SIBILA: A novel interpretable ensemble of general-purpose machine learning models applied to medical contexts

Personalized medicine remains a major challenge for scientists. The rapid growth of Machine learning and Deep learning has made them a feasible al- ternative for predicting the most appropriate therapy for individual patients. However, the need to develop a custom model for every dataset, the lack of interpretation of their results and high computational requirements make many reluctant to use these methods. Aiming to save time and bring light to the way models work internally, SIBILA has been developed. SIBILA is an ensemble of machine learning and deep learning models that applies a range of interpretability algorithms to identify the most relevant input features. Since the interpretability algo- rithms may not be in line with each other, a consensus stage has been imple- mented to estimate the global attribution of each variable to the predictions. SIBILA is containerized to be run on any high-performance computing plat- form. Although conceived as a command-line tool, it is also available to all users free of charge as a web server at https://bio-hpc.ucam.edu/sibila. Thus, even users with few technological skills can take advantage of it. SIBILA has been applied to two medical case studies to show its ability to predict in classification problems. Even though it is a general-purpose tool, it has been developed with the aim of becoming a powerful decision-making tool for clinicians, but can actually be used in many other domains. Thus, other two non-medical examples are supplied as supplementary material to prove that SIBILA still works well with noise and in regression problems.Comment: 23 pages, 4 figures, 6 tables, 2 equation

Improvement of Virtual Screening Predictions using Computational Intelligence Methods

Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. However, the accuracy of most VS methods is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of scoring functions used in most VS methods we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, this information being exploited afterwards to improve VS predictions.We thank the Catholic University of Murcia (UCAM) under grant PMAFI/26/12. This work was partially supported by the computing facilities of Extremadura Research Centre for Advanced Technologies (CETA-CIEMAT), funded by the European Regional Development Fund (ERDF). CETA-CIEMAT belongs to CIEMAT and the Government of Spain

Improving drug discovery using hybrid softcomputing methods

Virtual screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface in order to find new hotspots, where ligands might potentially interact with, and which is implemented in last generation massively parallel GPU hardware, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods and concretely BINDSURF is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of the scoring functions used in BINDSURF we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, being this information exploited afterwards to improve BINDSURF VS predictions.We thank the Catholic University of Murcia (UCAM) under grant PMAFI/26/12. This work was partially supported by the computing facilities of Extremadura Research Centre for Advanced Technologies (CETA-CIEMAT), funded by the European Regional Development Fund (ERDF). CETA-CIEMAT belongs to CIEMAT and the Government of Spain

Parallel implementation of fuzzy minimals clustering algorithm

Clustering aims to classify different patterns into groups called clusters. Many algorithms for both hard and fuzzy clustering have been developed to deal with exploratory data analysis in many contexts such as image processing, pattern recognition, etc. However, we are witnessing the era of big data computing where computing resources are becoming the main bottleneck to deal with those large datasets. In this context, sequential algorithms need to be redesigned and even rethought to fully leverage the emergent massively parallel architectures. In this paper, we propose a parallel implementation of the fuzzy minimals clustering algorithm called Parallel Fuzzy Minimal (PFM). Our experimental results reveal linear speed-up of PFM when compared to the sequential counterpart version, keeping very good classification quality.Ingeniería, Industria y Construcció

L-type Ca2+ channels and SK channels in mouse embryonic stem cells and their contribution to cell proliferation

Mouse embryonic stem cells (mESCs) are capable of both self-renewal and multilineage differentiation; thus, they can be expanded in vivo or in vitro and differentiated to produce different cell types. Despite their biological and medical interest, many physiological properties of undifferentiated mESCs, such as ion channel function, are not fully understood. Ion channels are thought to be involved in cell proliferation and differentiation. The aim of this study was to characterize functional ion channels in cultured undifferentiated mESCs and their role in cell proliferation. L-type voltage-activated Ca2+ channels sensitive to nifedipine and small conductance Ca2+- activated K+ (SK) channels sensitive to apamin were identified. Ca2+-activated K+ currents were blocked by millimolar concentrations of tetraethylammonium (TEA). The effects of Ca2+ channel and Ca2+-activated K+ channel blockers on the proliferation of undifferentiated mESCs were investigated by bromodeoxyuridine (BrdU) incorporation. Dihydropyridine derivatives, such as nifedipine, inhibited cell growth and BrdU incorporation into the cells, whereas apamin, which selectively blocks SK channels, had no effect on cell growth. These results demonstrate that functional voltageoperated Ca2+ channels (VOCCs) and Ca2+-activated K+ channels are present in undifferentiated mESCs. Moreover, voltage-gated L-type Ca2+ channels, but not SK channels, might be necessary for proliferation of undifferentiated mESCs.Medicin

METADOCK: A parallel metaheuristic schema for virtual screening methods

Virtual screening through molecular docking can be translated into an optimization problem, which can be tackled with metaheuristic methods. The interaction between two chemical compounds (typically a protein, enzyme or receptor, and a small molecule, or ligand) is calculated by using highly computationally demanding scoring functions that are computed at several binding spots located throughout the protein surface. This paper introduces METADOCK, a novel molecular docking methodology based on parameterized and parallel metaheuristics and designed to leverage heterogeneous computers based on heterogeneous architectures. The application decides the optimization technique at running time by setting a configuration schema. Our proposed solution finds a good workload balance via dynamic assignment of jobs to heterogeneous resources which perform independent metaheuristic executions when computing different molecular interactions required by the scoring functions in use. A cooperative scheduling of jobs optimizes the quality of the solution and the overall performance of the simulation, so opening a new path for further developments of virtual screening methods on high-performance contemporary heterogeneous platforms.Ingeniería, Industria y Construcció

