Statistics in Proteomics: A Meta-analysis of 100 Proteomics Papers Published in 2019 by David C. L. Handler (8791451)

“…This included questions such as whether a pilot study was conducted and whether false discovery rate calculation was employed at either the quantitation or identification stage. These data were then transformed to binary inputs, analyzed via machine learning algorithms, and classified accordingly, with the aim of determining if clusters of data existed for specific journals or if certain statistical measures correlated with each other. …”

Steps in the extraction of 14 coordinates from the CT slices for the curved MPR. by Linus Woitke (22783534)

Table_1_Identification of sources of DIF using covariates in patient-reported outcome measures: a simulation study comparing two approaches based on Rasch family models.DOCX by Yseulys Dubuy (16809567)

Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke by Chulho Kim (622686)

“…</p><p>Conclusions</p><p>Supervised ML based NLP algorithms are useful for automatic classification of brain MRI reports for identification of AIS patients. …”