Publications

Positive-Unlabeled (PU) learning scenarios are a class of semi-supervised learning where only a fraction of the data is labeled, and all available labels are positive. The goal is to assign correct (positive and negative) labels to as much data as possible. Several important learning problems fall into the PU-learning domain, as in many cases the cost and feasibility of obtaining negative examples is prohibitive. In addition to the positive-negative disparity the overall cost of labeling these datasets typically leads to situations where the number of unlabeled examples greatly outnumbers the labeled. Accordingly, we perform several experiments, on both synthetic and real-world datasets, examining the performance of state of the art PU-learning algorithms when there is significant bias in the labeling process. We propose novel PU algorithms and demonstrate that they outperform the current state of the art on a variety of benchmarks. Lastly, we present a methodology for removing the costly parameter-tuning step in a popular PU algorithm.

RORγt+ Th17 cells are important for mucosal defenses but also contribute to autoimmune disease. They accumulate in the intestine in response to microbiota and produce IL-17 cytokines. Segmented filamentous bacteria (SFB) are Th17-inducing commensals that potentiate autoimmunity in mice. RORγt+ T cells were induced in mesenteric lymph nodes early after SFB colonization and distributed across different segments of the gastrointestinal tract. However, robust IL-17A production was restricted to the ileum, where SFB makes direct contact with the epithelium and induces serum amyloid A proteins 1 and 2 (SAA1/2), which promote local IL-17A expression in RORγt+ T cells. We identified an SFB-dependent role of type 3 innate lymphoid cells (ILC3), which secreted IL-22 that induced epithelial SAA production in a Stat3-dependent manner. This highlights the critical role of tissue microenvironment in activating effector functions of committed Th17 cells, which may have important implications for how these cells contribute to inflammatory disease.

The cohesin complex (consisting of Rad21, Smc1a, Smc3, and Stag2 proteins) is critically important for proper sister chromatid separation during mitosis. Mutations in the cohesin complex were recently identified in a variety of human malignancies including acute myeloid leukemia (AML). To address the potential tumor-suppressive function of cohesin in vivo, we generated a series of shRNA mouse models in which endogenous cohesin can be silenced inducibly. Notably, silencing of cohesin complex members did not have a deleterious effect on cell viability. Furthermore, knockdown of cohesin led to gain of replating capacity of mouse hematopoietic progenitor cells. However, cohesin silencing in vivo rapidly altered stem cells homeostasis and myelopoiesis. Likewise, we found widespread changes in chromatin accessibility and expression of genes involved in myelomonocytic maturation and differentiation. Finally, aged cohesin knockdown mice developed a clinical picture closely resembling myeloproliferative disorders/neoplasms (MPNs), including varying degrees of extramedullary hematopoiesis (myeloid metaplasia) and splenomegaly. Our results represent the first successful demonstration of a tumor suppressor function for the cohesin complex, while also confirming that cohesin mutations occur as an early event in leukemogenesis, facilitating the potential development of a myeloid malignancy.

Many important processes in the cell are mediated by stiff microtubule polymers and the active motor proteins moving on them. This includes the transport of subcellular structures (nuclei, chromosomes, organelles) and the self-assembly and positioning of the mitotic spindle. Little is understood of these processes, but they present fascinating problems in fluid-structure interactions. Microtubules and motor proteins are also the building blocks of new biosynthetic active suspensions driven by motor-protein activity. These reduced systems can be probed—and modeled—more easily than can the fully biological ones and demonstrate their own aspects of self-assembly and complex dynamics. I review recent work modeling such systems as fluid-structure interaction problems and as multiscale complex fluids.

