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In 2006, Narum published a paper in Conservation Genetics emphasizing that Bonferroni correction for multiple testing can be highly conservative with poor statistical power (high Type II error). He pointed out that other approaches for multiple testing correction can control the false discovery rate (FDR) with a better balance of Type I and Type II errors and suggested that the approach of Benjamini and Yekutieli (BY) 2001 provides the most biologically relevant correction for evaluating the significance of population differentiation in conservation genetics. However, there are crucial differences between the original Benjamini and Yekutieli procedure and that described by Narum. After carefully reviewing both papers, we found an error due to the incorrect implementation of the BY procedure in Narum (Conserv Genet 7:783–787, 2006) such that the approach does not adequately control FDR. Since the incorrect BY approach has been increasingly used, not only in conservation genetics, but also in medicine and biology, it is important that the error is made known to the scientific community. In addition, we provide an overview of FDR approaches for multiple testing correction and encourage authors first and foremost to provide effect sizes for their results; and second, to be transparent in their descriptions of multiple testing correction. Finally, the impact of this error on conservation genetics and other fields will be study-dependent, as it is related to the number of true to false positives for each study.

In this paper we analyse the performance of machine learning methods in predicting patient information such as age or sex solely from retinal imaging modalities in a heterogeneous clinical population. Our dataset consists of N = 135,667 fundus images and N = 85,536 volumetric OCT scans. Deep learning models were trained to predict the patient’s age and sex from fundus images, OCT cross sections and OCT volumes. For sex prediction, a ROC AUC of 0.80 was achieved for fundus images, 0.84 for OCT cross sections and 0.90 for OCT volumes. Age prediction mean absolute errors of 6.328 years for fundus, 5.625 years for OCT cross sections and 4.541 for OCT volumes were observed. We assess the performance of OCT scans containing different biomarkers and note a peak performance of AUC = 0.88 for OCT cross sections and 0.95 for volumes when there is no pathology on scans. Performance drops in case of drusen, fibrovascular pigment epitheliuum detachment and geographic atrophy present. We conclude that deep learning based methods are capable of classifying the patient’s sex and age from color fundus photography and OCT for a broad spectrum of patients irrespective of underlying disease or image quality. Non-random sex prediction using fundus images seems only possible if the eye fovea and optic disc are visible.

13 p.-6 fig.-5 tab., Alzheimer's disease is one of the most common neurodegenerative disorders in elder population. The β-site amyloid cleavage enzyme 1 (BACE1) is the major constituent of amyloid plaques and plays a central role in this brain pathogenesis, thus it constitutes an auspicious pharmacological target for its treatment. In this paper, a QSAR model for identification of potential inhibitors of BACE1 protein is designed by using classification methods. For building this model, a database with 215 molecules collected from different sources has been assembled. This dataset contains diverse compounds with different scaffolds and physical-chemical properties, covering a wide chemical space in the drug-like range. The most distinctive aspect of the applied QSAR strategy is the combination of hybridization with backward elimination of models, which contributes to improve the quality of the final QSAR model. Another relevant step is the visual analysis of the molecular descriptors that allows guaranteeing the absence of information redundancy in the model. The QSAR model performances have been assessed by traditional metrics, and the final proposed model has low cardinality, and reaches a high percentage of chemical compounds correctly classified., This work is kindly supported by CONICET, grant PIP 112–2012–0100471, by UNS, grants PGI 24/N042 and PGI 24/ZM17, Ministerio de Economía y Competitividad (CTQ2015-66313-R) and by MINCYT, for their economic support given to IP in the “6° Programa de Movilidad Docente a Madrid”.

The Support Vector Machine (SVM) has emerged in recent years as a popular approach to the classification of data. One problem that faces the user of an SVM is how to choose a kernel and the specific parameters for that kernel. Applications of an SVM therefore require a search for the optimum settings for a particular problem. This paper proposes a classification technique, which we call the Genetic Kernel SVM (GK SVM), that uses Genetic Programming to evolve a kernel for a SVM classifier. Results of initial experiments with the proposed technique are presented. These results are compared with those of a standard SVM classifier using the Polynomial, RBF and Sigmoid kernel with various parameter settings.