Graph Representation Learning in Biomedicine

Biomedical networks are universal descriptors of systems of interacting elements, from protein interactions to disease networks, all the way to healthcare systems and scientific knowledge. With the remarkable success of representation learning in providing powerful predictions and insights, we have witnessed a rapid expansion of representation learning techniques into modeling, analyzing, and learning with such networks. In this review, we put forward an observation that long-standing principles of networks in biology and medicine -- while often unspoken in machine learning research -- can provide the conceptual grounding for representation learning, explain its current successes and limitations, and inform future advances. We synthesize a spectrum of algorithmic approaches that, at their core, leverage graph topology to embed networks into compact vector spaces, and capture the breadth of ways in which representation learning is proving useful. Areas of profound impact include identifying variants underlying complex traits, disentangling behaviors of single cells and their effects on health, assisting in diagnosis and treatment of patients, and developing safe and effective medicines

Knowledge Graph Completion to Predict Polypharmacy Side Effects

The polypharmacy side effect prediction problem considers cases in which two drugs taken individually do not result in a particular side effect; however, when the two drugs are taken in combination, the side effect manifests. In this work, we demonstrate that multi-relational knowledge graph completion achieves state-of-the-art results on the polypharmacy side effect prediction problem. Empirical results show that our approach is particularly effective when the protein targets of the drugs are well-characterized. In contrast to prior work, our approach provides more interpretable predictions and hypotheses for wet lab validation.Comment: 13th International Conference on Data Integration in the Life Sciences (DILS2018

Graph AI in Medicine

In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks (GNNs), stands out for its capability to capture intricate relationships within structured clinical datasets. With diverse data -- from patient records to imaging -- GNNs process data holistically by viewing modalities as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters or minimal re-training. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on graph relationships, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph models integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions

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

The Gas Phase Photoemission Beamline at Elettra

This paper reports the present stage of commissioning of the gas-phase photoemission beamline at Elettra, Trieste. The beamline is designed for atomic and molecular science experiments with high-resolution and high-flux synchrotron radiation. It consists of an undulator source, variable-angle spherical-grating monochromator and two experimental stations. The design value of the energy range is 20 to 800 eV with a specified resolving power of over 10000. The procedure adopted for calibration of this type of monochromator is discussed. At present a resolving power up to 20000 and a range up to 900 eV have been measured. Absorption spectra taken at the argon L II,III-edge and at the nitrogen, oxygen and neon K-edges are as sharp as, or sharper than, any reported in the literature. The instrumental broadening is well below the natural line-width making it difficult to quantify the resolution; this problem is discussed

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

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