A machine-learning framework for predicting multiple air pollutants' concentrations via multi-target regression and feature selection.

Air pollution is considered one of the biggest threats for the ecological system and human existence. Therefore, air quality monitoring has become a necessity in urban and industrial areas. Recently, the emergence of Machine Learning techniques justifies the application of statistical approaches for environmental modeling, especially in air quality forecasting. In this context, we propose a novel feature ranking method, termed as Ensemble of Regressor Chains-guided Feature Ranking (ERCFR) to forecast multiple air pollutants simultaneously over two cities. This approach is based on a combination of one of the most powerful ensemble methods for Multi-Target Regression problems (Ensemble of Regressor Chains) and the Random Forest permutation importance measure. Thus, feature selection allowed the model to obtain the best results with a restricted subset of features. The experimental results reveal the superiority of the proposed approach compared to other state-of-the-art methods, although some cautions have to be considered to improve the runtime performance and to decrease its sensitivity over extreme and outlier values.

A Genome-Wide Association Study Identifies a Novel Locus for Bortezomib-Induced Peripheral Neuropathy in European Patients with Multiple Myeloma.

PURPOSE: Painful peripheral neuropathy is a frequent toxicity associated with bortezomib therapy. This study aimed to identify loci that affect susceptibility to this toxicity. EXPERIMENTAL DESIGN: A genome-wide association study (GWAS) of 370,605 SNPs was performed to identify risk variants for developing severe bortezomib-induced peripheral neuropathy (BiPN) in 469 patients with multiple myeloma who received bortezomib-dexamethasone therapy prior to autologous stem cell in randomized clinical trials of the Intergroupe Francophone du Myelome (IFM) and findings were replicated in 114 patients with multiple myeloma of the HOVON-65/GMMG-HD4 clinical trial. RESULTS: An SNP in the PKNOX1 gene was associated with BiPN in the exploratory cohort [rs2839629; OR, 1.89, 95% confidence interval (CI), 1.45-2.44; P = 7.6 × 10(-6)] and in the replication cohort (OR, 2.04; 95% CI, = 1.11-3.33; P = 8.3 × 10(-3)). In addition, rs2839629 is in strong linkage disequilibrium (r(2) = 0.87) with rs915854, located in the intergenic region between PKNOX1 and cystathionine-ß-synthetase (CBS) Expression quantitative trait loci mapping showed that both rs2839629 and rs915854 genotypes have an impact on PKNOX1 expression in nerve tissue, whereas rs2839629 affects CBS expression in skin and blood. CONCLUSIONS: The use of GWAS in multiple myeloma pharmacogenomics has identified a novel candidate genetic locus mapping to PKNOX1 and in the immediate vicinity of CBS at 21q22.3 associated with the severe bortezomib-induced toxicity. The proximity of these two genes involved in neurologic pain whose tissue-specific expression is modified by the two variants provides new targets for neuroprotective strategies. Clin Cancer Res; 22(17); 4350-5. ©2016 AACR.