CONFERENCE SPONSORS


CONFERENCE HOST UNIVERSITY AND GOLD SPONSOR:

Purdue University
Vice President, Office of Research
Bioinformatics Core


 SILVER SPONSORS:


Indiana University
University Information Technology Services
Department of Biology
School of Informatics and Computing
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University of Michigan, Dept of Computational Medicine and Bioinformatics

BRONZE SPONSORS:


The Research Division
of Ohio University
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Department of Computer Science and Engineering
Eck Institute for Global Health
Complex Networks Lab
University of Notre Dame


EXHIBITOR SHOWCASE SPONSOR:

 

Cincinnati Childrens’s Hospital Medical Center
Division of Biomedical Informatics, University of Cincinnati


POSTER AWARDS SPONSOR:


Faculty of 1000


BEST PAPER AWARD SPONSOR:


Springer


INDUSTRY SPONSOR:



University of Michigan Bioinformatics Core
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PerkinElmer


GENERAL SPONSOR:


Purdue University

Agricultural Research

Introduction to Python

Python is a versatile language commonly used for computational biology. This workshop will provide an overview of a lot of common functionality in Python with no previous experience required. We will also discuss some common libraries. Participants will also have an opportunity to test their skills on some simple programming problems before moving on to some more advanced topics. We will cover basic programming concepts such as variables, functions, loops, and booleans as preparation for forming a basic understanding of machine learning concepts implemented in Python.

Organizer: Ria Talwar

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Machine Learning with Python

Machine learning has applications in many fields, including computational biology. Within this field, ML is often used in assisting in diagnosing and treating patients. It also has been applied to drug development. There are many types of machine learning models, but they can all be classified into unsupervised and supervised. For this, we will focus on supervised models. Supervised Models are ones that predict an output value(s) from inputs. Within supervised models, there are many different. types of models, such as logistic regression and neural networks.

Organizer: Ekansh Mittal

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