Where is the Discovery in Literature-Based Discovery?

Kostoff, Ronald N.

doi:10.1007/978-3-540-68690-3_5

Cited by 5 publications

(6 citation statements)

References 43 publications

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“…We are aware that many new interactions inferred by our model have already been reported in scientific papers. However, we have observed that these findings had not received patent protection at the USPTO until 2005 and we have used some of these scientific papers as validation of our results, mainly due to the inexistence of textual collections currently available for validating literature-based discovery systems [21,22,44]. For a production system we should index as much as possible of the current biological literature sources in order to filter prior art.…”

Section: Discussionmentioning

confidence: 92%

“…In literature-based discovery we must avoid the inference of interactions already stated in the literature. Kostoff [ 21 , 22 ] has discussed this problem and issues related to the quantity and quality of interactions. Kostoff et al [ 23 ] have presented a generic methodology for literature-based discovery and have used this methodology to identify interactions concerning Raynaud’s phenomenon [ 24 ], cataracts [ 25 ], Parkinson’s disease [ 26 ], multiple sclerosis [ 27 ] and water purification [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

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Can the vector space model be used to identify biological entity activities?

et al. 2011

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BackgroundBiological systems are commonly described as networks of entity interactions. Some interactions are already known and integrate the current knowledge in life sciences. Others remain unknown for long periods of time and are frequently discovered by chance. In this work we present a model to predict these unknown interactions from a textual collection using the vector space model (VSM), a well known and established information retrieval model. We have extended the VSM ability to retrieve information using a transitive closure approach. Our objective is to use the VSM to identify the known interactions from the literature and construct a network. Based on interactions established in the network our model applies the transitive closure in order to predict and rank new interactions.ResultsWe have tested and validated our model using a collection of patent claims issued from 1976 to 2005. From 266,528 possible interactions in our network, the model identified 1,027 known interactions and predicted 3,195 new interactions. Iterating the model according to patent issue dates, interactions found in a given past year were often confirmed by patent claims not in the collection and issued in more recent years. Most confirmation patent claims were found at the top 100 new interactions obtained from each subnetwork. We have also found papers on the Web which confirm new inferred interactions. For instance, the best new interaction inferred by our model relates the interaction between the adrenaline neurotransmitter and the androgen receptor gene. We have found a paper that reports the partial dependence of the antiapoptotic effect of adrenaline on androgen receptor.ConclusionsThe VSM extended with a transitive closure approach provides a good way to identify biological interactions from textual collections. Specifically for the context of literature-based discovery, the extended VSM contributes to identify and rank relevant new interactions even if these interactions occcur in only a few documents in the collection. Consequently, we have developed an efficient method for extracting and restricting the best potential results to consider as new advances in life sciences, even when indications of these results are not easily observed from a mass of documents.

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Section: Discussionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Can the vector space model be used to identify biological entity activities?

et al. 2011

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“…These standard evaluation methodologies aid in system development, but the ultimate goal of LBD is to generate practical new knowledge, and a system's ability to do so is the best indicator of a system's success. While LBD researchers often claim new discoveries, many of these discoveries have failed to withstand empirical evaluation and expert assessment (Bekhuis, 2006;Kostoff, 2008a;Kostoff, 2008c;Kostoff et al, 2008a). Therefore, combining new discoveries with empirical evaluation is essential.…”

Section: Literature Based Discovery Evaluationmentioning

confidence: 99%

Using Literature Based Discovery to Gain Insights Into the Metabolomic Processes of Cardiac Arrest

Henry

Wijesinghe

Myers

et al. 2021

Front. Res. Metr. Anal.

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In this paper, we describe how we applied LBD techniques to discover lecithin cholesterol acyltransferase (LCAT) as a druggable target for cardiac arrest. We fully describe our process which includes the use of high-throughput metabolomic analysis to identify metabolites significantly related to cardiac arrest, and how we used LBD to gain insights into how these metabolites relate to cardiac arrest. These insights lead to our proposal (for the first time) of LCAT as a druggable target; the effects of which are supported by in vivo studies which were brought forth by this work. Metabolites are the end product of many biochemical pathways within the human body. Observed changes in metabolite levels are indicative of changes in these pathways, and provide valuable insights toward the cause, progression, and treatment of diseases. Following cardiac arrest, we observed changes in metabolite levels pre- and post-resuscitation. We used LBD to help discover diseases implicitly linked via these metabolites of interest. Results of LBD indicated a strong link between Fish Eye disease and cardiac arrest. Since fish eye disease is characterized by an LCAT deficiency, it began an investigation into the effects of LCAT and cardiac arrest survival. In the investigation, we found that decreased LCAT activity may increase cardiac arrest survival rates by increasing ω-3 polyunsaturated fatty acid availability in circulation. We verified the effects of ω-3 polyunsaturated fatty acids on increasing survival rate following cardiac arrest via in vivo with rat models.

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“…A contributing factor to the limited amount of industry-focussed literature is the lack of industry-provided training data due to what Kostoff (2005) calls “ incentives to conceal rather than reveal ”. Companies are unwilling to put their data in the public domain, wary of sharing data or research with potential competitors.…”

Section: Introductionmentioning

confidence: 99%

“…Companies are unwilling to put their data in the public domain, wary of sharing data or research with potential competitors. This is especially true in maintenance where data contain details of product and operational problems that organisations do not want to call attention to (Kostoff, 2005).…”

Section: Introductionmentioning

confidence: 99%

Text mining techniques for identifying failure modes

Malan

Jooste

2023

JQME

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PurposeThe purpose of this paper is to compare the effectiveness of the various text mining techniques that can be used to classify maintenance work-order records into their respective failure modes, focussing on the choice of algorithm and preprocessing transforms. Three algorithms are evaluated, namely Bernoulli Naïve Bayes, multinomial Naïve Bayes and support vector machines.Design/methodology/approachThe paper has both a theoretical and experimental component. In the literature review, the various algorithms and preprocessing techniques used in text classification is considered from three perspectives: the domain-specific maintenance literature, the broader short-form literature and the general text classification literature. The experimental component consists of a 5 × 2 nested cross-validation with an inner optimisation loop performed using a randomised search procedure.FindingsFrom the literature review, the aspects most affected by short document length are identified as the feature representation scheme, higher-order n-grams, document length normalisation, stemming, stop-word removal and algorithm selection. However, from the experimental analysis, the selection of preprocessing transforms seemed more dependent on the particular algorithm than on short document length. Multinomial Naïve Bayes performs marginally better than the other algorithms, but overall, the performances of the optimised models are comparable.Originality/valueThis work highlights the importance of model optimisation, including the selection of preprocessing transforms. Not only did the optimisation improve the performance of all the algorithms substantially, but it also affects model comparisons, with multinomial Naïve Bayes going from the worst to the best performing algorithm.

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Where is the Discovery in Literature-Based Discovery?

Cited by 5 publications

References 43 publications

Can the vector space model be used to identify biological entity activities?

Can the vector space model be used to identify biological entity activities?

Using Literature Based Discovery to Gain Insights Into the Metabolomic Processes of Cardiac Arrest

Text mining techniques for identifying failure modes

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