Exploratory Analysis of the Gene Expression Matrix Based on Dual Conditional Dimensionality Reduction.

One of the major goals in gene expression data analysis is to explore and discover groups of genes and groups of biological conditions with meaningful relationships. While this problem can be addressed by algorithms, their results require an analysis within context, since they may be affected by many side processes -such as tissue differentiation- that could hinder the target goal. Visual analytics-based methods for exploratory analysis of the gene expression matrix (GEM) are essential in biomedical research since they allow us to frame the analysis within the user's knowledge domain. In this paper, we present a visual analytics approach to discover relevant connections between genes and samples based on linking a reordered GEM heatmap and dual 2D projections of its rows and columns, which can be recomputed conditioned by subsets of genes and/or samples selected by the user during the analysis. We demonstrate the capability of our approach to discover relevant knowledge in three case studies involving two cancer types plus normal tissue from the TCGA database.

Morphing projections: a new visual technique for fast and interactive large-scale analysis of biomedical datasets.

MOTIVATION: Biomedical research entails analyzing high dimensional records of biomedical features with hundreds or thousands of samples each. This often involves using also complementary clinical metadata, as well as a broad user domain knowledge. Common data analytics software makes use of machine learning algorithms or data visualization tools. However, they are frequently one-way analyses, providing little room for the user to reconfigure the steps in light of the observed results. In other cases, reconfigurations involve large latencies, requiring a retraining of algorithms or a large pipeline of actions. The complex and multiway nature of the problem, nonetheless, suggests that user interaction feedback is a key element to boost the cognitive process of analysis, and must be both broad and fluid. RESULTS: In this article, we present a technique for biomedical data analytics, based on blending meaningful views in an efficient manner, allowing to provide a natural smooth way to transition among different but complementary representations of data and knowledge. Our hypothesis is that the confluence of diverse complementary information from different domains on a highly interactive interface allows the user to discover relevant relationships or generate new hypotheses to be investigated by other means. We illustrate the potential of this approach with three case studies involving gene expression data and clinical metadata, as representative examples of high dimensional, multidomain, biomedical data. AVAILABILITY AND IMPLEMENTATION: Code and demo app to reproduce the results available at https://gitlab.com/idiazblanco/morphing-projections-demo-and-dataset-preparation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DCNN for condition monitoring and fault detection in rotating machines and its contribution to the understanding of machine nature.

Rotating machines are critical equipment in many processes, and failures in their operation can have serious implications. Consequently, fault detection in rotating machines has been widely investigated. Conventional detection systems include two blocks: feature extraction and classification. These systems are based on manually engineered features (ball pass frequencies, RMS value, kurtosis, crest factor, etc.) and therefore require a high level of human expertise (it is a human who designs and selects the most appropriate set of features to perform the classification). Instead, we propose a system for condition monitoring and fault detection in rotating machines based on a 1-D deep convolutional neural network (1D DCNN), which merges the tasks of feature extraction and classification into a single learning body. The proposed system has been designed for use on a rotating machine with seven possible operating states and it proves to be able to determine the operating condition of the machine almost as accurately as conventional feature-engineered classifiers, but without the need for prior knowledge of the machine. The proposed system has also reported good classification on a bearing fault dataset from another machine, thus demonstrating its capability to monitor the condition of different machines. Finally, the analysis of the features learned by the deep model has revealed valuable and previously unknown machine information, such as the rotational speed of the machine or the number of balls in the bearings. In this way, our results illustrate not only the good performance of CNNs, but also their versatility and the valuable information they could provide about the monitored machine.