Structured penalized regression for drug sensitivity prediction

Large-scale {\it in vitro} drug sensitivity screens are an important tool in personalized oncology to predict the effectiveness of potential cancer drugs. The prediction of the sensitivity of cancer cell lines to a panel of drugs is a multivariate regression problem with high-dimensional heterogeneous multi-omics data as input data and with potentially strong correlations between the outcome variables which represent the sensitivity to the different drugs. We propose a joint penalized regression approach with structured penalty terms which allow us to utilize the correlation structure between drugs with group-lasso-type penalties and at the same time address the heterogeneity between omics data sources by introducing data-source-specific penalty factors to penalize different data sources differently. By combining integrative penalty factors (IPF) with tree-guided group lasso, we create the IPF-tree-lasso method. We present a unified framework to transform more general IPF-type methods to the original penalized method. Because the structured penalty terms have multiple parameters, we demonstrate how the interval-search Efficient Parameter Selection via Global Optimization (EPSGO) algorithm can be used to optimize multiple penalty parameters efficiently. Simulation studies show that IPF-tree-lasso can improve the prediction performance compared to other lasso-type methods, in particular for heterogenous data sources. Finally, we employ the new methods to analyse data from the Genomics of Drug Sensitivity in Cancer project.Comment: Zhao Z, Zucknick M (2020). Structured penalized regression for drug sensitivity prediction. Journal of the Royal Statistical Society, Series C. 19 pages, 6 figures and 2 table

Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features

Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants

The prediction of electrical power produced in combined cycle power plants is a key challenge in the electrical power and energy systems field. This power production can vary depending on environmental variables, such as temperature, pressure, and humidity. Thus, the business problem is how to predict the power production as a function of these environmental conditions, in order to maximize the profit. The research community has solved this problem by applying Machine Learning techniques, and has managed to reduce the computational and time costs in comparison with the traditional thermodynamical analysis. Until now, this challenge has been tackled from a batch learning perspective, in which data is assumed to be at rest, and where models do not continuously integrate new information into already constructed models. We present an approach closer to the Big Data and Internet of Things paradigms, in which data are continuously arriving and where models learn incrementally, achieving significant enhancements in terms of data processing (time, memory and computational costs), and obtaining competitive performances. This work compares and examines the hourly electrical power prediction of several streaming regressors, and discusses about the best technique in terms of time processing and predictive performance to be applied on this streaming scenario.This work has been partially supported by the EU project iDev40. This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 783163. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Austria, Germany, Belgium, Italy, Spain, Romania. It has also been supported by the Basque Government (Spain) through the project VIRTUAL (KK-2018/00096), and by Ministerio de Economía y Competitividad of Spain (Grant Ref. TIN2017-85887-C2-2-P)