DeepLife: An Entity-aware Search, Analytics and Exploration Platform for Health and Life Sciences

Erñst, Patrick; Siu, Amy; Milchevski, Dragan; Hoffart, Johannes; Weikum, Gerhard

doi:10.18653/v1/p16-4004

Cited by 13 publications

(11 citation statements)

References 17 publications

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“…Luo et al [6] proposed an algorithm that translates free-text sentences from pathology reports into a graph representation, where the nodes of the graph represent the concepts (e.g., genes and proteins) and the edges indicate the syntactic dependency links between these concepts. Further, a number of researchers have attempted to integrate different information sources to form a large KB [7]. …”

Section: Related Workmentioning

confidence: 99%

OC-2-KB: A software pipeline to build an evidence-based obesity and cancer knowledge base

Lossio-Ventura

Hogan

Modave

et al. 2017

2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Obesity has been linked to several types of cancer. Access to adequate health information activates people’s participation in managing their own health, which ultimately improves their health outcomes. Nevertheless, the existing online information about the relationship between obesity and cancer is heterogeneous and poorly organized. A formal knowledge representation can help better organize and deliver quality health information. Currently, there are several efforts in the biomedical domain to convert unstructured data to structured data and store them in Semantic Web knowledge bases (KB). In this demo paper, we present, OC-2-KB (Obesity and Cancer to Knowledge Base), a system that is tailored to guide the automatic KB construction for managing obesity and cancer knowledge from free-text scientific literature (i.e., PubMed abstracts) in a systematic way. OC-2-KB has two important modules which perform the acquisition of entities and the extraction then classification of relationships among these entities. We tested the OC-2-KB system on a data set with 23 manually annotated obesity and cancer PubMed abstracts and created a preliminary KB with 765 triples. We conducted a preliminary evaluation on this sample of triples and reported our evaluation results.

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Section: Related Workmentioning

confidence: 99%

OC-2-KB: A software pipeline to build an evidence-based obesity and cancer knowledge base

Lossio-Ventura

Hogan

Modave

et al. 2017

2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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“…DigSee [8] performs keyphrase detection for sentences describing the relationship between genes and diseases. DeepLife [9] also performs entity recognition and, in contrast to the previous tools which all consider only PubMed abstracts, also indexes certain web sites and social media content. RefMED [10] facilitates search in PubMed by user relevance feedback.…”

Section: Introductionmentioning

confidence: 99%

VIST - a Variant-Information Search Tool for precision oncology

et al. 2019

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Background Diagnosis and treatment decisions in cancer increasingly depend on a detailed analysis of the mutational status of a patient’s genome. This analysis relies on previously published information regarding the association of variations to disease progression and possible interventions. Clinicians to a large degree use biomedical search engines to obtain such information; however, the vast majority of scientific publications focus on basic science and have no direct clinical impact. We develop the Variant-Information Search Tool (VIST), a search engine designed for the targeted search of clinically relevant publications given an oncological mutation profile. Results VIST indexes all PubMed abstracts and content from ClinicalTrials.gov. It applies advanced text mining to identify mentions of genes, variants and drugs and uses machine learning based scoring to judge the clinical relevance of indexed abstracts. Its functionality is available through a fast and intuitive web interface. We perform several evaluations, showing that VIST’s ranking is superior to that of PubMed or a pure vector space model with regard to the clinical relevance of a document’s content. Conclusion Different user groups search repositories of scientific publications with different intentions. This diversity is not adequately reflected in the standard search engines, often leading to poor performance in specialized settings. We develop a search engine for the specific case of finding documents that are clinically relevant in the course of cancer treatment. We believe that the architecture of our engine, heavily relying on machine learning algorithms, can also act as a blueprint for search engines in other, equally specific domains. VIST is freely available at https://vist.informatik.hu-berlin.de/ Electronic supplementary material The online version of this article (10.1186/s12859-019-2958-3) contains supplementary material, which is available to authorized users.

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“…However, each of these KBs is highly specialized and covers only a relatively narrow topic within life sciences (Ernst et al, 2016). Also, there is limited inter-linkage between entities in these KBs (e.g., between drug, disease and gene entities).…”

Section: Introductionmentioning

confidence: 99%

“…• Previous Efforts and Limitations. In life sciences domain, recent studies (Ernst et al, 2016;Szklarczyk et al, 2014;Thomas et al, 2012;Kim et al, 2008) rely on biomedical entity information associated with the documents to support entity-centric literature search. Most existing information retrieval systems exploit either the MeSH terms manually annotated for each PubMed article (Kim et al, 2008) or textual mentions of biomedical entities automatically recognized within the documents (Thomas et al, 2012), to capture the entity-document relatedness.…”

Section: Introductionmentioning

confidence: 99%

“…• Limited Diversity and Coverage: There exist several biomedical knowledge bases (KBs) (e.g., Gene Ontology, UniProt, STRING (Szklarczyk et al, 2014), Literome (Poon et al, 2014)) that support search and data exploration functionality. However, each of these KBs is highly specialized and covers only a relatively narrow topic within life sciences (Ernst et al, 2016). Also, there is limited inter-linkage between entities in these KBs (e.g., between drug, disease and gene entities).…”

Section: Introductionmentioning

confidence: 99%

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

Ren

Shen

et al. 2017

Proceedings of ACL 2017, System Demonstrations

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Search engines running on scientific literature have been widely used by life scientists to find publications related to their research. However, existing search engines in the life-science domain, such as PubMed, have limitations when applied to exploring and analyzing factual knowledge (e.g., disease-gene associations) in massive text corpora. These limitations are mainly due to the problems that factual information exists as an unstructured form in text, and also keyword and MeSH term-based queries cannot effectively imply semantic relations between entities. This demo paper presents the Life-iNet system to address the limitations in existing search engines on facilitating life sciences research. Life-iNet automatically constructs structured networks of factual knowledge from large amounts of background documents, to support efficient exploration of structured factual knowledge in the unstructured literature. It also provides functionalities for finding distinctive entities for given entity types, and generating hypothetical facts to assist literaturebased knowledge discovery (e.g., drug target prediction).

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DeepLife: An Entity-aware Search, Analytics and Exploration Platform for Health and Life Sciences

Cited by 13 publications

References 17 publications

OC-2-KB: A software pipeline to build an evidence-based obesity and cancer knowledge base

OC-2-KB: A software pipeline to build an evidence-based obesity and cancer knowledge base

VIST - a Variant-Information Search Tool for precision oncology

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

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