Proteomics is the interdisciplinary field focusing on the large-scale study of proteins. Proteins essentially organize and execute all functions within organisms. Today, the bottom-up analysis approach is the most commonly used workflow, where proteins are digested into peptides and subsequently analyzed using Tandem Mass Spectrometry (MS/MS). MS-based proteomics has transformed various fields in life sciences, such as drug discovery and biomarker identification. Today, proteomics is entering a phase where it is helpful for clinical decision-making. Computational methods are vital in turning large amounts of acquired raw MS data into information and, ultimately, knowledge. Deep learning has proved its success in multiple domains as a robust framework for supervised and unsupervised machine learning problems. In proteomics, scientists are increasingly leveraging the potential of deep learning to predict the properties of peptides based on their sequence to improve their confident identification. However, a reference dataset is missing, covering several proteomics tasks, enabling performance comparison, and evaluating reproducibility and generalization. Here, we present a large labeled proteomics dataset spanning several tasks in the domain to address this challenge. We focus on two common applications: peptide retention time and MS/MS spectrum prediction. We review existing methods and task formulations from a machine learning perspective and recommend suitable evaluation metrics and visualizations. With an accessible dataset, we aim to lower the entry barrier and enable faster development in machine learning for proteomics.

The identification of specific components in EEG signals is often key when designing EEG-based brain-computer interfaces (BCIs), and a good understanding of the factors that elicit such components can be helpful when it comes to precise, energy-efficient and time-accurate actuation of exoskeletons. CNVs (Contingent Negative Variations), ERDs or ERSs (Event-Related Desynchronizations/Synchronizations) as well as ErrPs (Error-Related Potentials) are particularly important components can be identified during motor tasks and related to specific events in a Coincident Timing (CT) task. This work investigates offline EEG signals acquired during an upper limb CT task and analyzes the task protocol with the purpose of correlating the aforementioned EEG features to movement onset. CNVs and ERD/ERS were successfully identified after averaging multiple trials, and it was further concluded that complementary information about muscle activity (via EMG) as well as video tracking of arm movement play a critical role in the synchronization of EEG components with movement onset. The framework for EEG analysis presented in this paper allows for future development of a BCI on top of this CT task capable of assessing motor learning and actuating an exoskeleton to enable faster motor rehabilitation.