Balancing data privacy and usability in the federal statistical system.

The federal statistical system is experiencing competing pressures for change. On the one hand, for confidentiality reasons, much socially valuable data currently held by federal agencies is either not made available to researchers at all or only made available under onerous conditions. On the other hand, agencies which release public databases face new challenges in protecting the privacy of the subjects in those databases, which leads them to consider releasing fewer data or masking the data in ways that will reduce their accuracy. In this essay, we argue that the discussion has not given proper consideration to the reduced social benefits of data availability and their usability relative to the value of increased levels of privacy protection. A more balanced benefit-cost framework should be used to assess these trade-offs. We express concerns both with synthetic data methods for disclosure limitation, which will reduce the types of research that can be reliably conducted in unknown ways, and with differential privacy criteria that use what we argue is an inappropriate measure of disclosure risk. We recommend that the measure of disclosure risk used to assess all disclosure protection methods focus on what we believe is the risk that individuals should care about, that more study of the impact of differential privacy criteria and synthetic data methods on data usability for research be conducted before either is put into widespread use, and that more research be conducted on alternative methods of disclosure risk reduction that better balance benefits and costs.

Node sampling for protein complex estimation in bait-prey graphs.

In cellular biology, node-and-edge graph or "network" data collection often uses bait-prey technologies such as co-immunoprecipitation (CoIP). Bait-prey technologies assay relationships or "interactions" between protein pairs, with CoIP specifically measuring protein complex co-membership. Analyses of CoIP data frequently focus on estimating protein complex membership. Due to budgetary and other constraints, exhaustive assay of the entire network using CoIP is not always possible. We describe a stratified sampling scheme to select baits for CoIP experiments when protein complex estimation is the main goal. Expanding upon the classic framework in which nodes represent proteins and edges represent pairwise interactions, we define generalized nodes as sets of adjacent nodes with identical adjacency outside the set and use these as strata from which to select the next set of baits. Strata are redefined at each round of sampling to incorporate accumulating data. This scheme maintains user-specified quality thresholds for protein complex estimates and, relative to simple random sampling, leads to a marked increase in the number of correctly estimated complexes at each round of sampling. The R package seqSample contains all source code and is available at http://vault.northwestern.edu/~dms877/Rpacks/.

When do latent class models overstate accuracy for diagnostic and other classifiers in the absence of a gold standard?

Latent class models are increasingly used to assess the accuracy of medical diagnostic tests and other classifications when no gold standard is available and the true state is unknown. When the latent class is treated as the true class, the latent class models provide measures of components of accuracy including specificity and sensitivity and their complements, type I and type II error rates. The error rates according to the latent class model differ from the true error rates, however, and empirical comparisons with a gold standard suggest the true error rates often are larger. We investigate conditions under which the true type I and type II error rates are larger than those provided by the latent class models. Results from Uebersax (1988, Psychological Bulletin 104, 405-416) are extended to accommodate random effects and covariates affecting the responses. The results are important for interpreting the results of latent class analyses. An error decomposition is presented that incorporates an error component from invalidity of the latent class model.

Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing.

The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world in many ways, from cities under lockdown to new social experiences. Although in most cases COVID-19 results in mild illness, it has drawn global attention due to the extremely contagious nature of SARS-CoV-2. Governments and healthcare professionals, along with people and society as a whole, have taken any measures to break the chain of transition and flatten the epidemic curve. In this study, we used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research by identifying the latent topics and analyzing the temporal evolution of the extracted research themes, publications similarity, and sentiments, within the time-frame of January-May 2020. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues and the latter focusing more on intelligent systems/tools to predict/diagnose COVID-19. The special attention of the research community to the high-risk groups and people with complications was also confirmed.

Epidermal growth factor and trail interactions in epithelial-derived cells.

In healthy tissues, there is a balance between cell survival and death. This balance ensures epithelial cells survive in the right milieu, but undergo programmed cell death (apoptosis) when the environment is no longer supportive. Cells sense these changes primarily through receptors on the cell surface that bind to specific ligands present in the extracellular environment. These receptors, through signal transduction pathways, lead to promotion of cell survival or induction of cell death. One of the most important types of receptors regulating cell survival is the epidermal growth factor (EGF) receptors, while one of the most important types of receptors regulating apoptosis is the tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) death receptors. EGF receptors activate survival signaling pathways including PI3K/AKT, Ras/MAPK, and JAK/STAT signaling pathways leading to cell survival. TRAIL activates apoptotic signaling pathways leading to caspase activation and mitochondrial dysfunction. The balance between these two signaling pathways determine whether a cell survives or dies. In disease states, this balance is altered. For example, epithelial-derived cancer cells often have increased expression of EGF receptors and are resistant to apoptosis. Understanding the interactions between survival and apoptotic signaling pathways mediated by EGF receptors and TRAIL death receptors will be essential to explain the role these pathways play in healthy and diseased cells.