Ten simple rules for navigating the computational aspect of an interdisciplinary PhD

Islam, Sabrina; Wells, Christine A.

doi:10.1371/journal.pcbi.1008554

Cited by 2 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As mentioned in the introduction, a major challenge is getting researchers from multiple disciplines (engineerings, clinicians, etc.) to productively work together towards a common goal, despite often very different approaches and skillsets [ 6 , 7 ]. To facilitate these collaborations, educational and technical workshops aimed at both clinicians and computational modellers would be beneficial to better understand one another’s theory, approach, and terminology.…”

Section: Challengesmentioning

confidence: 99%

“…Neurorehabilitation exemplifies how multiple disciplines—physical therapy, biomedical engineering, medicine, neuroscience, kinesiology, and others—come together with the common purpose of improving the lives of those with a neurological condition [ 1 – 5 ]. Yet major challenges for multidisciplinary research are differences in theory, approach, and terminology between disciplines [ 6 , 7 ], all of which may hinder both collaboration and the development of improved neurorehabilitation. In this perspective piece, we propose a general, operational framework to promote synergistic collaboration among computational modellers, clinicians, and patients to improve functionality and quality of life.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

NSF DARE—transforming modeling in neurorehabilitation: a patient-in-the-loop framework

Cashaback,

Allen,

Chou

et al. 2024

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

In 2023, the National Science Foundation (NSF) and the National Institute of Health (NIH) brought together engineers, scientists, and clinicians by sponsoring a conference on computational modelling in neurorehabiilitation. To facilitate multidisciplinary collaborations and improve patient care, in this perspective piece we identify where and how computational modelling can support neurorehabilitation. To address the where, we developed a patient-in-the-loop framework that uses multiple and/or continual measurements to update diagnostic and treatment model parameters, treatment type, and treatment prescription, with the goal of maximizing clinically-relevant functional outcomes. This patient-in-the-loop framework has several key features: (i) it includes diagnostic and treatment models, (ii) it is clinically-grounded with the International Classification of Functioning, Disability and Health (ICF) and patient involvement, (iii) it uses multiple or continual data measurements over time, and (iv) it is applicable to a range of neurological and neurodevelopmental conditions. To address the how, we identify state-of-the-art and highlight promising avenues of future research across the realms of sensorimotor adaptation, neuroplasticity, musculoskeletal, and sensory & pain computational modelling. We also discuss both the importance of and how to perform model validation, as well as challenges to overcome when implementing computational models within a clinical setting. The patient-in-the-loop approach offers a unifying framework to guide multidisciplinary collaboration between computational and clinical stakeholders in the field of neurorehabilitation.

show abstract

Section: Challengesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

NSF DARE—transforming modeling in neurorehabilitation: a patient-in-the-loop framework

Cashaback,

Allen,

Chou

et al. 2024

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

show abstract

On the reproducibility of enzyme reactions and kinetic modelling

Gygli

2022

Biological Chemistry

View full text Add to dashboard Cite

Enzyme reactions are highly dependent on reaction conditions. To ensure reproducibility of enzyme reaction parameters, experiments need to be carefully designed and kinetic modeling meticulously executed. Furthermore, to enable quality control of enzyme reaction parameters, the experimental conditions, the modeling process as well as the raw data need to be reported comprehensively. By taking these steps, enzyme reaction parameters can be open and FAIR (findable, accessible, interoperable, re-usable) as well as repeatable, replicable and reproducible. This review discusses these requirements and provides a practical guide to designing initial rate experiments for the determination of enzyme reaction parameters and gives an open, FAIR and re-editable example of the kinetic modeling of an enzyme reaction. Both the guide and example are scripted with Python in Jupyter Notebooks and are publicly available (https://fairdomhub.org/investigations/483/snapshots/1). Finally, the prerequisites of automated data analysis and machine learning algorithms are briefly discussed to provide further motivation for the comprehensive, open and FAIR reporting of enzyme reaction parameters.

show abstract

Ten simple rules for navigating the computational aspect of an interdisciplinary PhD

Cited by 2 publications

References 24 publications

NSF DARE—transforming modeling in neurorehabilitation: a patient-in-the-loop framework

NSF DARE—transforming modeling in neurorehabilitation: a patient-in-the-loop framework

On the reproducibility of enzyme reactions and kinetic modelling

Contact Info

Product

Resources

About