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Policy information about studies involving animals; ARRIVE guidelines recommended for reporting animal research Laboratory animalsC57Bl/6 male mice, aged (19 months from NIA rodent colony), young (3 months from Charles River or Jackson Labs) Wild animalsThis study did not involve wild animals.Field-collected samples This study did not involve field-collected samples. Ethics oversightInstitutional Animal Care and Use Committee at Stanford University Note that full information on the approval of the study protocol must also be provided in the manuscript. Human research participantsPolicy information about studies involving human research participants Population characteristicsMale and female aged (58-93 years old) cognitively normal and clinically-diagnosed Alzheimer's disease patients. Cognitively normal patients do not display atypical vascular pathologies. Patients are mixed in APOE genotype. RecruitmentSubjects were not recruited specifically for this study. Samples are derived from a brain bank maintained by the Stanford/ VA/ NIA Aging Clinical Research Center (ACRC) from patients that provide consent for broad, de-identified data sharing under Institutional Review Board (IRB) approval. Ethics oversightStanford/ VA/ NIA Aging Clinical Research Center (ACRC) Note that full information on the approval of the study protocol must also be provided in the manuscript.

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.

Since the initial release of miRPathDB, tremendous progress has been made in the field of microRNA (miRNA) research. New miRNA reference databases have emerged, a vast amount of new miRNA candidates has been discovered and the number of experimentally validated target genes has increased considerably. Hence, the demand for a major upgrade of miRPathDB, including extended analysis functionality and intuitive visualizations of query results has emerged. Here, we present the novel release 2.0 of the miRNA Pathway Dictionary Database (miRPathDB) that is freely accessible at https://mpd.bioinf.uni-sb.de/. miRPathDB 2.0 comes with a ten-fold increase of pre-processed data. In total, the updated database provides putative associations between 27 452 (candidate) miRNAs, 28 352 targets and 16 833 pathways for Homo sapiens, as well as interactions of 1978 miRNAs, 24 898 targets and 6511 functional categories for Mus musculus. Additionally, we analyzed publications citing miRPathDB to identify common use-cases and further extensions. Based on this evaluation, we added new functionality for interactive visualizations and down-stream analyses of bulk queries. In summary, the updated version of miRPathDB, with its new custom-tailored features, is one of the most comprehensive and advanced resources for miRNAs and their target pathways.