Virtualitics wraps up exciting data science hackathon with Caltech students leveraging the power of Virtualitics Immersive Platform (VIP) to solve unique business cases and challenges.
We are proud to have just wrapped up our Data Science Hackathon for Caltech students in which they leveraged the power of Virtualitics Immersive Platform (VIP) to solve unique business cases in the Bioinformatics and Predictive Maintenance & IoT spaces.
Each team consisted of 2-5 students where they competed for $9000 in prizes and internship/full-time opportunities with the Virtualitics AI and Engineering teams. Within 24 hours of gaining access to VIP students were able to immediately leverage the speed and ease of use of VIP's augmented analytical tools to garner key insights.
Virtualitics Immersive Platform (VIP) enables you to augment your analytical practices to easily find, visualize, and share insights from complex data sets 100X faster than traditional business intelligence tools. VIP creates a no-code/low code environment where users can leverage patented ML & AI models to analyze data at the speed of now. VIP helps identify and detect correlations within your data at a record pace.
🏆 Check out the winners!
Predictive Maintenance & IoT
🥇 Solving Supply Chain Inefficiencies using VIP (Team 11)
Team Members: Esmir Mesic, Ian Fowler, Devin Hartzell, Krish Mehta
Caltech students leveraged Virtualitics Immersive Platform (VIP) to identify inefficiencies in a company's supply chain and provided recommendations on alternative warehouse locations.
A global logistics company with records pertaining to supply chain needs to analyze their supply chain data. The students analyzed this data in order to identify and eliminate inefficiencies in the supply chain.
The data is geospatial and has many features to analyze, some of which are categorical. Either of these factors make the data hard to visualize through common methods such as Excel and Matplotlib.
The team constructed features in Python to better analyze the tardiness of warehouse orders and geographically categorize them, and then used VIP to segregate the data by category in order to pinpoint inefficiencies in our supply chain. For instance, they found that moving operations for Mens’ Footwear from Moldova to Italy would reduce lateness in their orders.
VIP capabilities used:
Within VIP, generation of 2D and 3D histograms, bar, scatter, and violin plots allowed us to quickly conduct exploratory data analysis. VIP’s playback and filter features allowed us to investigate interesting parts of our data and begin to track down our root issue. VIP’s Python API allowed us to seamlessly generate and visualize network graphs from geospatial data. This was the most intuitive way to view the data, essential for bringing us to our final insights. VIP’s network explainability tool helped us understand the data better.
🥇 Drug Repurposing Using Network Analysis (Team 3)
Team Members: Kevin Huang, Anish Shenoy, Grace Lu, Jae Yoon Kim, Wesley Huang
Caltech students leveraged Virtualitics Immersive Platform (VIP) to predict which drugs could be repurposed to treat certain diseases while accounting for drug-drug interactions.
The Objective was to identify which drugs could potentially be repurposed to treat different diseases based on network analysis of drug-target-interaction data.
The Challenge was to find which drugs could potentially be repurposed based on unstructured and difficult to process drug-bank data with drugs that have no known use for a given class of disease.
To solve this problem, Caltech students first processed their data to get the similarity between drugs based on their shared targets. They clustered their data to locate the candidates and verified their accuracy by comparing older and newer versions of their drug-target-interaction data to see if their candidates were indeed repurposed after medical research.
VIP capabilities used
Network Graph Community Detection: clustering the data helped group drugs with similar properties.
Histograms: using the histogram feature helped figure out what the leading drug property of each cluster of drugs was to identify which drugs didn’t match this leading property and label it for repurposing.
Eccentricity: after displaying which repurposed drugs were successfully verified, we ran eccentricity to see if there was a pattern in them.
🥈 Multiplexed single cell RNA-seq for network-based analysis of COVID-19 inflammatory response (Team 10)
Team Members: Saehui Hwang, Liam Silvera, Archie Shahidullah
Caltech students leveraged Virtualitics Immersive platform (VIP) to create network graphs to visualize how different conditions affect gene expression in multiplexed single cells of COVID-19 inflammatory response.
Honorable Mention: Differential Causal Gene Networks in Asthma (Team 1)
Team Members: Agnim Agarwal, Pranav Patil, Brandon Guo, Neha Dalia
Caltech students leveraged Virtualitics Immersive Platform to identify genes independently correlated with asthma to determine underlying causal relationships.