Rediscovering Don Swanson:The Past, Present and Future of Literature-based Discovery

Smalheiser, Neil R.

doi:10.1515/jdis-2017-0019

Cited by 46 publications

(43 citation statements)

References 76 publications

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“…These new directions include storytelling methodologies (Sebastian, Siew & Orimaye, 2017b), analogy mining (Mower et al, 2016), outlier detection (Gubiani et al, 2017), gaps characterisation (Peng, Bonifield & Smalheiser, 2017), and negative consensus analysis (Smalheiser & Gomes, 2015). For a comprehensive discussion of contemporary discovery models and future directions, please refer (Smalheiser, 2017;Smalheiser, 2012).…”

Section: Discovery Modelsmentioning

confidence: 99%

A systematic review on literature-based discovery workflow

Thilakaratne

Falkner

Atapattu

2019

PeerJ Computer Science

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As scientific publication rates increase, knowledge acquisition and the research development process have become more complex and time-consuming. Literature-Based Discovery (LBD), supporting automated knowledge discovery, helps facilitate this process by eliciting novel knowledge by analysing existing scientific literature. This systematic review provides a comprehensive overview of the LBD workflow by answering nine research questions related to the major components of the LBD workflow (i.e., input, process, output, and evaluation). With regards to theinputcomponent, we discuss the data types and data sources used in the literature. Theprocesscomponent presents filtering techniques, ranking/thresholding techniques, domains, generalisability levels, and resources. Subsequently, theoutputcomponent focuses on the visualisation techniques used in LBD discipline. As for theevaluationcomponent, we outline the evaluation techniques, their generalisability, and the quantitative measures used to validate results. To conclude, we summarise the findings of the review for each component by highlighting the possible future research directions.

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Section: Discovery Modelsmentioning

confidence: 99%

A systematic review on literature-based discovery workflow

Thilakaratne

Falkner

Atapattu

2019

PeerJ Computer Science

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“…Swanson [17] proposed the ABC model that can be applied to new hypothesis generation for rehabilitation therapy repositioning, where ABC model is pertinent to an association rule between a separate set of publications: if A is associated with B, and B is associated with C, then there is a potential relation between A and C. ABC model has played a number of roles in the direction of drug discovery and repositioning. Earliest applications of the ABC model derived from two major findings of fish oil treatment for Raynaud’s disease and magnesium treatment for migraines, both of which have been clinically confirmed [30]; in recent years, vigorous development of bioinformatics mining and omics study indirectly borrowed the mode l [31]. However, one biggest limitation of the current ABC model-based approaches is that without certain domain knowledge, it is not easy to identify significative AB and BC directly.…”

Section: Discussionmentioning

confidence: 99%

Adopting Literature-based Discovery on Rehabilitation Therapy Repositioning for Stroke

Meng

Huang

et al. 2018

Preprint

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2 18 Abstract 19 Stroke is a common disabling disease severely affecting the daily life of the 20 patients. There is evidence that rehabilitation therapy can improve the movement 21 function. However, there are no clear guidelines that identify specific, effective 22 rehabilitation therapy schemes, and the development of new rehabilitation techniques 23 has been fairly slow. One informatics translational approach, called ABC model in 24 Literature-based Discovery, was used to mine an existing rehabilitation candidate 25 which is most likely to be repositioned for stroke. As in the classic ABC model 26 originated from Don Swanson, we built the internal links of stroke (A), assessment 27 scales (B), rehabilitation therapies (C) in PubMed relating to upper limb function 28 measurements for stroke patients. In the first step, with E-utility we retrieved both 29 stroke related assessment scales and rehabilitation therapies records, and complied 30 two datasets called Stroke_Scales and Stroke_Therapies, respectively. In the next 31 step, we crawled all rehabilitation therapies co-occurred with the Stroke_Theapies, 32 named as All_Therapies. Therapies that were already included in Stroke_Therapies 33 were deleted from All_Therapies, so that the remaining therapies were the potential 34 rehabilitation therapies, which could be repositioned for stroke after subsequent 35 filtration by manual check. We identified the top ranked repositioning rehabilitation 36 therapy following by subsequent clinical validation. Hand-arm bimanual intensive 37 training (HABIT) ranked the first in our repositioning rehabilitation therapies list, 38 with the most interaction links with Stroke_Scales. HABIT showed a significant 3 39 improvement in clinical scores on assessment scales of Fugl-Meyer Assessment and 40 Action Research Arm Test in the clinical validation on upper limb function for acute 41 stroke patients. Based on the ABC model and clinical validation of the results, we put 42 forward that HABIT as a promising rehabilitation therapy for stroke, which shows 43 that the ABC model is an effective text mining approach for rehabilitation therapy 44 repositioning. The results seem to be promoted in clinical knowledge discovery. 45 46 Keywords 47 Text mining; ABC model; Stroke; Hand-arm bimanual intensive training; Upper 48 extremity 49 50 Author Summary 51 In the present study, we proposed a text mining approach to mining terms related 52 to disease, rehabilitation therapy, and assessment scale from literature, with a 53 subsequent ABC inference analysis to identify relationships of these terms across 54 publications. The clinical validation demonstrated that our approach can be used to 55 identify potential repositioning rehabilitation therapy strategies for stroke. 56 Specifically, we identified a promising rehabilitation method called HABIT 57 previously used in pediatric congenital hemiplegia. A subsequent clinical trial 58 confirmed this as a highly promising rehabilitation therapy for stroke. 4 59 Introduction 60 Stroke and rehabil...

