Cancer Data Science and Computational Medicine

Yu, Peter Paul; Kibbe, Warren A.

doi:10.1200/cci.21.00006

Cited by 4 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The next evolution in oncology research and cancer care are being driven by data science (Yu and Kibbe, 2021). So, it is of paramount importance to address current accuracy and reproducibility issues.…”

Section: Discussionmentioning

confidence: 99%

NPARS—A Novel Approach to Address Accuracy and Reproducibility in Genomic Data Science

Peterson

Shin

et al. 2021

Front. Big Data

View full text Add to dashboard Cite

Background: Accuracy and reproducibility are vital in science and presents a significant challenge in the emerging discipline of data science, especially when the data are scientifically complex and massive in size. Further complicating matters, in the field of genomic-based science high-throughput sequencing technologies generate considerable amounts of data that needs to be stored, manipulated, and analyzed using a plethora of software tools. Researchers are rarely able to reproduce published genomic studies.Results: Presented is a novel approach which facilitates accuracy and reproducibility for large genomic research data sets. All data needed is loaded into a portable local database, which serves as an interface for well-known software frameworks. These include python-based Jupyter Notebooks and the use of RStudio projects and R markdown. All software is encapsulated using Docker containers and managed by Git, simplifying software configuration management.Conclusion: Accuracy and reproducibility in science is of a paramount importance. For the biomedical sciences, advances in high throughput technologies, molecular biology and quantitative methods are providing unprecedented insights into disease mechanisms. With these insights come the associated challenge of scientific data that is complex and massive in size. This makes collaboration, verification, validation, and reproducibility of findings difficult. To address these challenges the NGS post-pipeline accuracy and reproducibility system (NPARS) was developed. NPARS is a robust software infrastructure and methodology that can encapsulate data, code, and reporting for large genomic studies. This paper demonstrates the successful use of NPARS on large and complex genomic data sets across different computational platforms.

show abstract

Section: Discussionmentioning

confidence: 99%

NPARS—A Novel Approach to Address Accuracy and Reproducibility in Genomic Data Science

Peterson

Shin

et al. 2021

Front. Big Data

View full text Add to dashboard Cite

show abstract

“…Computational medicine has the potential to drive transformative advances in healthcare, extend health span, and reign in healthcare costs by i) enabling a more holistic understanding of the broad spectrum of all factors, processes, and their interplay impacting wellbeing at the individual and the population level, and by ii) translating such understanding into dynamically adaptive, personalized medical decisions to drive effective and sustainable health management practices. There are already several efforts demonstrating the potential of computational medicine across various diseases and conditions (e.g., (Louis et al, 2014;Mulder et al, 2018;Athanasiou et al, 2019;Bukowski et al, 2021;Yu and Kibbe, 2021;Hiram Guzzi et al, 2022;Toma et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Computational medicine: Grand challenges and opportunities for revolutionizing personalized healthcare

Tourassi

2023

Front. Med. Eng.

View full text Add to dashboard Cite

“…3 Recent development in cancer data science, particularly through the application of artificial intelligence and machine learning, greatly promotes the speed and scale of information analysis at an unprecedented rate. 4 An integrated system of data collection, storage, analysis, and modeling will build a critical foundation for computational medicine, with a sustained impact in cancer care and beyond. 5 In this issue of JAMA Oncology, Zeng et al 6 present an elegant phenome-wide association study (PheWAS) to discover new diseases associated with pathogenic variants of hereditary cancer genes.…”

mentioning

confidence: 99%

“…Methods to effectively analyze the ever-increasing amount of health data and systematically link genetic information from NGS with clinical data from EHRs is becoming an active area of ongoing research . Recent development in cancer data science, particularly through the application of artificial intelligence and machine learning, greatly promotes the speed and scale of information analysis at an unprecedented rate . An integrated system of data collection, storage, analysis, and modeling will build a critical foundation for computational medicine, with a sustained impact in cancer care and beyond …”

mentioning

confidence: 99%

A Phenome-Wide Association Study and the Discovery of a New Clinical Spectrum of Hereditary Cancer Genes

Yang

Scott

2022

JAMA Oncol

View full text Add to dashboard Cite

Cancer Data Science and Computational Medicine

Cited by 4 publications

References 15 publications

NPARS—A Novel Approach to Address Accuracy and Reproducibility in Genomic Data Science

NPARS—A Novel Approach to Address Accuracy and Reproducibility in Genomic Data Science

Computational medicine: Grand challenges and opportunities for revolutionizing personalized healthcare

A Phenome-Wide Association Study and the Discovery of a New Clinical Spectrum of Hereditary Cancer Genes

Contact Info

Product

Resources

About