The function for much of the 3 billion letters in the human genome remain to be understood. Advances in DNA sequencing technology have generated enormous amount of data, yet we don’t have the tool to extract rules of how the genome works. Deep learning holds great potential in decoding the genome, in particular due to the digital nature of DNA sequences and the ability to handle large data sets. However, like many other applications, the interpretability of deep learning models hampers its ability to help understand the genome. We are developing deep learning architectures embedded with the principles of gene regulation and we will be leveraging billions of existing measurements of gene activity to learn a mechanistic model of gene regulation in human cells.

Continue reading

This project will be focused on creating a deep learning framework for tracking individual molecules and proteins as they move within a cell under various conditions. Using total internal reflection (TIRF) microscopy, we have accumulated more than 10 million trajectories over dozens of experimental preparations with differences in both the imaging approaches as well as the biological context. In our experiments we have captured particles under a wide variety of conditions including increased protein expression level, and a range of drug concentrations. Our biggest challenge is being able to stably track the movement of a particle as it passes by other particles or groups of particles, and to do this in a way that generalizes over novel conditions. The Data Science Institute Scholar chosen for this project would work with scientists in the Javitch laboratory and others across the Columbia campus to conceive of an approach for efficiently and effectively tracking particles. The resulting work would be of great interest to an increasing number of scientists working in this field who currently rely on methods based on feature engineering that are often inaccurate or inflexible compared to modern deep learning methods.

Continue reading

All complex behaviors require animals to coordinate their perception and actions. To successfully achieve a goal, a decision maker (DM; be it a human, animal, or artificial agent) must determine which action to take and, faced with much more information than she can fully process, must decide which source of information to consult to best guide that action. But in contrast with natural tasks, traditional research has focused primarily on action selection but eschewed the process of information demand. We aim to fill this gap by investigating the factors that motivate people to become curious and seek information. We are collecting behavioral data from a large sample of participants on a battery of online tasks testing various aspects of curiosity, and seek a DSI scholar who can quantitatively analyze the data. The scholar will be supervised by two co-PIs: Jacqueline Gottlieb, in Columbia’s Neuroscience Department and Zuckerman Institute, and Vince Dorie, in the DSI. The scholar will obtain training with advanced data analytic methods and the opportunity to co-author what is expected to be a high impact paper with interdisciplinary appeal in economics, neuroscience, and psychology.

Continue reading

We have been studying bladder cancer in a mouse model of the disease and we are seeking to understand the molecular features of the mouse models as they relate to human bladder cancer.

Continue reading

Big data with temporal dependence brings unique challenges in effective prediction and data analysis. The complex high-dimensional interactions between observations in such data brings unique challenges which standard off-the-shelf machine learning algorithms cannot handle. Even basic tasks of clustering, visualization and identification of recurring patterns are difficult.

Continue reading

The ocean significantly mitigates climate change by absorbing fossil fuel carbon from the atmosphere. Cumulatively since the preindustrial times, the ocean has absorbed 40% of emissions. To understand past changes, diagnose ongoing changes, and to predict the future behavior of the ocean carbon sink, we must understand its spatial and temporal variability. However, the ocean is poorly sampled and so we cannot do this from direct measurements.

Continue reading

The funded study examines policies that regulate the PO supply, including those related to prescription drug monitoring programs, pain management clinic laws, and prescribing limits. In this supplement, we add a set of complementary opioid policies that affect access to treatment for opioid use disorder (e.g., Medicaid coverage of medication for opioid use disorder).

Continue reading