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Certificate of Completion

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THIS ACKNOWLEDGES THAT

HAS COMPLETED THE SPRING 2024 DATA SCIENCE BOOT CAMP

Hemaa Selvakumar

Roman Holowinsky, PhD

MAY 01, 2024

DIRECTOR

DATE

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TEAM

Nuclear Localization Signal (NLS) Prediction - NLSeer

Scott Auerbach, Ukamaka Nnyaba, Ming Zhang, Yingyi Guo, Hemaa Selvakumar, Cisil Karaguzel

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The purpose of the project is to build a prediction tool that estimates the possibility of having nuclear localization signals inside a protein's sequence based on the significance of each amino acid. Nuclear localization signals (NLS) are segments of a protein sequence that direct it towards the nucleus and have been implicated in human diseases and play an important role in many biological pathways. We employed datasets including whole protein sequences with and without nuclear localization signals and trained both classifiers and neural networks to predict whether or not a protein contained a NLS. Using a random forest classifier, we developed a web app through Flask that can predict whether or not a given protein is likely to have a NLS, and if so, also estimate the likelihood of each amino acid contributing to a NLS.

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