A frequently sought output from a shotgun proteomics experiment is a list of proteins that we believe to have been present in the analyzed sample before proteolytic digestion. The standard technique to control for errors in such lists is to enforce a preset threshold for the false discovery rate (FDR). Many consider protein‐level FDRs a difficult and vague concept, as the measurement entities, spectra, are manifestations of peptides and not proteins. Here, we argue that this confusion is unnecessary and provide a framework on how to think about protein‐level FDRs, starting from its basic principle: the null hypothesis. Specifically, we point out that two competing null hypotheses are used concurrently in today's protein inference methods, which has gone unnoticed by many. Using simulations of a shotgun proteomics experiment, we show how confusing one null hypothesis for the other can lead to serious discrepancies in the FDR. Furthermore, we demonstrate how the same simulations can be used to verify FDR estimates of protein inference methods. In particular, we show that, for a simple protein inference method, decoy models can be used to accurately estimate protein‐level FDRs for both competing null hypotheses.

With the growth of the movie industry, it is becoming increasingly important for the stakeholders to get an idea about the probable profit made by the movie in the box office. In fact, among movies produced between 2000 and 2010 in the United States, only 36% had box office revenues higher than their production budgets, which further highlights the importance of making the right investment decisions. To address this issue, different machine learning algorithms like Logistic Regression, Support Vector Machine (SVM) and Multi Layer Perceptron (MLP) are used in this study to predict the box office return of a movie based on the data available before the release of the movie. The models use 35 movie parameters from 3200 movies as inputs to predict the profit made by a movie and classify the success of a movie from "flop" to "blockbuster" based on the generated revenue. An analysis of different machine learning architectures is also presented in this research. Finally, a system is proposed that produces comparable results with existing researches in this field and it can predict the profit generated by a movie with a "one class away" accuracy of 85.31% without using any sales information.

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