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“…Both datasets use Unified Medical Language System (UMLS) concept pairs instead of term pairs to represent relationships, but a mapping between terms and concepts exists (and can be found using a tool such as the UMLS-Interface [43]), so these datasets can be used regardless of whether a system uses concepts or terms. Both datasets are imperfect, but we relax the constraint that the silver standard datasets must contain all possible future discoveries, and instead evaluate based solely on the presence or absence of samples in each dataset, making them more easily assessed [6, 16, 17, 29].…”

Section: Methodsmentioning

confidence: 99%

“…Evaluation methods have been criticized as too narrow, as is the case with discovery replication [26], too noisy, as is the case with time slicing [8], not quantitative or replicable, as is the case with new discovery proposal [4], or are system specific and do not generalize [23, 27, 28]. Methods that are applicable across systems, quantifiable, and replicable are preferred, and since time slicing and link prediction type evaluation methods look at the presence or absence of links, rather than whether they are in-fact true and novel discoveries they are more easily assessed quantitatively [29].…”

Section: Introductionmentioning

confidence: 99%

Indirect association and ranking hypotheses for literature based discovery

Henry

McInnes

2019

BMC Bioinformatics

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Background Literature Based Discovery (LBD) produces more potential hypotheses than can be manually reviewed, making automatically ranking these hypotheses critical. In this paper, we introduce the indirect association measures of Linking Term Association (LTA), Minimum Weight Association (MWA), and Shared B to C Set Association (SBC), and compare them to Linking Set Association (LSA), concept embeddings vector cosine, Linking Term Count (LTC), and direct co-occurrence vector cosine. Our proposed indirect association measures extend traditional association measures to quantify indirect rather than direct associations while preserving valuable statistical properties. Results We perform a comparison between several different hypothesis ranking methods for LBD, and compare them against our proposed indirect association measures. We intrinsically evaluate each method’s performance using its ability to estimate semantic relatedness on standard evaluation datasets. We extrinsically evaluate each method’s ability to rank hypotheses in LBD using a time-slicing dataset based on co-occurrence information, and another time-slicing dataset based on SemRep extracted-relationships. Precision and recall curves are generated by ranking term pairs and applying a threshold at each rank. Conclusions Results differ depending on the evaluation methods and datasets, but it is unclear if this is a result of biases in the evaluation datasets or if one method is truly better than another. We conclude that LTC and SBC are the best suited methods for hypothesis ranking in LBD, but there is value in having a variety of methods to choose from.

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Rediscovering Don Swanson:The Past, Present and Future of Literature-based Discovery

Cited by 46 publications

References 76 publications

A systematic review on literature-based discovery workflow

A systematic review on literature-based discovery workflow

Adopting Literature-based Discovery on Rehabilitation Therapy Repositioning for Stroke

Indirect association and ranking hypotheses for literature based discovery

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