Machine Learning and Its Applications in Molecular Biophysics Jacob Andrzejczyk and Harish Vashisth Department of Chemical Engineering, University of New.

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Presentation transcript:

Machine Learning and Its Applications in Molecular Biophysics Jacob Andrzejczyk and Harish Vashisth Department of Chemical Engineering, University of New Hampshire, Durham, NH 03824 Introduction Potential Projects Predicting Hydrogen-Deuterium Exchange Patterns using Support Vector Machines Predicting RNA-Protein Interactions using Homology Comparison Networks Optimizing Multi-Dimensional Free Energy Surfaces Using Artificial Neural Networks Machine Learning is the scientific study of algorithms and statistical models that computer systems use in order to perform a specific task effectively without having to use explicit instructions, relying on patterns and inference. Computational Biophysics uses numerical algorithms to study the physical principles underlying biological phenomena and processes. Provides a means of approximating solutions for theoretical biophysical problems lacking closed- form solutions, and simulating systems for which experiments are deemed infeasible. Machine Learning Tools Linear Stochastic Gradient Descent K-Nearest Neighbors Decision Trees Random Forests Support Vector Machines(SVMs) Neural Networks Principal Component Analysis(PCA) Non-Negative Matrix Factorization(NMF) DBSCAN Agglomerative Clustering K-Means Clustering Classification Methods These methods are a form of supervised learning in which the inputs/outputs are known. Machine Learning Libraries SciKit-Learn NumPy SciPy Pandas Matplotlib Ipython TensorFlow Keras Shogun Clustering Methods References These methods are a form of unsupervised learning in which the inputs are known, and outputs are unknown. Albon, Chris. Machine Learning with Python Cookbook. O'Reilly Media, 2018. Guido, Sarah. Introduction to Machine Learning with Python. O'Reilly Media, 2016. Kuriyan, John, et al. The Molecules of Life: Physical and Chemical Principles. Garland Science, Taylor & Francis Group, 2013. Liu, Yong, et al. “Diffusion Network of CO in FeFe-Hydrogenase.” The Journal of Chemical Physics, vol. 149, no. 20, 2018, p. 204108., doi:10.1063/1.5054877. Roy, Rahul. “Best Python Libraries for Machine Learning.” GeeksforGeeks, 19 Jan. 2019, www.geeksforgeeks.org/best-python-libraries-for-machine-learning/. “Wolfram Demonstrations Project.” Trajectories on the Müller-Brown Potential Energy Surface, demonstrations.wolfram.com/TrajectoriesOnTheMullerBrownPotentialEnergySurface/. Acknowledgements We are grateful for financial support through US National Science Foundation (Award #CBET-1554558).