1. Smartphone-Based Recognition of Human Activities and Postural Transitions: Activity recognition data set built from the recordings of 30 subjects performing basic activities and postural transitions while carrying a waist-mounted smartphone with embedded inertial sensors.
2. p53 Mutants: The goal is to model mutant p53 transcriptional activity (active vs inactive) based on data extracted from biophysical simulations.
3. Myocardial infarction complications: Prediction of myocardial infarction complications
4. MicroMass: A dataset to explore machine learning approaches for the identification of microorganisms from mass-spectrometry data.
5. LSVT Voice Rehabilitation: 126 samples from 14 participants, 309 features. Aim: assess whether voice rehabilitation treatment lead to phonations considered 'acceptable' or 'unacceptable' (binary class classification problem).
6. Influenza outbreak event prediction via Twitter data: By identifying influenza-related tweets, the goal is to forecast the spatiotemporal patterns of influenza outbreaks for different locations and dates.
7. gene expression cancer RNA-Seq: This collection of data is part of the RNA-Seq (HiSeq) PANCAN data set, it is a random extraction of gene expressions of patients having different types of tumor: BRCA, KIRC, COAD, LUAD and PRAD.
8. Epileptic Seizure Recognition: This dataset is a pre-processed and re-structured/reshaped version of a very commonly used dataset featuring epileptic seizure detection.
9. Dorothea: DOROTHEA is a drug discovery dataset. Chemical compounds represented by structural molecular features must be classified as active (binding to thrombin) or inactive. This is one of 5 datasets of the NIPS 2003 feature selection challenge.
10. Breath Metabolomics: Breath analysis is a pivotal method for biological phenotyping. In a pilot study, 100 experiments with four subjects have been performed to study the reproducibility of this technique.
11. Arcene: ARCENE's task is to distinguish cancer versus normal patterns from mass-spectrometric data. This is a two-class classification problem with continuous input variables. This dataset is one of 5 datasets of the NIPS 2003 feature selection challenge.
12. Activity recognition using wearable physiological measurements: This dataset contains features from Electrocardiogram (ECG), Thoracic Electrical Bioimpedance (TEB) and the Electrodermal Activity (EDA) for activity recognition.