Data science for the scientific life cycle

Ezer, Daphne; Whitaker, Kirstie

doi:10.7554/elife.43979

Cited by 13 publications

(11 citation statements)

References 35 publications

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“…Biomedical data science tools have allowed researchers to conduct studies that would have been impossible just ten years ago (Ezer and Whitaker, 2019). Phenotype mapping is one bottleneck that impedes translation of biomedical knowledge to improve human health.…”

Section: Discussionmentioning

confidence: 99%

WikiMedMap: Expanding the Phenotyping Mapping Toolbox Using Wikipedia

Sulieman

Denny

et al. 2019

Preprint

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Researchers utilizing phenotypic data from diverse sources require matching of phenotypes to standard clinical vocabularies. Mapping phenotypes to vocabulary can be difficult, as existing tools are often incomplete, can be difficult to access, and can be cumbersome to use, especially for non-experts. We created WikiMedMap as a simple tool that leverages Wikipedia and maps phenotype strings to standard clinical vocabularies. We assessed WikiMedMap by mapping phenotype strings from questionnaires in the UK Biobank and from Mendelian diseases in Online Mendelian Inheritance in Man (OMIM) database to eight vocabularies: International Classification of Diseases, Ninth Revision (ICD-9), ICD-10, ICD-O, Medical Subject Headings (MeSH), OMIM, Disease Database, and MedlinePlus. WikiMedMap outperformed conventional mapping tools in finding potential matches for phenotype strings. We envision WikiMedMap as a technique that complements existing and established tools to map strings to clinical vocabularies that usually do not coexist in one source. Word Count: 2650 words

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Section: Discussionmentioning

confidence: 99%

WikiMedMap: Expanding the Phenotyping Mapping Toolbox Using Wikipedia

Sulieman

Denny

et al. 2019

Preprint

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“…The Amazon dataset has a size of 365.82 MB. It contains a list of over 1,500 consumer reviews of Amazon products such as the Kindle, and Fire TV Stick as provided by Datafiniti's Product Database 4 . It has a total of 27 columns which includes basic product information such as rating, review text, and more for each product.…”

Section: Evaluation Of Contramentioning

confidence: 99%

“…Datax was used to analyse the Amazon product review dataset as provided by Datafiniti's Product Database. Figure 2 depicts the evaluation pane and shows a sneak peek into the first five rows of the investigated dataset while the right side 2 https://github.com/marioJoker/Datax 3 https://data.world/datafiniti/consumer-reviews-of-amazon-products 4 https://datafiniti.co/products/product-data/ displays the statistical analysis of the dataset. The statistical analysis displayed includes: the names of the investigated columns, the total number of missing values per column of the dataset, and the percentage of the missing data per column (Figure 3).…”

Section: Evaluation Of Dataxmentioning

confidence: 99%

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Visual Identification of Inconsistency in Pattern

Chika¹,

Emmanuel²,

Celestine³

et al. 2021

Applications of Pattern Recognition

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The visual identification of inconsistencies in patterns is an area in computing that has been understudied. While pattern visualisation exposes the relationships among identified regularities, it is still very important to identify inconsistencies (irregularities) in identified patterns. The significance of identifying inconsistencies for example in the growth pattern of children of a particular age will enhance early intervention such as dietary modifications for stunted children. It is described in this chapter, the need to have a system that identifies inconsistencies in identified pattern of a dataset. Also, techniques that enable the visual identification of inconsistencies in patterns such as fault tolerance and colour coding are described. Two approaches are presented in this chapter for visualising inconsistencies in patterns namely; visualising inconsistencies in objects with many attribute values and visual comparison of an investigated dataset with a case control dataset. These approaches are associated with tools which were developed by the authors of this chapter: Firstly, ConTra which allows its users to mine and analyse the contradictions in attribute values whose data does not abide by the mutual exclusion rule of the dataset. Secondly, Datax which mines missing data; enables the visualisation of the missingness and the identification of the associated patterns. Finally, WellGrowth which explores Children’s growth dataset by comparing an investigated dataset (data obtained from a Primary Health Centre) with a case control dataset (data from the website of World Health Organisation). Instances of inconsistencies as discovered in the explored datasets are discussed.

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“…The coming of Data Era makes it possible to reconcile the physical science and phenomenology philosophy at the data science level. (23)(24)(25). Data are acquired objectively, but are little meaningful without being subjectively interpreted or visualized appropriately.…”

Section: Introductionmentioning

confidence: 99%

Coloring Single Nanoparticle Trajectory in Live Cell with its Own History: a Presuppositionless Preprocessing Approach

Zhao

Huang

et al. 2019

Preprint

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Analyzing single particle trajectories is a prominent issue in understanding complex dynamics such as nanoparticle-cell interactions. Existing methods treat data points as isolated "atoms" and use predefined mechanical models to "frame" their complicated relationship. Herein, we propose a "historical evolution" based model-free strategy. It allows spatiotemporal heterogeneity embedded in a trajectory to self-emerge as consecutive colored segments before any model assumption, provide both an overall picture and local state transitions on the particle movement with minimum information loss, and inspire further model-based investigation. We demonstrate with simulations and experiments that the underlying mechanisms of various time-series and motion states of single nanoparticles on live cell membranes could all be revealed successfully. Since complexity studies at different levels of molecules, particles, cells, human beings, vehicles, and even stars could all be reduced to analyzing spatiotemporal trajectories of "single particles", this presuppositionless approach will help fundamental researches on many important systems. Impact Statement:A preprocessing strategy for single particle trajectory analysis is established by providing an intuitive global pattern from "historical experiences" of the particle without predefining any mechanical models.

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Data science for the scientific life cycle

Cited by 13 publications

References 35 publications

WikiMedMap: Expanding the Phenotyping Mapping Toolbox Using Wikipedia

WikiMedMap: Expanding the Phenotyping Mapping Toolbox Using Wikipedia

Visual Identification of Inconsistency in Pattern

Coloring Single Nanoparticle Trajectory in Live Cell with its Own History: a Presuppositionless Preprocessing Approach

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