Data-driven ocean, atmosphere, and land parameterizations calibrated from indirect data

The multiscale nature of climate necessarily requires approximations within all subcomponents, and may be based on machine learning, physics or both. Our approach to make accurate and trustworthy predictions of future, never-observed climate regimes combines physics-based models with subcomponents learned from accessible data, which are often only indirectly informative about the modeled processes. We have developed a suite of model-agnostic machine learning tools to learn about subcomponent models from such data. These tools rely on ensembles of model simulations, effectively carried out on GPUs in distributed systems; and our framework for assessing uncertainty of calibrations (CalibrateEmulateSample) requires 1,000 times fewer model evaluations than traditional approaches. Our approach has produced several scientific successes, such as a unified turbulence and cloud parameterization calibrated with a library of large-eddy simulations, a neural network snow model trained from station data, and calibration-directed development of a parameterization for upper-ocean turbulence (CATKE).</p

Additional file 13: of Stratification of candidate genes for Parkinson’s disease using weighted protein-protein interaction network analysis

List of PD network proteins responsible for the enrichment of the terms in Supplementary File 6. (XLSX 62 kb

Additional file 5: of Stratification of candidate genes for Parkinson’s disease using weighted protein-protein interaction network analysis

g:Pprofiler enrichment for the entire network. (XLSX 172 kb

Additional file 15: of Stratification of candidate genes for Parkinson’s disease using weighted protein-protein interaction network analysis

PD-GWAS gene prioritization. Significant SNPs from PD-GWAS and the number of ORFs in LD (from r2 > 0.5 to r2 > 0.8) are shown. Part A contains significant SNPs as per joint analysis, part B contains significant SNPs as per discovery phase. The candidate genes based on proximity are summarized as suggested in the original GWAS. Newly proposed candidate genes within each are identified on the basis of our analysis of the functionally relevant proteins in the PPI network. Genes previously selected by proximity and now also confirmed by functional analysis of the PD-network are in bold font. The top pie chart represents the distribution of proteins across the different relevant processes. In the final column the cell type with major expression (> 5% of average expression) is reported as calculated from the dataset generated by Zhang et al. [13] (A = mature astrocytes, N = neurons, M = microglia, O = oligodendrocytes and, E = endothelial cells). The bottom pie chart represents the distribution of proteins based on cell type expression in human temporal lobe cortex. (JPG 349 kb

Additional file 1: of Stratification of candidate genes for Parkinson’s disease using weighted protein-protein interaction network analysis

Supplementary Methods. (DOCX 64 kb

Additional file 2: of Stratification of candidate genes for Parkinson’s disease using weighted protein-protein interaction network analysis

Seeds and Parkinsonian syndromes. (JPG 266 kb

Additional file 18: of Stratification of candidate genes for Parkinson’s disease using weighted protein-protein interaction network analysis

GO Annotation Frequency. Proteins whose ORF is in the LD blocks around the prioritized SNPs in the PD-GWAS have been evaluate in terms of numbers of GO annotations present in GO for that specific ORF. In red are reported the proteins that correspond to genes that we prioritized with our analysis. In grey, all the other genes are reported. For some of the loci (A) the genes we prioritized was the gene with the maximum number of GO annotations for that locus; in some other cases (B) the genes we prioritized was NOT the gene with the maximum number of GO annotations for that locus. Finally, there are also mixed cases (C). (TIFF 315 kb

Additional file 3: of Stratification of candidate genes for Parkinson’s disease using weighted protein-protein interaction network analysis

Second layer network topological properties. (JPG 94 kb

Additional file 8: of Stratification of candidate genes for Parkinson’s disease using weighted protein-protein interaction network analysis

g:Pprofiler enrichment for the PD IIHs. (XLSX 25 kb

Additional file 17: of Stratification of candidate genes for Parkinson’s disease using weighted protein-protein interaction network analysis

Random Distribution. Distribution of the number of matches obtained in 100,000 simulated experiments in which we matched the relevant PD, process-specific, network proteins to randomly generated gene-sets of the same length as the list of ORFs in LD blocks with the top SNPS in the PD-GWAS. The distribution in blue is generated for random gene-sets of the same length as the list of ORFs in LD r2 ≥ 0.5; the distribution in red is generated for random gene-sets of the same length as the list of ORFs in LD r2 ≥ 0.8. (PDF 36 kb