Literature-related discovery (LRD): Water purification

Kostoff, Ronald N.; Solka, Jeffrey L.; Rushenberg, Robert L.; Wyatt, Jeffrey A.

doi:10.1016/j.techfore.2007.11.009

Cited by 36 publications

(16 citation statements)

References 26 publications

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“…Vários estudos baseiam-se em técnicas semânticas para extrair relacionamentos, como Latent Semantic Indexing (KOSTOFF et al, 2008) WIDDOWS, 2010). O conhecimento semântico contido em ontologias ou terminologias biomédicas também tem sido utilizado para a extração de relacionamentos (KANG et al, 2011).…”

Section: Extração De Relacionamentosunclassified

Mineração de textos biomédicos: uma revisão bibliométrica

Woszezenki¹,

Gonçalves

2013

Perspect. ciênc. inf.

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A mineração de textos vem sendo, cada vez mais, empregada para automatizar o processo de extração de informações importantes, contidas em textos biomédicos, possibilitando que os pesquisadores fiquem a par do desenvolvimento da biomedicina. Considerando a importância deste campo de pesquisa, este artigo apresenta um mapeamento das publicações científicas sobre mineração de textos biomédicos e discute as principais tarefas desse campo de pesquisa, as quais os pesquisadores têm dedicado maior atenção. Para isso, foi utilizada a bibliometria, uma técnica que permite analisar o desenvolvimento de um campo da ciência, visando identificar suas características. O mapeamento apresentado promove o conhecimento sobre o histórico e o estado atual do campo de pesquisa e disponibiliza insumos, que permitem enriquecer a discussão sobre os possíveis rumos que as pesquisas, na área, têm tomado e as prováveis tendências científicas para os pesquisadores e interessados no tema.Palavras-chave: Mineração de textos; Descoberta baseada em literatura; Bioinformática.

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Section: Extração De Relacionamentosunclassified

Mineração de textos biomédicos: uma revisão bibliométrica

Woszezenki¹,

Gonçalves

2013

Perspect. ciênc. inf.

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“…In 2008-2009, our research group published 1) a detailed review of the complete LRDI literature in ARIST [1], 2) a journal Special Issue devoted to the LRDI methodology and applications circa 2007 [2][3][4][5][6][7][8][9], and 3) a comprehensive report that covered all the studies in the journal Special Issue and some raw data as well [10]. In 2011, I published an LRDI study on the SARS pandemic/coronavirus [11].…”

Section: Purpose Of Articlementioning

confidence: 99%

“…This work culminated in Guest Editing a Special Issue of Technological Forecasting and Social Change in 2008, which included eight papers from our group [2][3][4][5][6][7][8][9]. Four of the papers were on medical topics (Raynaud's phenomenon [RP] [4], cataracts [5], Parkinson's disease [PD] [6], multiple sclerosis [MS] [7]), and the fifth was on a technical non-medical topic (alternative water desalinization approaches) [8]. All of the studies were of the open discovery system (ODS) type, where one starts with a problem (e.g., the disease) and identifies solutions (e.g., preventatives, treatments).…”

Section: Journal Special Issue On Lrdi Technique and Medical/technicamentioning

confidence: 99%

Literature-related discovery and innovation — update

Kostoff

2012

Technological Forecasting and Social Change

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Literature-Related Discovery and Innovation (LRDIformerly LRDliterature-related discovery) integrates 1) discovery generation from disparate literatures with 2) the wealth of knowledge contained in prior art to 3) potentially reverse chronic and infectious diseases and/or 4) potentially solve technical problems that appear intractable. This article describes the evolution of LRDI by the author and the insights gained/lessons learned over the past decade. To illustrate the potential power of LRDI, the article emphasizes the relationship between the results of our 2008 LRDI multiple sclerosis (MS) study and a recent demonstration of MS reversal. Lessons learned from the six LRDI medical studies done so far include:⁎ The main operational problem in the author's LRDI approach is selecting the most important concepts from extremely large volumes of potential discovery retrieval. This is contrary to most published LRDI research, where the discovery focus is searching for rare events. ⁎ It is important to have topical specialist(s) working closely with information technologist(s); the topical specialist(s) applies judgment in selecting the most important concepts. ⁎ A functional form of the information retrieval query with proximity searching capability provides highly selective filtering for discovery retrieval and core prevention/treatment retrieval; the functional form of the query with proximity searching capability allows the use of full-text for discovery and core prevention/treatment. ⁎ Bibliographic coupling (identifying papers that share common references) combined with text-based relationships strengthens selection for potential discovery further. ⁎ Having 'skin-in-the-game' (being affected personally) relative to the medical outcome is a strong incentive to do whatever is necessary to solve the research problem. ⁎ Hormesis is critical to healing; relatively modest doses of stimuli tend to be beneficial, whereas relatively large doses may be harmful. The synergy of hormetic treatment doses produces effects larger than combinations of individual doses and requires smaller doses when combined; the synergy of hormetic doses allows conversion of megadoses of nutrients typically reported in lab/clinical studies to physiological (food-level) doses and associated increased safety. ⁎ Co-promoters (combinations of toxic stimuli required to produce disease symptoms) are extremely important for explaining seemingly conflicting results; if true co-promotion is present, elimination of one of the co-promoters may be adequate for removing symptoms, even though the overall problem persists. ⁎ Prior art (potential treatments already published in the literature but not pursued by mainline medicine) may have much to contribute to potentially solve many serious medical problems; much of prior art is overlooked, especially low-tech prior art (e.g., foods, food extracts, herbs, etc.).⁎ Systemic and focused treatments are both necessary components of healing, but neither will be fully, or many times even partially, effective until t...

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“…Reference [5] introduced the methodology of LRD. His team used five instances to verify the effective of LRD [7][8] [9][10] [11] . A credible method of LBD needs multiple literatures with four characters [5] , as follow, (1) These multiple literatures should be complementary for solving the problem.…”

Section: Literature Related Discovery-lrdmentioning

confidence: 99%