Genome wide CRISPR lethality screens show broad variability in cellular fitness phenotypes across cancer. We postulate that genes with overlapping functions should deliver similar responses enabling functional annotation of uncharacterized genes. Here we will build a network connecting genes based on the similarity of their knockout phenotypes, benchmark this network using protein interaction databases and functional transcriptomics, and leverage network analyses to identify mutational and transcriptional modulators of functional complexes.

Continue reading

Atherosclerosis, a chronic inflammatory disease of the artery wall, is the underlying cause of human coronary heart diseases. Single-cell genomics have catalyzed the revolution in understanding of cellular heterogeneity and dynamics in atherosclerotic vasculature. The goal of the project is to leverage published and our own single-cell genomic data and perform a meta-analysis. Meta-analysis allows integrated analysis of much larger cell numbers and helps resolve the full spectrum of cellular heterogeneity and dynamics in atherosclerotic vessels and facilitate therapeutic translation. The DSI scholar will: (1) use the latest bioinformatic pipeline to integrate the existing scRNA-seq, CITE-seq, and scATAC-seq datasets; (2) analyze the integrated datasets using R/Bioconductor packages (e.g. Seurat); (3) interpret the data using pathway and network analysis. Some relevant workflows are available through the “Resources” page of our lab website at https://hanruizhang.github.io/zhanglab/.

Continue reading

Future wireless networks will use high-frequency millimeter-wave (mmWave) links for transmitting and receiving information with high throughput. A key difference between mmWave links and conventional sub-6GHz links is that mmWave links are severely affected by weather conditions. Students working on this project will use a state-of-the-art mmWave radar to assess the impact of wind speed, temperature, humidity, and other factors on the high-frequency link. The end goal of the project is to develop a classifier that can infer weather conditions based on the signal received from the mmWave radar. In this project, students are expected to learn how the mmWave radar works, design experiments to obtain labeled data, perform measurements, and develop the classifier.

Continue reading

The proposed project would focus on analyzing quantitative data from a 4 year NIMH-funded study entitled “Integrating evidence-based depression treatment in primary care: Tuberculosis (TB) in Brazil as a model” (PI: Sweetland, K01MH104514). The aim of the study was to assess whether social network analysis could be used to leverage the receptivity and connectivity of TB providers in a Brazilian public health system in a way that could accelerate the adoption (implementation) and diffusion (dissemination) of an evidence-based treatment for depression treatment in a primary care network. Baseline receptivity was operationalized via six brief quantitative scales to measure mental health literacy, work self-efficacy, organizational climate, attitudes towards evidence-based practices, organizational readiness to change and individual innovation thresholds. Connectivity was assessed by asking TB providers with whom they discuss difficult cases, give advice to, or receive advice regarding difficult TB cases. Baseline receptivity and connectivity data was used to identify 3 pilot sites in which to train primary care providers to deliver evidence-based depression treatment for one year.

Continue reading

Alzheimer’s disease and related dementia (AD/dementia) represent a looming public health crisis, affecting roughly 5 million people in the U.S. and 11% of older adults. As with other chronic conditions, racial/ethnic and socio-economic disparities exist in the prevalence and burden of illness. However, less is known about how disparities in access to care influence the care trajectories – i.e., the scope, frequency and sequence of services used across healthcare settings – of those with AD/dementia.

Continue reading

Networked systems are ubiquitous in modern society. In a dynamic social or biological environment, the interactions among subjects can undergo large and systematic changes. Due to the rapid advancement of technology, a lot of social networks are observed with time information. Some examples include the email communication network between users, comments on Facebook, the retweet activities on Twitter, etc. We aim to propose new statistical models and associated methodologies for various problems including community detection, change point detection and behavior prediction. The proposed methods will be evaluated on a wide range of network datasets in different areas.

Continue reading