Unsupervised analysis of whole transcriptome data from human pluripotent stem cells cardiac differentiation.

The main objective of the present work was to highlight differences and similarities in gene expression patterns between different pluripotent stem cell cardiac differentiation protocols, using a workflow based on unsupervised machine learning algorithms to analyse the transcriptome of cells cultured as a 2D monolayer or as 3D aggregates. This unsupervised approach effectively allowed to portray the transcriptomic changes that occurred throughout the differentiation processes, with a visual representation of the entire transcriptome. The results allowed to corroborate previously reported data and also to unveil new gene expression patterns.

EpiBOX: An Automated Platform for Long-Term Biosignal Collection.

Biosignals represent a first-line source of information to understand the behavior and state of human biological systems, often used in machine learning problems. However, the development of healthcare-related algorithms that are both personalized and robust requires the collection of large volumes of data to capture representative instances of all possible states. While the rise of flexible biosignal acquisition solutions has enabled the expedition of data collection, they often require complicated frameworks or do not provide the customization required in some research contexts.

Positive and Negative Evidence Accumulation Clustering for Sensor Fusion: An Application to Heartbeat Clustering.

In this work, a new clustering algorithm especially geared towards merging data arising from multiple sensors is presented. The algorithm, called PN-EAC, is based on the ensemble clustering paradigm and it introduces the novel concept of negative evidence. PN-EAC combines both positive evidence, to gather information about the elements that should be grouped together in the final partition, and negative evidence, which has information about the elements that should not be grouped together.

Combining multiple clusterings using evidence accumulation.

We explore the idea of evidence accumulation (EAC) for combining the results of multiple clusterings. First, a clustering ensemble--a set of object partitions, is produced. Given a data set (n objects or patterns in d dimensions), different ways of producing data partitions are: 1) applying different clustering algorithms and 2) applying the same clustering algorithm with different values of parameters or initializations.