Search Results - Fiszman, Marcelo

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  1. 1

    Broad-coverage biomedical relation extraction with SemRep by Kilicoglu, Halil, Rosemblat, Graciela, Fiszman, Marcelo, Shin, Dongwook

    Published in BMC bioinformatics (14-05-2020)
    “…In the era of information overload, natural language processing (NLP) techniques are increasingly needed to support advanced biomedical information management…”
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    Journal Article
  2. 2

    Repurposing non-pharmacological interventions for Alzheimer's disease through link prediction on biomedical literature by Xiao, Yongkang, Hou, Yu, Zhou, Huixue, Diallo, Gayo, Fiszman, Marcelo, Wolfson, Julian, Zhou, Li, Kilicoglu, Halil, Chen, You, Su, Chang, Xu, Hua, Mantyh, William G., Zhang, Rui

    Published in Scientific reports (15-04-2024)
    “…Non-pharmaceutical interventions (NPI) have great potential to improve cognitive function but limited investigation to discover NPI repurposing for Alzheimer's…”
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  3. 3

    Constructing a semantic predication gold standard from the biomedical literature by Kilicoglu, Halil, Rosemblat, Graciela, Fiszman, Marcelo, Rindflesch, Thomas C

    Published in BMC bioinformatics (20-12-2011)
    “…Semantic relations increasingly underpin biomedical text mining and knowledge discovery applications. The success of such practical applications crucially…”
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  4. 4

    Investigating the role of interleukin-1 beta and glutamate in inflammatory bowel disease and epilepsy using discovery browsing by Rindflesch, Thomas C, Blake, Catherine L, Cairelli, Michael J, Fiszman, Marcelo, Zeiss, Caroline J, Kilicoglu, Halil

    Published in Journal of biomedical semantics (27-12-2018)
    “…Structured electronic health records are a rich resource for identifying novel correlations, such as co-morbidities and adverse drug reactions. For drug…”
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  5. 5

    Towards a characterization of apparent contradictions in the biomedical literature using context analysis by Rosemblat, Graciela, Fiszman, Marcelo, Shin, Dongwook, Kilicoglu, Halil

    Published in Journal of biomedical informatics (01-10-2019)
    “…[Display omitted] •Semantic predications to identify candidate contradictions in biomedical literature.•Automatic and manual filtering steps identify apparent…”
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  6. 6

    Drug repurposing for COVID-19 via knowledge graph completion by Zhang, Rui, Hristovski, Dimitar, Schutte, Dalton, Kastrin, Andrej, Fiszman, Marcelo, Kilicoglu, Halil

    Published in Journal of biomedical informatics (01-03-2021)
    “…[Display omitted] •Developed a novel drug repurposing approach integrating literature-based discovery and knowledge graph completion.•Constructed a biomedical…”
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  7. 7

    SemMedDB: a PubMed-scale repository of biomedical semantic predications by KILICOGLU, Halil, SHIN, Dongwook, FISZMAN, Marcelo, ROSEMBLAT, Graciela, RINDFLESCH, Thomas C

    Published in Bioinformatics (01-12-2012)
    “…Effective access to the vast biomedical knowledge present in the scientific literature is challenging. Semantic relations are increasingly used in knowledge…”
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  8. 8

    Text summarization as a decision support aid by Workman, T Elizabeth, Fiszman, Marcelo, Hurdle, John F

    “…PubMed data potentially can provide decision support information, but PubMed was not exclusively designed to be a point-of-care tool. Natural language…”
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  9. 9

    Consumer perceptions of satiating and meal replacement bars, built up from cues in packaging information, health claims and nutritional claims by Miraballes, Marcelo, Fiszman, Susana, Gámbaro, Adriana, Varela, Paula

    Published in Food research international (01-10-2014)
    “…Previous research has shown that consumers' sensory and hedonic perceptions could be greatly influenced by the messages highlighted on the front of the…”
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  10. 10

    Exploiting Literature-derived Knowledge and Semantics to Identify Potential Prostate Cancer Drugs by Zhang, Rui, Cairelli, Michael J., Fiszman, Marcelo, Kilicoglu, Halil, Rindflesch, Thomas C., Pakhomov, Serguei V., Melton, Genevieve B.

