Opioidergic Signaling — A Neglected, Yet Potentially Important Player in Atopic Dermatitis

Atopic dermatitis (AD) is one of the most common skin diseases, the prevalence of which is especially high among children. Although our understanding about its pathogenesis has substantially grown in recent years, and hence, several novel therapeutic targets have been successfully exploited in the management of the disease, we still lack curative treatments for it. Thus, there is an unmet societal demand to identify further details of its pathogenesis to thereby pave the way for novel therapeutic approaches with favorable side effect profiles. It is commonly accepted that dysfunction of the complex cutaneous barrier plays a central role in the development of AD; therefore, the signaling pathways involved in the regulation of this quite complex process are likely to be involved in the pathogenesis of the disease and can provide novel, promising, yet unexplored therapeutic targets. Thus, in the current review, we aim to summarize the available potentially AD-relevant data regarding one such signaling pathway, namely cutaneous opioidergic signaling

Industry-Scale Orchestrated Federated Learning for Drug Discovery

To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n{\deg}831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups. The MELLODDY platform was the first industry-scale platform to enable the creation of a global federated model for drug discovery without sharing the confidential data sets of the individual partners. The federated model was trained on the platform by aggregating the gradients of all contributing partners in a cryptographic, secure way following each training iteration. The platform was deployed on an Amazon Web Services (AWS) multi-account architecture running Kubernetes clusters in private subnets. Organisationally, the roles of the different partners were codified as different rights and permissions on the platform and administrated in a decentralized way. The MELLODDY platform generated new scientific discoveries which are described in a companion paper.Comment: 9 pages, 4 figures, to appear in AAAI-23 ([IAAI-23 track] Deployed Highly Innovative Applications of AI

GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series.

status: publishe

Multi-aspect candidates for repositioning: data fusion methods using heterogeneous information sources.

Drug repositioning, an innovative therapeutic application of an old drug, has received much attention as a particularly costeffective strategy in drug R&D Recent work has indicated that repositioning can be promoted by utilizing a wide range of information sources, including medicinal chemical, target, mechanism, main and side-effect-related information, and also bibliometric and taxonomical fingerprints, signatures and knowledge bases. This article describes the adaptation of a conceptually novel, more efficient approach for the identification of new possible therapeutic applications of approved drugs and drug candidates, based on a kernel-based data fusion method. This strategy includes (1) the potentially multiple representation of information sources, (2) the automated weighting and statistically optimal combination of information sources, and (3) the automated weighting of parts of the query compounds. The performance was systematically evaluated by using Anatomical Therapeutic Chemical Classification System classes in a cross-validation framework. The results confirmed that kernel-based data fusion can integrate heterogeneous information sources significantly better than standard rank-based fusion can, and this method provides a unique solution for repositioning; it can also be utilized for de novo drug discovery. The advantages of kernel-based data fusion are illustrated with examples and open problems that are particularly relevant for pharmaceutical applications

Hunting Human Mendelian Disease-Associated Genes Using Matrix Completion with Side information

status: publishe

Protein Fold Recognition Using Matrix Factorization Technique

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A comprehensive comparison of two MEDLINE annotators for disease and gene linkage: sometimes less is more

© Springer International Publishing Switzerland 2016. Text mining is popular in biomedical applications because it allows retrieving highly relevant information. Particularly for us, it is quite practical in linking diseases to the genes involved in them. However text mining involves multiple challenges, such as (1) recognizing named entities (e.g., diseases and genes) inside the text, (2) constructing specific vocabularies that efficiently represent the available text, and (3) applying the correct statistical criteria to link biomedical entities with each other. We have previously developed Beegle, a tool that allows prioritizing genes for any search query of interest. The method starts with a search phase, where relevant genes are identified via the literature. Once known genes are identified, a second phase allows prioritizing novel candidate genes through a data fusion strategy. Many aspects of our method could be potentially improved. Here we evaluate two MEDLINE annotators that recognize biomedical entities inside a given abstract using different dictionaries and annotation strategies. We compare the contribution of each of the two annotators in associating genes with diseases under different vocabulary settings. Somewhat surprisingly, with fewer recognized entities and a more compact vocabulary, we obtain better associations between genes and diseases. We also propose a novel but simple association criterion to link genes with diseases, which relies on recognizing only gene entities inside the biomedical text. These refinements significantly improve the performance of our method.status: publishe