scholarly journals NeuronUnit: A package for data-driven validation of neuron models using SciUnit

2019 ◽  
Author(s):  
Richard C. Gerkin ◽  
Justas Birgiolas ◽  
Russell J. Jarvis ◽  
Cyrus Omar ◽  
Sharon M. Crook

ABSTRACTValidating a quantitative scientific model requires comparing its predictions against many experimental observations, ideally from many labs, using transparent, robust, statistical comparisons. Unfortunately, in rapidly-growing fields like neuroscience, this is becoming increasingly untenable, even for the most conscientious scientists. Thus the merits and limitations of existing models, or whether a new model is an improvement on the state-of-the-art, is often unclear.Software engineers seeking to verify, validate and contribute to a complex software project rely on suites of simple executable tests, called “unit tests”. Drawing inspiration from this practice, we previously developed SciUnit, an easy-to-use framework for developing data-driven “model validation tests” – executable functions, here written in Python. Each such test generates and statistically validates predictions from a model against one relevant feature of empirical data to produce a score indicating agreement between the model and the data. Suites of such validation tests can be used to clearly identify the merits and limitations of existing models and developmental progress on new models.Here we describe NeuronUnit, a library that builds upon SciUnit and integrates with several existing neuroinformatics resources to support the validation of single-neuron models using data gathered by neurophysiologists and neuroanatomists. NeuronUnit integrates with existing technologies like Jupyter, Pandas, NeuroML and resources such as NeuroElectro, The Allen Institute, and The Human Brain Project in order to make neuron model validation as easy as possible for computational neuroscientists.

PEDIATRICS ◽  
2016 ◽  
Vol 137 (Supplement 3) ◽  
pp. 256A-256A
Author(s):  
Catherine Ross ◽  
Iliana Harrysson ◽  
Lynda Knight ◽  
Veena Goel ◽  
Sarah Poole ◽  
...  

2020 ◽  
Vol 16 (1) ◽  
pp. 639-647 ◽  
Author(s):  
Olugbenga Moses Anubi ◽  
Charalambos Konstantinou

2021 ◽  
pp. 263208432110100
Author(s):  
Satyendra Nath Chakrabartty

Background Scales for evaluating insomnia differ in number of items, response format, and result in different scores distributions and score ranges and may not facilitate meaningful comparisons. Objectives Transform ordinal item-scores of three scales of insomnia to continuous, equidistant, monotonic, normally distributed scores, avoiding limitations of summative scoring of Likert scales. Methods Equidistant item-scores by weighted sum using data-driven weights to different levels of different items, considering cell frequencies of Item-Levels matrix, followed by normalization and conversion to [1, 10]. Equivalent test-scores (as sum of transformed item- scores) for a pair of scales were found by Normal Probability curves. Empirical illustration given. Results Transformed test-scores are continuous, monotonic and followed Normal distribution with no outliers and tied scores. Such test-scores facilitate ranking, better classification and meaningful comparison of scales of different lengths and formats and finding equivalent score combinations of two scales. For a given value of transformed test-score of a scale, easy alternate method avoiding integration proposed to find equivalent scores of another scales. Equivalent scores of scales help to relate various cut-off scores of different scales and uniformity in interpretations. Integration of various scales of insomnia is achieved by finding one-to-one correspondence among the equivalent score of various scales with correlation over 0.99 Conclusion Resultant test-scores facilitated undertaking analysis in parametric set up. Considering the theoretical advantages including meaningfulness of operations, better comparison, use of such method of transforming scores of Likert items/test is recommended test and items, Future studies were suggested.


Author(s):  
Syeda Anmol Fatima ◽  
Nasser Ramli ◽  
Syed Ali Ammar Taqvi ◽  
Haslinda Zabiri
Keyword(s):  

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Brinnae Bent ◽  
Peter J. Cho ◽  
Maria Henriquez ◽  
April Wittmann ◽  
Connie Thacker ◽  
...  

AbstractPrediabetes affects one in three people and has a 10% annual conversion rate to type 2 diabetes without lifestyle or medical interventions. Management of glycemic health is essential to prevent progression to type 2 diabetes. However, there is currently no commercially-available and noninvasive method for monitoring glycemic health to aid in self-management of prediabetes. There is a critical need for innovative, practical strategies to improve monitoring and management of glycemic health. In this study, using a dataset of 25,000 simultaneous interstitial glucose and noninvasive wearable smartwatch measurements, we demonstrated the feasibility of using noninvasive and widely accessible methods, including smartwatches and food logs recorded over 10 days, to continuously detect personalized glucose deviations and to predict the exact interstitial glucose value in real time with up to 84% and 87% accuracy, respectively. We also establish methods for designing variables using data-driven and domain-driven methods from noninvasive wearables toward interstitial glucose prediction.


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