity type information. Furthermore, we develop a novel unsupervised model selection algorithm, based on the technique of weighted rank aggregation, to automatically choose the parameter settings in SetRank without resorting to a labeled validation set. We evaluate our proposed unsupervised approach using datasets from TREC Genomics Tracks and Semantic Scholar's query log. The experiments demonstrate that SetRank significantly outperforms the baseline unsupervised models, especially on entity-set queries, and our model selection algorithm effectively chooses suitable parameter settings.

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Life-iNet: A Structured Network-Based Knowledge Exploration and Analytics System for Life Sciences

Cited by 13 publications

References 13 publications

KinderMiner Web: a simple web tool for ranking pairwise associations in biomedical applications

KinderMiner Web: a simple web tool for ranking pairwise associations in biomedical applications

ESRA: Explainable Scientific Research Assistant

Entity Set Search of Scientific Literature

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