The reproducibility of biomedical research: Sleepers awake!

Bustin, Stephen A.

doi:10.1016/j.bdq.2015.01.002

Cited by 48 publications

(46 citation statements)

References 137 publications

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“…This is problematic, since when evaluated for correctness, citation statements are often inaccurate [20]. More seriously, citations are both retained and reused within the literature even after the work that they are citing has been retracted [21]. Thus, a key, original focus of this work is to focus on the assertions that summarize the primary findings of a given paper rather than seek to use any and all available language to use for machine reading tasks.…”

Section: Discussionmentioning

confidence: 99%

Extracting Evidence Fragments for Distant Supervision of Molecular Interactions

Burns

Dasigi

Hovy

2017

Preprint

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Abstract. We describe a methodology for automatically extracting 'evidence fragments' from a set of biomedical experimental research articles. These fragments provide the primary description of evidence that is presented in the papers' figures. They elucidate the goals, methods, results and interpretations of experiments that support the original scientific contributions the study being reported. Within this paper, we describe our methodology and showcase an example data set based on the European Bioinformatics Institute's INTACT database (http:www.ebi.ac. uk/intact/). Using figure codes as anchors, we linked evidence fragments to INTACT data records as an example of distant supervision so that we could use INTACT's preexisting, manually-curated structured interaction data to act as a gold standard for machine reading experiments. We report preliminary baseline event extraction measures from this collection based on a publicly available, machine reading system (REACH). We use semantic web standards for our data and provide open access to all source code.

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Section: Discussionmentioning

confidence: 99%

Extracting Evidence Fragments for Distant Supervision of Molecular Interactions

Burns

Dasigi

Hovy

2017

Preprint

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“…Conversely, ignoring the guidelines can yield data of lower reliability and robustness, thus wasting researcher time and resourceswhich is especially galling in light of contracting research budgets. The conclusion from other people, he says, citing figures published in The Lancet (2), is that approximately 85% of research funding is wasted.…”

Section: Adoption Issuesmentioning

confidence: 92%

“…In an editorial in the journal Biomolecular Detection and Quantification , a publication Bustin co-founded specifically to promote high-quality quantitative studies, he reported that of 10 articles selected at random with RT-qPCR data published by journals in the Nature Publishing Group in 2014, none reported such key MIQE details as RNA integrity, RNA purity, reverse transcriptase conditions, or PCR efficiency, and all used just a single unvalidated reference gene for transcript normalization, despite evidence suggesting the inaccuracy of that approach (2). …”

Section: Adoption Issuesmentioning

confidence: 98%

Guiding our PCR experiments

Perkel¹

2015

BioTechniques

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“…One of the major advantages of disease networks is the intuitive access to the underlying complex interactions between diseases and other diseases, genes or drugs. Thus, publishing not only the data but also means to explore and exploit the network is key to ensure reproducibility and extensibility of the study [90]. Early studies lacked this option, although access to their data allowed the construction of visualization tools a posteriori.…”

Section: Network Exposition Visualization and Interactionmentioning

confidence: 99%

Disease networks and their contribution to disease understanding and drug repurposing: Evolution of the concept, techniques and data sources

et al. 2018

Preprint

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Over a decade ago, a new discipline called network medicine emerged as an approach to understand human diseases from a network theory point-of-view. Disease networks proved to be an intuitive and powerful way to reveal hidden connections among apparently unconnected biomedical entities such as diseases, physiological processes, signaling pathways, and genes.One of the fields that has benefited most from this improvement is the identification of new opportunities for the use of old drugs, known as drug repurposing. The importance of drug repurposing lies in the high costs and the prolonged time from target selection to regulatory approval of traditional drug development. In this document we analyze the evolution of disease network concept during the last decade and apply a data science pipeline approach to evaluate their functional units. As a result of this analysis, we obtain a list of the most commonly used functional units and the challenges that remain to be solved. This information can be very valuable for the generation of new prediction models based on disease networks. Data Science Pipelineimprove the classification of diseases [2]. However, the use of these sources raised new problems such as their fragmentation, heterogeneity, availability and different conceptualization of their data [3,4].Recent developments in network theory provide a way to address this challenge by representing these complex relationships as a collection of linked nodes [5]. Complex networks theory is a statistical physics interpretation of the old graph theory, aimed at describing and understanding the structures created by the relationships between the elements of a complex system [6][7][8][9]. Those elements are represented by nodes, pairwise connected by links whenever a relationship is observed between the corresponding elements. The resulting structure can then be described by means of a plethora of topological metrics [10], or be used as a base for modelling the system. The application of this field to biological problems has been named "network biology", while its use in biomedical problems is known as "network medicine" [11]. Some applications of biological networks are protein-protein interaction networks, gene regulatory networks (DNA-protein interaction networks), metabolic networks, signaling networks, neuronal network or phylogenetic trees [12].Following this approach, disease networks express the relationship between diseases as nodes and edges in a graph in, where D represents the set of diseases (nodes) and W the set of their relationships (edges) based upon their similarity. The meaning of similarity varies depending on the data used to build the network, which may be biological (genes or common proteins) or phenotypic (comorbidity, similar symptoms) [13], among other approaches. As will be explained throughout this article, the concept of disease network is not limited to diseasedisease connections (homogeneous networks), but also to relations between the disease and other factors such as its symptoms, its associ...

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The reproducibility of biomedical research: Sleepers awake!

Cited by 48 publications

References 137 publications

Extracting Evidence Fragments for Distant Supervision of Molecular Interactions

Extracting Evidence Fragments for Distant Supervision of Molecular Interactions

Guiding our PCR experiments

Disease networks and their contribution to disease understanding and drug repurposing: Evolution of the concept, techniques and data sources

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