Generating Explainable and Effective Data Descriptors Using Relational Learning: Application to Cancer Biology

The key to success in machine learning is the use of effective data representations. The success of deep neural networks (DNNs) is based on their ability to utilize multiple neural network layers, and big data, to learn how to convert simple input representations into richer internal representations that are effective for learning. However, these internal representations are sub-symbolic and difficult to explain. In many scientific problems explainable models are required, and the input data is semantically complex and unsuitable for DNNs. This is true in the fundamental problem of understanding the mechanism of cancer drugs, which requires complex background knowledge about the functions of genes/proteins, their cells, and the molecular structure of the drugs. This background knowledge cannot be compactly expressed propositionally, and requires at least the expressive power of Datalog. Here we demonstrate the use of relational learning to generate new data descriptors in such semantically complex background knowledge. These new descriptors are effective: adding them to standard propositional learning methods significantly improves prediction accuracy. They are also explainable, and add to our understanding of cancer. Our approach can readily be expanded to include other complex forms of background knowledge, and combines the generality of relational learning with the efficiency of standard propositional learning

Inhibition of SLPI ameliorates disease activity in experimental autoimmune encephalomyelitis

<p>Abstract</p> <p>Background</p> <p>The secretory leukocyte protease inhibitor (SLPI) exerts wide ranging effects on inflammatory pathways and is upregulated in EAE but the biological role of SLPI in EAE, an animal model of multiple sclerosis is unknown</p> <p>Methods</p> <p>To investigate the pathophysiological effects of SLPI within EAE, we induced SLPI-neutralizing antibodies in mice and rats to determine the clinical severity of the disease. In addition we studied the effects of SLPI on the anti-inflammatory cytokine TGF-β.</p> <p>Results</p> <p>The induction of SLPI neutralizing antibodies resulted in a milder disease course in mouse and rat EAE. SLPI neutralization was associated with increased serum levels of TGF-β and increased numbers of FoxP3+ CD4+ T cells in lymph nodes. <it>In vitro</it>, the addition of SLPI significantly decreased the number of functional FoxP3+ CD25^hiCD4+ regulatory T cells in cultures of naive human CD4+ T cells. Adding recombinant TGF-β to SLPI-treated human T cell cultures neutralized SLPI's inhibitory effect on regulatory T cell differentiation.</p> <p>Conclusion</p> <p>In EAE, SLPI exerts potent pro-inflammatory actions by modulation of T-cell activity and its neutralization may be beneficial for the disease.</p

Importance of basophil activation testing in insect venom allergy

<p>Abstract</p> <p>Background</p> <p>Venom immunotherapy (VIT) is the only effective treatment for prevention of serious allergic reactions to bee and wasp stings in sensitized individuals. However, there are still many questions and controversies regarding immunotherapy, like selection of the appropriate allergen, safety and long term efficacy.</p> <p>Methods</p> <p>Literature review was performed to address the role of basophil activation test (BAT) in diagnosis of venom allergy.</p> <p>Results</p> <p>In patients with positive skin tests or specific IgE to both honeybee and wasp venom, IgE inhibition test can identify sensitizing allergen only in around 15% and basophil activation test increases the identification rate to around one third of double positive patients. BAT is also diagnostic in majority of patients with systemic reactions after insect stings and no detectable IgE. High basophil sensitivity to allergen is associated with a risk of side effects during VIT. Persistence of high basophil sensitivity also predicts a treatment failure of VIT.</p> <p>Conclusion</p> <p>BAT is a useful tool for better selection of allergen for immunotherapy, for identification of patients prone to side effects and patients who might be treatment failures. However, long term studies are needed to evaluate the accuracy of the test.</p

Організаційно-економічне забезпечення розвитку електронної промисловості

Розкрито питання організаційно-економічного забезпечення електронної промисловості в рамках організаційно-економічного механізму розвитку електронної промисловості на інноваційній основі, який регламентує діяльність державних, галузевих і підприємницьких структур, що забезпечують розвиток електронної промисловості. Ключові слова: електронна промисловість, організаційне забезпечення розвитку електронної промисловості, організаційно-економічний механізм, інноваційний розвиток.  Раскрываются вопросы организационно-экономического обеспечения электронной промышленности в рамках организационно-экономического механизма развития электронной промышленности на инновационной основе, который регламентирует деятельность государственных, отраслевых и предпринимательских структур, обеспечивающих развитие электронной промышленности. Ключевые слова: электронная промышленность, организационное обеспечение развития электронной промышленности, организационно-экономический механизм, инновационное развитие.  The paper deals with the issues of organizational and economic support of electronic industry in the framework of the organizational and economic mechanism of the above industry development on the basis of innovation. It regulates the activities of the government, sectoral and business organizations, which provide the development of the electronic industry. The proposalsare as follows: to work out a State Program of Development of the Electronic Industry, andto create a sectoral information system, a cluster “development of the electronic industry”, holding the electronic industry, a sectoral technology transfer system, training educational and scientific centres for the engineering staff. It is shown that at a corporate level the development of electronic industry is promoted by establishment of production facilities with the use of well-known brands and foreign electronic productions, technologies transfer with consideration of supply channels, introduction of business market mechanisms, IPC standards, and production information systems PDM/PLM. A specific feature of these measures is that to develop the issues of financial and economic, technical and technological, innovation and market support of the electronic industry development the methods of grouping, generalization of economic indicators received from the enterprises of this industry, and economic mathematical modelling using a correlation regression and structural logical analysis have been used. The application of these methods suggests that the use of the organizational and economic support contributes to promising development of the electronic industry in Ukraine which consists in formation of the core of the electronic industry and its integration in the world electronic space in the future. Keywords: electronic industry, organizational support of electronic industry development, organizational and economic mechanism, innovation-based development

Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure

A comprehensive structural, biochemical and biological profiling of the human NUDIX hydrolase family

The NUDIX enzymes are involved in cellular metabolism and homeostasis, as well as mRNA processing. Although highly conserved throughout all organisms, their biological roles and biochemical redundancies remain largely unclear. To address this, we globally resolve their individual properties and inter-relationships. We purify 18 of the human NUDIX proteins and screen 52 substrates, providing a substrate redundancy map. Using crystal structures, we generate sequence alignment analyses revealing four major structural classes. To a certain extent, their substrate preference redundancies correlate with structural classes, thus linking structure and activity relationships. To elucidate interdependence among the NUDIX hydrolases, we pairwise deplete them generating an epistatic interaction map, evaluate cell cycle perturbations upon knockdown in normal and cancer cells, and analyse their protein and mRNA expression in normal and cancer tissues. Using a novel FUSION algorithm, we integrate all data creating a comprehensive NUDIX enzyme profile map, which will prove fundamental to understanding their biological functionality

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio