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.

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This project has a two-fold aim. First, we seek to determine what makes an idea seem novel versus ordinary and if there is an ideal mix of the two. Second, building on these findings, we build a generative model that suggests tweaks to an idea that enhance its perceived creativity and appeal. We will pursue these two aims using 69K recipes and reviews from allrecipes.com. We will use NLP approach to extract important features from the recipe such as ingredients, preparation instruction and review content.

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Prediction Markets have been used to forecasts outcomes of research interest using market mechanism (See https://www.nature.com/news/the-power-of-prediction-markets-1.20820). A decentralized prediction market, Augur, has been created on blockchain for betting purposes (See, https://www.augur.net/). An alternative approach to prediction market has been proposed in Dalal et al (https://www.sciencedirect.com/science/article/abs/pii/S0040162511000734). This project proposes to develop a new model for decentralized prediction market which can be used to elicit opinions of university researchers on socially important issues. Specifically, the project will use Ethereum based platform to develop a smart contract and an ERC-20 compliant token for researchers to participate in the new market.

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The federal government spends billions of dollars a year supporting rural broadband (internet access), subsidizing build-out in low-density areas that do not have broadband (unserved areas). However, it is not clear whether the rural areas most in need are receiving a fair share of the funding. Using a very large dataset of broadband availability, census data and recent auction results, the project will analyze whether unserved areas with high racial diversity or lower median income are receiving a fair share of funding. Depending on team size, we will also attempt to create a shareable master data set building on OpenStreetMap and other sources that provides key data points for census units.

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Water joined gold, oil and other commodities traded on Wall Street, highlighting worries that the life-sustaining natural resource may become scarce across more of the world. In the state of California, the biggest U.S. agriculture market and world’s fifth-largest economy, this challenge is particularly prevalent. Farmers, hedge funds and municipalities are now able to prepare for the risk that future water availability issues can bring in the state of California.

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Recent advances in genomic technologies have led to the identification of many novel disease-gene associations, enabling more precise diagnoses. Along with the technologies enabling rapid DNA sequencing, multiple computational approaches have been developed to identify structural variants (i.e. relatively large deletions and duplications of genomic sequences). These workflows can lead to the identification of different structural variants, raising the risk of missing disease-causing variants when using only one of those methods.

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Columbia Data Science Institute (DSI) Scholars Program

The DSI Scholars Program is to engage and support undergraduate and master students in participating data science related research with Columbia faculty. The program’s unique enrichment activities will foster a learning and collaborative community in data science at Columbia.

Columbia University DSI

New York, NY