2021/04/09 by Michael L. Blinov, John H. Gennari, Blinov, Michael L. +9
Decision Sciences · Biochemistry, Genetics and Molecular Biology · #Scientific Computing and Data Management #Bioinformatics and Genomic Networks #Cell Image Analysis Techniques
paper · pdf · doi:10.48550/arxiv.2104.04604
Although reproducibility is a core tenet of the scientific method, it remains\nchallenging to reproduce many results. Surprisingly, this also holds true for\ncomputational results in domains such as systems biology where there have been\nextensive standardization efforts. For example, Tiwari et al. recently found\nthat they could only repeat 50% of published simulation results in systems\nbiology. Toward improving the reproducibility of computational systems\nresearch, we identified several resources that investigators can leverage to\nmake their research more accessible, executable, and comprehensible by others.\nIn particular, we identified several domain standards and curation services, as\nwell as powerful approaches pioneered by the software engineering industry that\nwe believe many investigators could adopt. Together, we believe these\napproaches could substantially enhance the reproducibility of systems biology\nresearch. In turn, we believe enhanced reproducibility would accelerate the\ndevelopment of more sophisticated models that could inform precision medicine\nand synthetic biology.\n