Model Pruning Enables Efficient Federated Learning on Edge Devices.

Federated learning (FL) allows model training from local data collected by edge/mobile devices while preserving data privacy, which has wide applicability to image and vision applications. A challenge is that client devices in FL usually have much more limited computation and communication resources compared to servers in a data center. To overcome this challenge, we propose PruneFL -a novel FL approach with adaptive and distributed parameter pruning, which adapts the model size during FL to reduce both communication and computation overhead and minimize the overall training time, while maintaining a similar accuracy as the original model.

Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes.

Importance: Systems-level barriers to diabetes care could be improved with population health planning tools that accurately discriminate between high- and low-risk groups to guide investments and targeted interventions.

Objective: To develop and validate a population-level machine learning model for predicting type 2 diabetes 5 years before diabetes onset using administrative health data.

Design, Setting, And Participants: This decision analytical model study used linked administrative health data from the diverse, single-payer health system in Ontario, Canada, between January 1, 2006, and December 31, 2016.

Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data.

Across jurisdictions, government and health insurance providers hold a large amount of data from patient interactions with the healthcare system. We aimed to develop a machine learning-based model for predicting adverse outcomes due to diabetes complications using administrative health data from the single-payer health system in Ontario, Canada. A Gradient Boosting Decision Tree model was trained on data from 1,029,366 patients, validated on 272,864 patients, and tested on 265,406 patients.

Enhanced prediction of Src homology 2 (SH2) domain binding potentials using a fluorescence polarization-derived c-Met, c-Kit, ErbB, and androgen receptor interactome.

Many human diseases are associated with aberrant regulation of phosphoprotein signaling networks. Src homology 2 (SH2) domains represent the major class of protein domains in metazoans that interact with proteins phosphorylated on the amino acid residue tyrosine. Although current SH2 domain prediction algorithms perform well at predicting the sequences of phosphorylated peptides that are likely to result in the highest possible interaction affinity in the context of random peptide library screens, these algorithms do poorly at predicting the interaction potential of SH2 domains with physiologically derived protein sequences.

Comprehensive binary interaction mapping of SH2 domains via fluorescence polarization reveals novel functional diversification of ErbB receptors.

First-generation interaction maps of Src homology 2 (SH2) domains with receptor tyrosine kinase (RTK) phosphosites have previously been generated using protein microarray (PM) technologies. Here, we developed a large-scale fluorescence polarization (FP) methodology that was able to characterize interactions between SH2 domains and ErbB receptor phosphosites with higher fidelity and sensitivity than was previously achieved with PMs. We used the FP assay to query the interaction of synthetic phosphopeptides corresponding to 89 ErbB receptor intracellular tyrosine sites against 93 human SH2 domains and 2 phosphotyrosine binding (PTB) domains.

Targeted protein-omic methods are bridging the gap between proteomic and hypothesis-driven protein analysis approaches.

While proteomic methods have illuminated many areas of biological protein space, many fundamental questions remain with regard to systems-level relationships between mRNAs, proteins and cell behaviors. While mass spectrometric methods offer a panoramic picture of the relative expression and modification of large numbers of proteins, they are neither optimal for the analysis of predefined targets across large numbers of samples nor for assessing differences in proteins between individual cells or cell compartments. Conversely, traditional antibody-based methods are effective at sensitively analyzing small numbers of proteins across small numbers of conditions, and can be used to analyze relative differences in protein abundance and modification between cells and cell compartments.