    Published in Cancer informatics (01-01-2014)
    “…In this study, we report on the performance of an automated approach to discovery of potential prostate cancer drugs from the biomedical literature. We used…”
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  11. 11

    Automatically classifying question types for consumer health questions by Roberts, Kirk, Kilicoglu, Halil, Fiszman, Marcelo, Demner-Fushman, Dina

    “…We present a method for automatically classifying consumer health questions. Our thirteen question types are designed to aid in the automatic retrieval of…”
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  12. 12

    Text summarization in the biomedical domain: A systematic review of recent research by Mishra, Rashmi, Bian, Jiantao, Fiszman, Marcelo, Weir, Charlene R., Jonnalagadda, Siddhartha, Mostafa, Javed, Del Fiol, Guilherme

    Published in Journal of biomedical informatics (01-12-2014)
    “…[Display omitted] •First systematic review of text summarization in the biomedical domain.•The study found a predominance of methods producing extractive…”
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  13. 13

    Networks of neuroinjury semantic predications to identify biomarkers for mild traumatic brain injury by Cairelli, Michael J, Fiszman, Marcelo, Zhang, Han, Rindflesch, Thomas C

    Published in Journal of biomedical semantics (18-05-2015)
    “…Mild traumatic brain injury (mTBI) has high prevalence in the military, among athletes, and in the general population worldwide (largely due to falls)…”
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  14. 14

    An Ensemble Method for Spelling Correction in Consumer Health Questions by Kilicoglu, Halil, Fiszman, Marcelo, Roberts, Kirk, Demner-Fushman, Dina

    “…Orthographic and grammatical errors are a common feature of informal texts written by lay people. Health-related questions asked by consumers are a case in…”
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  15. 15

    Discovering novel drug-supplement interactions using SuppKG generated from the biomedical literature by Schutte, Dalton, Vasilakes, Jake, Bompelli, Anu, Zhou, Yuqi, Fiszman, Marcelo, Xu, Hua, Kilicoglu, Halil, Bishop, Jeffrey R., Adam, Terrence, Zhang, Rui

    Published in Journal of biomedical informatics (01-07-2022)
    “…[Display omitted] •Developed a novel methodology to create SuppKG, a dietary supplement specialized knowledge graph.•Produced a text mining tool, SemRepDS,…”
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  16. 16

    Classification of clinically useful sentences in clinical evidence resources by Morid, Mohammad Amin, Fiszman, Marcelo, Raja, Kalpana, Jonnalagadda, Siddhartha R., Del Fiol, Guilherme

    Published in Journal of biomedical informatics (01-04-2016)
    “…[Display omitted] •We investigated automated extraction of clinically useful sentences from UpToDate.•A Kernel-based Bayes Network classifier was applied on…”
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  17. 17

    The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text by Rindflesch, Thomas C, Fiszman, Marcelo

    Published in Journal of biomedical informatics (01-12-2003)
    “…Interpretation of semantic propositions in free-text documents such as MEDLINE citations would provide valuable support for biomedical applications, and…”
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  18. 18

    Sortal anaphora resolution to enhance relation extraction from biomedical literature by Kilicoglu, Halil, Rosemblat, Graciela, Fiszman, Marcelo, Rindflesch, Thomas C

    Published in BMC bioinformatics (14-04-2016)
    “…Entity coreference is common in biomedical literature and it can affect text understanding systems that rely on accurate identification of named entities, such…”
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  19. 19

    Clustering cliques for graph-based summarization of the biomedical research literature by Zhang, Han, Fiszman, Marcelo, Shin, Dongwook, Wilkowski, Bartlomiej, Rindflesch, Thomas C

    Published in BMC bioinformatics (07-06-2013)
    “…Graph-based notions are increasingly used in biomedical data mining and knowledge discovery tasks. In this paper, we present a clique-clustering method to…”
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  20. 20

    Automatically extracting sentences from Medline citations to support clinicians' information needs by Jonnalagadda, Siddhartha Reddy, Del Fiol, Guilherme, Medlin, Richard, Weir, Charlene, Fiszman, Marcelo, Mostafa, Javed, Liu, Hongfang

    “…Online health knowledge resources contain answers to most of the information needs raised by clinicians in the course of care. However, significant barriers…”
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