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This paper introduces a software tool named KEEL which is a software tool to assess evolutionary algorithms for Data Mining problems of various kinds including as regression, classification, unsupervised learning, etc. It includes evolutionary learning algorithms based on different approaches: Pittsburgh, Michigan and IRL, as well as the integration of evolutionary learning techniques with different pre-processing techniques, allowing it to perform a complete analysis of any learning model in comparison to existing software tools. Moreover, KEEL has been designed with a double goal: research and educational.

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Early prediction of school dropout is a serious problem in education, but it is not an easy issue to resolve. On the one hand, there are many factors that can influence student retention. On the other hand, the traditional classification approach used to solve this problem normally has to be implemented at the end of the course to gather maximum information in order to achieve the highest accuracy. In this paper, we propose a methodology and a specific classification algorithm to discover comprehensible prediction models of student dropout as soon as possible. We used data gathered from 419 high schools students in Mexico. We carried out several experiments to predict dropout at different steps of the course, to select the best indicators of dropout and to compare our proposed algorithm versus some classical and imbalanced well‐known classification algorithms. Results show that our algorithm was capable of predicting student dropout within the first 4–6 weeks of the course and trustworthy enough to be used in an early warning system.

Multi-label learning is quite a recent supervised learning paradigm. Owing to its capabilities to improve performance in problems where a pattern may have more than one associated class, it has attracted the attention of researchers, producing an increasing number of publications. This study presents an up-to-date overview about multi-label learning with the aim of sorting and describing the main approaches developed till now. The formal definition of the paradigm, the analysis of its impact on the literature, its main applications, works developed, pitfalls and guidelines, and ongoing research are presented.

Background: Dysregulation of splicing variants (SVs) expression has recently emerged as a novel cancer hallmark. Although the generation of aberrant SVs (e.g. AR-v7/sst5TMD4/etc.) is associated to prostate-cancer (PCa) aggressiveness and/or castration-resistant PCa (CRPC) development, whether the molecular reason behind such phenomena might be linked to a dysregulation of the cellular machinery responsible for the splicing process [spliceosome-components (SCs) and splicing-factors (SFs)] has not been yet explored. Methods: Expression levels of 43 key SCs and SFs were measured in two cohorts of PCa-samples: 1) Clinicallylocalized formalin-fixed paraffin-embedded PCa-samples (n = 84), and 2) highly-aggressive freshly-obtained PCa-samples (n = 42). Findings: A profound dysregulation in the expression of multiple components of the splicing machinery (i.e. 7 SCs/19 SFs) were found in PCa compared to their non-tumor adjacent-regions. Notably, overexpression of SNRNP200, SRSF3 and SRRM1 (mRNA and/or protein) were associated with relevant clinical (e.g. Gleason score, T-Stage, metastasis, biochemical recurrence, etc.) and molecular (e.g. AR-v7 expression) parameters of aggressiveness in PCa-samples. Functional (cell-proliferation/migration) and mechanistic [gene-expression (qPCR) and protein-levels (western-blot)] assays were performed in normal prostate cells (PNT2) and PCa-cells (LNCaP/22Rv1/PC-3/ DU145 cell-lines) in response to SNRNP200, SRSF3 and/or SRRM1 silencing (using specific siRNAs) revealed an overall decrease in proliferation/migration-rate in PCa-cells through the modulation of key oncogenic SVs expression levels (e.g. AR-v7/PKM2/XBP1s) and alteration of oncogenic signaling pathways (e.g. p-AKT/p-JNK). Interpretation: These results demonstrate that the spliceosome is drastically altered in PCa wherein SNRNP200, SRSF3 and SRRM1 could represent attractive novel diagnostic/prognostic and therapeutic targets for PCa and CRPC.

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In this paper we describe JCLEC, a Java software system for the development of evolutionary computation applications. This system has been designed as a framework, applying design patterns to maximize its reusability and adaptability to new paradigms with a minimum of programming effort. JCLEC architecture comprises three main modules: the core contains all abstract type definitions and their implementation; experiments runner is a scripting environment to run algorithms in batch mode; finally, GenLab is a graphical user interface that allows users to configure an algorithm, to execute it interactively and to visualize the results obtained. The use of JCLEC system is illustrated though the analysis of one case study: the resolution of the 0/1 knapsack problem by means of evolutionary algorithms.