Private and Efficient Meta-Learning with Low Rank and Sparse Decomposition

Meta-learning is critical for a variety of practical ML systems -- like personalized recommendations systems -- that are required to generalize to new tasks despite a small number of task-specific training points. Existing meta-learning techniques use two complementary approaches of either learning a low-dimensional representation of points for all tasks, or task-specific fine-tuning of a global model trained using all the tasks. In this work, we propose a novel meta-learning framework that combines both the techniques to enable handling of a large number of data-starved tasks. Our framework models network weights as a sum of low-rank and sparse matrices. This allows us to capture information from multiple domains together in the low-rank part while still allowing task specific personalization using the sparse part. We instantiate and study the framework in the linear setting, where the problem reduces to that of estimating the sum of a rank-$r$ and a $k$-column sparse matrix using a small number of linear measurements. We propose an alternating minimization method with hard thresholding -- AMHT-LRS -- to learn the low-rank and sparse part effectively and efficiently. For the realizable, Gaussian data setting, we show that AMHT-LRS indeed solves the problem efficiently with nearly optimal samples. We extend AMHT-LRS to ensure that it preserves privacy of each individual user in the dataset, while still ensuring strong generalization with nearly optimal number of samples. Finally, on multiple datasets, we demonstrate that the framework allows personalized models to obtain superior performance in the data-scarce regime.Comment: 97 pages, 3 figure

The next frontier: Fostering innovation by improving health data access and utilization

Beneath most lively policy debates sit dry-as-dust theoretical and methodological discussions. Current disputes over the EU Adaptive Pathways initiative and the proposed US 21st Century Cures Act may ultimately rest on addressing arcane issues of data curation, standardization, and utilization. Improved extraction of inform ation on the safety and effectiveness of drugs-in-use must parallel adjustments in evidence requirements at the time of licensing. To do otherwise may compromise safety and efficacy in the name of fostering innovation

Comparative proteomic analyses of avirulent, virulent and clinical strains of mycobacterium tuberculosis identify strain-specific patterns

Mycobacterium tuberculosis is an adaptable intracellular pathogen, existing in both dormant as well as active disease-causing states. Here, we report systematic proteomic analyses of four strains, H37Ra, H37Rv and clinical isolates BND and JAL, to determine the differences in protein expression patterns that contribute to their virulence and drug resistance. Resolution of lysates of the four strains by liquid chromatography, coupled to mass spectrometry analysis, identified a total of 2161 protein groups covering ∼54% of the predicted M. tuberculosis proteome. Label-free quantification analysis of the data revealed 257 differentially expressed protein groups. The differentially expressed protein groups could be classified into seven K-means cluster bins, which broadly delineated strain-specific variations. Analysis of the data for possible mechanisms responsible for drug resistance phenotype of JAL suggested that it could be due to a combination of overexpression of proteins implicated in drug resistance and the other factors. Expression pattern analyses of transcription factors and their downstream targets demonstrated substantial differential modulation in JAL, suggesting a complex regulatory mechanism. Results showed distinct variations in the protein expression patterns of Esx and mce1 operon proteins in JAL and BND strains, respectively. Abrogating higher levels of ESAT6, an important Esx protein known to be critical for virulence, in the JAL strain diminished its virulence, although it had marginal impact on the other strains. Taken together, this study reveals that strain-specific variations in protein expression patterns have a meaningful impact on the biology of the pathogen

Asiatic Acid Inhibits Pro-Angiogenic Effects of VEGF and Human Gliomas in Endothelial Cell Culture Models

Malignant gliomas are one of the most devastating and incurable tumors. Sustained excessive angiogenesis by glioma cells is the major reason for their uncontrolled growth and resistance toward conventional therapies resulting in high mortality. Therefore, targeting angiogenesis should be a logical strategy to prevent or control glioma cell growth. Earlier studies have shown that Asiatic Acid (AsA), a pentacyclic triterpenoid, is effective against glioma and other cancer cells; however, its efficacy against angiogenesis remains unknown. In the present study, we examined the anti-angiogenic efficacy of AsA using human umbilical vein endothelial cells (HUVEC) and human brain microvascular endothelial cells (HBMEC). Our results showed that AsA (5–20 µM) inhibits HUVEC growth and induces apoptotic cell death by activating caspases (3 and 9) and modulating the expression of apoptosis regulators Bad, survivin and pAkt-ser473. Further, AsA showed a dose-dependent inhibition of HUVEC migration, invasion and capillary tube formation, and disintegrated preformed capillary network. AsA also inhibited the VEGF-stimulated growth and capillary tube formation by HUVEC and HBMEC. Next, we analyzed the angiogenic potential of conditioned media collected from human glioma LN18 and U87-MG cells treated with either DMSO (control conditioned media, CCM) or AsA 20 µM (AsA20 conditioned media, AsA20CM). CCM from glioma cells significantly enhanced the capillary tube formation in both HUVEC and HBMEC, while capillary tube formation in both endothelial cell lines was greatly compromised in the presence of AsA20CM. Consistent with these results, VEGF expression was lesser in AsA20CM compared to CCM, and indeed AsA strongly inhibited VEGF level (both cellular and secreted) in glioma cells. AsA also showed dose-dependent anti-angiogenic efficacy in Matrigel plug assay, and inhibited the glioma cells potential to attract HUVEC/HBMEC. Overall, the present study clearly showed the strong anti-angiogenic potential of AsA and suggests its usefulness against malignant gliomas

Stoichiometric gene-to-reaction associations enhance model-driven analysis performance: Metabolic response to chronic exposure to Aldrin in prostate cancer

[Background] Genome-scale metabolic models (GSMM) integrating transcriptomics have been widely used to study cancer metabolism. This integration is achieved through logical rules that describe the association between genes, proteins, and reactions (GPRs). However, current gene-to-reaction formulation lacks the stoichiometry describing the transcript copies necessary to generate an active catalytic unit, which limits our understanding of how genes modulate metabolism. The present work introduces a new state-of-the-art GPR formulation that considers the stoichiometry of the transcripts (S-GPR). As case of concept, this novel gene-to-reaction formulation was applied to investigate the metabolic effects of the chronic exposure to Aldrin, an endocrine disruptor, on DU145 prostate cancer cells. To this aim we integrated the transcriptomic data from Aldrin-exposed and non-exposed DU145 cells through S-GPR or GPR into a human GSMM by applying different constraint-based-methods.[Results] Our study revealed a significant improvement of metabolite consumption/production predictions when S-GPRs are implemented. Furthermore, our computational analysis unveiled important alterations in carnitine shuttle and prostaglandine biosynthesis in Aldrin-exposed DU145 cells that is supported by bibliographic evidences of enhanced malignant phenotype.[Conclusions] The method developed in this work enables a more accurate integration of gene expression data into model-driven methods. Thus, the presented approach is conceptually new and paves the way for more in-depth studies of aberrant cancer metabolism and other diseases with strong metabolic component with important environmental and clinical implications.The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007–2013) / ERC Grant Agreement n. 320737 and from the Novo Nordisk Foundation (NNF10CC1016517 and NNF14OC0009473). MC is funded by ICREA Academia programme-2015 (Icrea Fundation), AGAUR-Generalitat de Catalunya (2017SGR-1033), MINECO European Commission FEDER funds (SAF2017-89673-R) and Instituto de Salud Carlos III (CIBEREHD CB17/04/00023).Peer reviewe