Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

[Abstract] Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure–Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron–Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/039Instituto de Salud Carlos III; PI13/0028

Parallel computing for brain simulation

[Abstract] Background: The human brain is the most complex system in the known universe, it is therefore one of the greatest mysteries. It provides human beings with extraordinary abilities. However, until now it has not been understood yet how and why most of these abilities are produced. Aims: For decades, researchers have been trying to make computers reproduce these abilities, focusing on both understanding the nervous system and, on processing data in a more efficient way than before. Their aim is to make computers process information similarly to the brain. Important technological developments and vast multidisciplinary projects have allowed creating the first simulation with a number of neurons similar to that of a human brain. Conclusion: This paper presents an up-to-date review about the main research projects that are trying to simulate and/or emulate the human brain. They employ different types of computational models using parallel computing: digital models, analog models and hybrid models. This review includes the current applications of these works, as well as future trends. It is focused on various works that look for advanced progress in Neuroscience and still others which seek new discoveries in Computer Science (neuromorphic hardware, machine learning techniques). Their most outstanding characteristics are summarized and the latest advances and future plans are presented. In addition, this review points out the importance of considering not only neurons: Computational models of the brain should also include glial cells, given the proven importance of astrocytes in information processing.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/039Instituto de Salud Carlos III; PI13/0028

Use of Multiple Astrocytic Configurations within an Artificial Neuro-Astrocytic Network

[Abstract] The artificial neural networks used in a multitude of fields are achieving good results. However, these systems are inspired in the vision of classical neuroscience where neurons are the only elements that process information in the brain. Advances in neuroscience have shown that there is a type of glial cell called astrocytes that collaborate with neurons to process information. In this work, a connectionist system formed by neurons and artificial astrocytes is presented. The astrocytes can have different configurations to achieve a biologically more realistic behaviour. This work indicates that the use of different artificial astrocytes behaviours is beneficial.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431D 2017/23Ministerio de Economía y Competitividad; UNLC08-1E-002Ministerio de Economía y Competitividad; UNLC13-13-350

Study of classical conditioning in Aplysia through the implementation of computational models of its learning circuit

“This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Experimental & Theoretical Artificial Intelligence on 04 Jul 2007, available online: http://wwww.tandfonline.com/DOI:10.1080/09528130601052177.”The learning phenomenon can be analysed at various levels, but in this paper we treat a specific paradigm of artificial intelligence, i.e. artificial neural networks (ANNs), whose main virtue is their capacity to seek unified and mutually satisfactory solutions which are relevant to biological and psychological models. Many of the procedures and methods proposed previously have used biological and/or psychological principles, models, and data; here, we focus on models which look for a greater degree of coherence. Therefore we analyse and compare all aspects of the Gluck–Thompson and Hawkins ANN models. A multithread computer model is developed for analysis of these models in order to study simple learning phenomena in a marine invertebrate (Aplysia californica) and to check their applicability to research in psychology and neurobiology. The predictive capacities of the models differs significantly: the Hawkins model provides a better analysis of the behavioural repertory of Aplysia on both the associative and the non-associative learning level. The scope of the ANN modelling technique is broadened by integration with neurobiological and behavioural models of associative learning, allowing enhancement of some architectures and procedures that are currently being used

First Multiplatform Application for Pharmacies in Spain, Which Guides the Prescription of Probiotics According to Pathology

[Abstract] The study of the intestinal microbiota is one of the biggest challenges in the current clinical environment. In this context, probiotics have been a focus of interest to achieve the stability of the intestinal microbiota, due to probiotics’ key role in its regulation. The development of an automated system that allows practitioners to easily search for the optimal probiotic is the main objective of this study. Although it is true that there have been previous attempts of applications with this purpose, only authorized probiotics available in the countries of origin, Canada and the USA, were included. This event was a limitation when looking for those endorsed in other countries such as Spain. Thus, a system has been developed from free and multiplatform technologies that allow its use without any cost, finding, in a simple way, those probiotics that would be ideal for each pathology, either from a browser or from a cell phone.This work was supported by the “Collaborative Project in Genomic Data Integration (CICLOGEN)” PI17/01826 funded by the Carlos III Health Institute from the Spanish National plan for Scientific and Technical Research and Innovation 2013–2016, the European Regional Development Funds (ERDF)—“A way to build Europe.”, the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. ED431D 2017/16), the “Galician Network for Colorectal Cancer Research” (Ref. ED431D 2017/23) and Competitive Reference Groups (Ref. ED431C 2018/49). The funding body did not have a role in the experimental design, data collection, analysis and interpretation, and writing of this manuscript. CITIC, as a Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidades” of Xunta de Galicia, 80% co-financed by the ERDF Funds, ERDF Operational Programme Galicia 2014-2020, and the remaining 20% by “Secretaría Xeral de Universidades” (Grant ED431G 2019/01)Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431C 2018/49Xunta de Galicia; ED431G 2019/0

Probiotic: First Prescriptive Application of Probiotics in Spain

[Abstract] The study of the intestinal microbiota is one of the greatest challenges in today’s clinical environment. Thus, probiotics have been established as a focus for its stability, as they play a key role in its regulation. The development of an automated technique that allows the practitioners the smooth search for the optimal probiotic is postulated as the main objective of this study. Despite the existence of previous attempts at applications for this purpose, they have only been carried out for the countries of origin, preventing them from being used in others such as Spain. Therefore, a system has been developed with open, multi-platform, and free technologies, which manages to locate the optimal probiotic for each pathology.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431D 2017/2

