Believe it or not: how much can we rely on published data on potential drug targets?

By F. Prinz, T. Schlange, K. Asadullah. Nature reviews. Drug discovery, 2011

1,752 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/22a872a231374563cc2aa8b13a75846222a41c74

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Citations in the corpus

50 Years of Data Science (2017, background): “There seem to be significant flaws in the validity of the scientific literature (Ioannidis 2007; Sullivan 2007; Prinz, Schlange, and Asadullah 2011; Open Science Collaboration et al. 2015).”
Deep learning in clinical natural language processing: a methodical review (2019, background): “A renewed emphasis on scientific rigor has permeated the academic establishment, 108,109 with replicability and reproducibility studies (eg, from ACM’s definitions 110,111 ) showing how much of the literature has fallen short of its scientific goals.”
Ten Simple Rules for Reproducible Computational Research (2013, result): “…through studies showing that scientific papers commonly leave out experimental details essential for reproduction [5], studies showing difficulties with replicating published experimental results [6], an increase in retracted papers [7], and through a high number of failing clinical trials [8,9].”
An open investigation of the reproducibility of cancer biology research (2014, positive): “Reproducing prior results is challenging because of insufficient, incomplete, or inaccurate reporting of methodologies ( Hess, 2011 ; Prinz et al., 2011 ; Steward et al., 2012 ; Hackam and Redelmeier, 2006 ; Landis et al., 2011 ).”
State of the Art: Reproducibility in Artificial Intelligence (2018, result): “This is even the case for results published in the most prestigious journals; even the original researchers cannot reproduce their own results (Aarts et al. 2016; Begley and Ellis 2012; Begley and Ioannidis 2014; Prinz et al. 2011).”
Scientific Utopia: II. Restructuring Incentives and Practices to Promote Truth Over Publishability (2012, background): “False effects can remain for decades, slowly fading or continuing to inspire and influence new research (Prinz et al., 2011).”
How to Make More Published Research True (2014, background): “Pharmaceutical industry teams have championed reproducibility in preclinical research [56,57] because they depend on pre-clinical academic investigations accurately pinpointing useful drug targets.”
High-throughput discovery of novel developmental phenotypes (2016, neutral): “Moreover, recent challenges in reproducibility of animal model experimentation 5,6 emphasize the need for careful standardization of allele design, genetic background, and phenotyping protocols.”