Many functionally important interactions between genes and proteins involved in immunological diseases and processes are unknown. The exponential growth in public high-throughput data offers an opportunity to expand this knowledge. To unlock human-immunology-relevant insight contained in the global biomedical research effort, including all public high-throughput datasets, we performed immunological-pathway-focused Bayesian integration of a comprehensive, heterogeneous compendium comprising 38,088 genome-scale experiments. The distillation of this knowledge into immunological networks of functional relationships between molecular entities (ImmuNet), and tools to mine this resource, are accessible to the public at http://immunet.princeton.edu. The predictive capacity of ImmuNet, established by rigorous statistical validation, is easily accessed by experimentalists to generate data-driven hypotheses. We demonstrate the power of this approach through the identification of unique host-virus interaction responses, and we show how ImmuNet complements genetic studies by predicting disease-associated genes. ImmuNet should be widely beneficial for investigating the mechanisms of the human immune system and immunological diseases.

Identifying functional effects of noncoding variants is a major challenge in human genetics. To predict the noncoding-variant effects de novo from sequence, we developed a deep learning–based algorithmic framework, DeepSEA (http://deepsea.princeton.edu/), that directly learns a regulatory sequence code from large-scale chromatin-profiling data, enabling prediction of chromatin effects of sequence alterations with single-nucleotide sensitivity. We further used this capability to improve prioritization of functional variants including expression quantitative trait loci (eQTLs) and disease-associated variants.

We estimated ideological preferences of 3.8 million Twitter users and, using a data set of nearly 150 million tweets concerning 12 political and nonpolitical issues, explored whether online communication resembles an “echo chamber” (as a result of selective exposure and ideological segregation) or a “national conversation.” We observed that information was exchanged primarily among individuals with similar ideological preferences in the case of political issues (e.g., 2012 presidential election, 2013 government shutdown) but not many other current events (e.g., 2013 Boston Marathon bombing, 2014 Super Bowl). Discussion of the Newtown shootings in 2012 reflected a dynamic process, beginning as a national conversation before transforming into a polarized exchange. With respect to both political and nonpolitical issues, liberals were more likely than conservatives to engage in cross-ideological dissemination; this is an important asymmetry with respect to the structure of communication that is consistent with psychological theory and research bearing on ideological differences in epistemic, existential, and relational motivation. Overall, we conclude that previous work may have overestimated the degree of ideological segregation in social-media usage.

“Big Data” has surpassed “systems biology” and “omics” as the hottest buzzword in the biological sciences, but is there any substance behind the hype? Certainly, we have learned about various aspects of cell and molecular biology from the many individual high-throughput data sets that have been published in the past 15–20 years. These data, although useful as individual data sets, can provide much more knowledge when interrogated with Big Data approaches, such as applying integrative methods that leverage the heterogeneous data compendia in their entirety. Here we discuss the benefits and challenges of such Big Data approaches in biology and how cell and molecular biologists can best take advantage of them.

IMP (Integrative Multi-species Prediction), originally released in 2012, is an interactive web server that enables molecular biologists to interpret experimental results and to generate hypotheses in the context of a large cross-organism compendium of functional predictions and networks. The system provides biologists with a framework to analyze their candidate gene sets in the context of functional networks, expanding or refining their sets using functional relationships predicted from integrated high-throughput data. IMP 2.0 integrates updated prior knowledge and data collections from the last three years in the seven supported organisms (Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Danio rerio, Caenorhabditis elegans, and Saccharomyces cerevisiae) and extends function prediction coverage to include human disease. IMP identifies homologs with conserved functional roles for disease knowledge transfer, allowing biologists to analyze disease contexts and predictions across all organisms. Additionally, IMP 2.0 implements a new flexible platform for experts to generate custom hypotheses about biological processes or diseases, making sophisticated data-driven methods easily accessible to researchers. IMP does not require any registration or installation and is freely available for use at http://imp.princeton.edu.

The present invention relates to oxopiperazines that mimic helix αB of the C-terminal transactivation domain of HIF1α. Also disclosed are pharmaceutical compositions containing these oxopiperazines and methods of using these oxopiperazines (e.g., to reduce gene transcription, treat or prevent disorders mediated by interaction of HIF1a with CREB-binding protein and/or p300, reduce or prevent angiogenesis in a tissue, induce apoptosis, and decrease cell survival and/or proliferation).