A systematic study on latent semantic analysis model parameters for mining biomedical literature

Yeasin, Mohammed; Malempati, Haritha; Homayouni, Ramin; Sorower, Mohammad S.

doi:10.1186/1471-2105-10-s7-a6

Cited by 3 publications

(5 citation statements)

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“…In order to develop an effective literature-mining framework to model disease-disease interaction networks, generate plausible new hypotheses, and support knowledge-discovery by finding semantically related entities, a Parameter Optimized LSA (POLSA) [ 14 ] was re-designed and adopted in the proposed HGF framework.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, the set of 96 associated factors represents a wide range of factors including generic factors such as depression and infection as well as specific factors such as vitamin E. As the final step, the set was further revised by an expert in the medical field. Using the improved POLSA technique [ 14 ], meaningful associations from the textual data in the PubMed database are extracted and mined. Furthermore, the factors are ranked based on their level of association to a given query.…”

Section: Resultsmentioning

confidence: 99%

“…The proposed hypothesis generation framework (HGF) is implemented based on parameter optimized latent semantic analysis (POLSA) [14] and is suitable to capture direct and indirect association among concepts. It is easy to note that the overall performance and quality of results obtained through LSA-based systems is a function of the dictionary used.…”

Section: Introductionmentioning

confidence: 99%

“…This paper designs and implements an efficient and scalable literature-mining framework to generate and also validate plausible hypotheses about various entities that include (but not limited to): risk factors, environmental factors, lifestyle, diseases, and disease groups. The proposed hypothesis generation framework (HGF) is implemented based on parameter optimized latent semantic analysis (POLSA) [ 14 ] and is suitable to capture direct and indirect association among concepts. It is easy to note that the overall performance and quality of results obtained through LSA-based systems is a function of the dictionary used.…”

Section: Introductionmentioning

confidence: 99%

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An automated framework for hypotheses generation using literature

et al. 2012

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BackgroundIn bio-medicine, exploratory studies and hypothesis generation often begin with researching existing literature to identify a set of factors and their association with diseases, phenotypes, or biological processes. Many scientists are overwhelmed by the sheer volume of literature on a disease when they plan to generate a new hypothesis or study a biological phenomenon. The situation is even worse for junior investigators who often find it difficult to formulate new hypotheses or, more importantly, corroborate if their hypothesis is consistent with existing literature. It is a daunting task to be abreast with so much being published and also remember all combinations of direct and indirect associations. Fortunately there is a growing trend of using literature mining and knowledge discovery tools in biomedical research. However, there is still a large gap between the huge amount of effort and resources invested in disease research and the little effort in harvesting the published knowledge. The proposed hypothesis generation framework (HGF) finds “crisp semantic associations” among entities of interest - that is a step towards bridging such gaps.MethodologyThe proposed HGF shares similar end goals like the SWAN but are more holistic in nature and was designed and implemented using scalable and efficient computational models of disease-disease interaction. The integration of mapping ontologies with latent semantic analysis is critical in capturing domain specific direct and indirect “crisp” associations, and making assertions about entities (such as disease X is associated with a set of factors Z).ResultsPilot studies were performed using two diseases. A comparative analysis of the computed “associations” and “assertions” with curated expert knowledge was performed to validate the results. It was observed that the HGF is able to capture “crisp” direct and indirect associations, and provide knowledge discovery on demand.ConclusionsThe proposed framework is fast, efficient, and robust in generating new hypotheses to identify factors associated with a disease. A full integrated Web service application is being developed for wide dissemination of the HGF. A large-scale study by the domain experts and associated researchers is underway to validate the associations and assertions computed by the HGF.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An automated framework for hypotheses generation using literature

et al. 2012

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“…The latter is critical for efficient mapping of domain knowledge to the semantic space with layer of genetic information. ARIANA adopts the POLSA [ 12 ] to capture direct as well as indirect statistical associations among the dictionary terms. In the POLSA model, term-frequency inverse-document-frequency (TF-IDF) matrix was used to generate the encoding matrix and the dimensionality was reduced to cover 95% of the total energy (dimensionality was reduced from 2,545 to 1,400 headings to create the encoding matrix).…”

Section: Methodsmentioning

confidence: 99%

Empirical study using network of semantically related associations in bridging the knowledge gap

2014

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BackgroundThe data overload has created a new set of challenges in finding meaningful and relevant information with minimal cognitive effort. However designing robust and scalable knowledge discovery systems remains a challenge. Recent innovations in the (biological) literature mining tools have opened new avenues to understand the confluence of various diseases, genes, risk factors as well as biological processes in bridging the gaps between the massive amounts of scientific data and harvesting useful knowledge.MethodsIn this paper, we highlight some of the findings using a text analytics tool, called ARIANA - Adaptive Robust and Integrative Analysis for finding Novel Associations.ResultsEmpirical study using ARIANA reveals knowledge discovery instances that illustrate the efficacy of such tool. For example, ARIANA can capture the connection between the drug hexamethonium and pulmonary inflammation and fibrosis that caused the tragic death of a healthy volunteer in a 2001 John Hopkins asthma study, even though the abstract of the study was not part of the semantic model.ConclusionAn integrated system, such as ARIANA, could assist the human expert in exploratory literature search by bringing forward hidden associations, promoting data reuse and knowledge discovery as well as stimulating interdisciplinary projects by connecting information across the disciplines.

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