MIANN models in medicinal, physical and organic chemistry

[Abstract] Reducing costs in terms of time, animal sacrifice, and material resources with computational methods has become a promising goal in Medicinal, Biological, Physical and Organic Chemistry. There are many computational techniques that can be used in this sense. In any case, almost all these methods focus on few fundamental aspects including: type (1) methods to quantify the molecular structure, type (2) methods to link the structure with the biological activity, and others. In particular, MARCH-INSIDE (MI), acronym for Markov Chain Invariants for Networks Simulation and Design, is a well-known method for QSAR analysis useful in step (1). In addition, the bio-inspired Artificial-Intelligence (AI) algorithms called Artificial Neural Networks (ANNs) are among the most powerful type (2) methods. We can combine MI with ANNs in order to seek QSAR models, a strategy which is called herein MIANN (MI & ANN models). One of the first applications of the MIANN strategy was in the development of new QSAR models for drug discovery. MIANN strategy has been expanded to the QSAR study of proteins, protein-drug interactions, and protein-protein interaction networks. In this paper, we review for the first time many interesting aspects of the MIANN strategy including theoretical basis, implementation in web servers, and examples of applications in Medicinal and Biological chemistry. We also report new applications of the MIANN strategy in Medicinal chemistry and the first examples in Physical and Organic Chemistry, as well. In so doing, we developed new MIANN models for several self-assembly physicochemical properties of surfactants and large reaction networks in organic synthesis. In some of the new examples we also present experimental results which were not published up to date.Ministerio de Ciencia e Innovación; CTQ2009-07733Universidad del Pais Vasco; UFI11/22Universidad del Pais Vasco; GIU 094

Banco de La Concepción: A new Natura 2000 Marine Site off Canary Islands

The main objective of the LIFE+ INDEMARES project is to contribute to the protection and sustainable use of the biodiversity in the Spanish seas through the identification of valuable areas for the Natura 2000 Network. The Spanish Institute of Oceanography (IEO) has been in charge of implementing scientific surveys to map sensitive habitats of seven of the ten INDEMARES areas, and to determine the fisheries footprint over these areas. Banco de La Concepci´on is one of the areas chosen to be depicted in the frame of INDEMARES project. Located at 71 km to the NE of Lanzarote, at the coordinates 29º 55’ Latitude N and 12º 45’ Longitude W, Banco de la Concepci´on raises from 2,541 m up to its summit at 170 m deep. The biological richness of Banco de la Concepci´on is very influenced by the deep water up-welling phenomena, which create a high productivity, attracting a multitude of pelagic species, such as cetaceans, turtles, sharks, and tunas looking for food. In its vicinity, fishery resources such as goraz, anglerfish, and hakes, are abundant, and a rich invertebrate fauna cohabits in their bottoms. Banco de la Concepci´on is a traditional fishing area of oceanic pelagic species, and very good to catch demersal fish; it is highly visited by Galician and Portuguese drifters and long liners that fish in Mauritania, and mainly by the Andalusian longliners. In general, its main impacts are related to uncontrolled fishing pressure. The available information on the anthropogenic impact of the area was scarce, and its level of research was very poor as well, before INDEMARES project. Methodology approach complies with a multidisciplinary perspective, having described the area from geological, oceanographic, biological and fisheries points of view. Several surveys have taken place since 2009 to 2013 at Banco de La Concepci´on waters. Traps, longlines, beam trawls, benthic dredges and box corers have been used to sample benthic fauna. These last two, plus EM 3002 multibeam echosounder, PS 18 parametric sub bottom profiler, EA600 monobeam sounder, Seapath 200 positioning sensor and SV Plus sound velocity calibration sensor were used to make a geophysical study which provides a range of environmental factors. CTD was used to depict physical conditions of the water column. Finally, Remote Operated Vehicle Liropus 2000 and different photogrammetric tugged sleds were used to make a great effort of visual sampling. Data from VMS (Vessel Monitoring System) were used, combined with interviews to users (fishers), to describe the fishery uses in the area. Results from all this field work provide enough information for the administrations to establish a new Natura 2000 area, trying to reconcile protection of biodiversity and artisanal local economic activities. This establishment should take place at the end of a process of public consultation to stakeholders which is taking place in the present and which will help to shape the future Management Plan which will give details about permitted and prohibited uses

Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning

The theoretical prediction of drug-decorated nanoparticles (DDNPs) has become a very important task in medical applications. For the current paper, Perturbation Theory Machine Learning (PTML) models were built to predict the probability of different pairs of drugs and nanoparticles creating DDNP complexes with anti-glioblastoma activity. PTML models use the perturbations of molecular descriptors of drugs and nanoparticles as inputs in experimental conditions. The raw dataset was obtained by mixing the nanoparticle experimental data with drug assays from the ChEMBL database. Ten types of machine learning methods have been tested. Only 41 features have been selected for 855,129 drug-nanoparticle complexes. The best model was obtained with the Bagging classifier, an ensemble meta-estimator based on 20 decision trees, with an area under the receiver operating characteristic curve (AUROC) of 0.96, and an accuracy of 87% (test subset). This model could be useful for the virtual screening of nanoparticle-drug complexes in glioblastoma. All the calculations can be reproduced with the datasets and python scripts, which are freely available as a GitHub repository from authors. View Full-TextThe APC was funded by IKERDATA, S.L. under grant 3/12/DP/2021/00102—Area 1: Development of innovative business projects, from Provincial Council of Vizcaya (BEAZ for the Creation of Innovative Business Innovative business ventures)