Drug solubility prediction with support vector machines on graphic processor units

En este trabajo se emplean métodos de inteligencia computacional, tales como las máquinas de soporte vectorial (MSV) para optimizar la predicción de la solubilidad de compuestos. Estas se entrenan con una base de datos de compuestos solubles e insolubles conocidos, y dicha información es posteriormente empleada para mejorar la predicción obtenida mediante cribado virtual. Los grandes avances en el campo de la computación de alto rendimiento ofrecen nuevas oportunidades en la simulación de sistemas biológicos y aplicaciones en bioinformática, biología computacional y química computacional. El uso de bases de datos de mayor tamaño aumenta las posibilidades en la generación de candidatos potenciales, pero el tiempo de cálculo necesario no sólo aumenta con el tamaño de la base de datos, sino también con la exactitud de los métodos de cribado virtual (CV) y del modelo. Se discuten los beneficios del uso de arquitecturas masivamente paralelas, en particular las unidades de procesamientos gráfico, demostrando empíricamente que están bien adaptadas para la aceleración de las MSV, obteniendo una aceleración de hasta 45 veces, en comparación con su versión secuencial.In this work we discuss the benefits of using computational intelligence methods, like Support Vector Machines (SVM) for the optimization of the prediction of compounds solubility. SVMs are trained with a database of known soluble and insoluble compounds, and this information is being exploited afterwards to improve Virtual Screening (VS) prediction. The landscape in the high performance computing arena opens up great opportunities in the simulation of relevant biological systems and for applications in bioinformatics, computational biology and computational chemistry. Larger databases increase the chances of generating hits or leads, but the computational time needed for the calculations increases not only with the size of the database but also with the accuracy of the VS methods and the model. We discussed the benefits of using massively parallel architectures, in particular graphics processing units. We empirically demonstrate that GPUs are well-suited architecture for the acceleration of SVM, obtaining up to 15 times sustained speedup compared to its sequential counterpart version.Este trabajo ha sido parcialmente financiado por los proyectos: NILS Mobility Project 012-ABEL-CM-2014A y Fundación Séneca 18946/JLI/13

In vitro production of porcine embryos with use of chemically semi-defined culture media system

The objective of this study was to determine the effect of a semi-defined culture media system developed in our laboratory, named Pigs Media System (PMS) on the in vitro production of porcine embryos. In a first assay, the cummulus-oocytes complexes (COCs) were matured, fertilized and cultured for embryo development in PMS supplemented with bovine serum albumin (BSA), and in North Carolina State University-23 (NCSU-23) supplemented with fluid follicular, until blastocysts evaluation. In the assay 2, maturation and culture were performed in PMS using BSA or polyvinyl alcohol (PVA) in a 2 × 2 factorial arrangement (PMS-BSA/BSA, PMS-BSA/PVA, PMS-PVA/PVA, PMS-PVA/BSA). The PMS had a positive effect on the total cell number (58.04) and the decrease of the total lipids (49.4%) regarding the NCSU-23 medium (37.98 and 59.2% respectively; p<0.05). The percentage of monospermic fertilization was significantly lower (42.3%; p<0.05) when oocytes were matured with PMS-BSA than in PMS-PVA (52.6%). The supplementation of BSA in the PMS for embryo culture, increased the blastocyst development, the cell number of blastocysts and decreased the content of total lipids (36.8%, 46.9 and 49.6% respectively; p<0.05), in comparison with the supplementation of PVA in the PMS for embryo culture. These results suggest that the semi-defined culture media system developed by the National Genetic Resources Center (CNRG), have proved favorable effects on the total cell number and the decrease of total lipids of porcine blastocysts in vitro produced. The objective of this study was to determine the effect of a semi-defined culture media system developed in our laboratory, named Pigs Media System (PMS) on the in vitro production of porcine embryos. In a first assay, the cummulus-oocytes complexes (COCs) were matured, fertilized and cultured for embryo development in PMS supplemented with bovine serum albumin (BSA), and in North Carolina State University-23 (NCSU-23) supplemented with fluid follicular, until blastocysts evaluation. In the assay 2, maturation and culture were performed in PMS using BSA or polyvinyl alcohol (PVA) in a 2 × 2 factorial arrangement (PMS-BSA/BSA, PMS-BSA/PVA, PMS-PVA/PVA, PMS-PVA/BSA). The PMS had a positive effect on the total cell number (58.04) and the decrease of the total lipids (49.4%) regarding the NCSU-23 medium (37.98 and 59.2% respectively; p<0.05). The percentage of monospermic fertilization was significantly lower (42.3%; p<0.05) when oocytes were matured with PMS-BSA than in PMS-PVA (52.6%). The supplementation of BSA in the PMS for embryo culture, increased the blastocyst development, the cell number of blastocysts and decreased the content of total lipids (36.8%, 46.9 and 49.6% respectively; p<0.05), in comparison with the supplementation of PVA in the PMS for embryo culture. These results suggest that the semi-defined culture media system developed by the National Genetic Resources Center (CNRG), have proved favorable effects on the total cell number and the decrease of total lipids of porcine blastocysts in vitro produced.