Machine Learning Application for molecular biology

Posted on Mar 21, 2016

Contributed by Wansang Lim. He is currently in the NYC Data Science Academy 12 week full time Data Science Bootcamp program taking place between January 11th to April 1st, 2016. This post is based on his fourth class project – machine learning(due on the 8th week of the program).

Introduction

The object of this project is to build a actually working shiny application for molecular analysis with machine learning. It predicts undependable variable with Support vector machine. This app is fit for RAPD analysis.

RAPD analysis
RAPD stands for 'Random Amplified Polymorphic DNA'. It is a type of PCR reaction, but the segments of DNA that are amplified are random. The scientist performing RAPD creates several arbitrary, short primers (8–12 nucleotides), then proceeds with the PCR using a large template of genomic DNA, hoping that fragments will amplify. By resolving the resulting patterns, a semi-unique profile can be gleaned from a RAPD reaction.

No knowledge of the DNA sequence for the targeted genome is required, as the primers will bind somewhere in the sequence.

Support Vector Machine
Support Vector Machines are based on the concept of decision planes that define decision boundaries. A decision plane is one that separates between a set of objects having different class memberships. A schematic example is shown in the illustration below. In this example, the objects belong either to class GREEN or RED. The separating line defines a boundary on the right side of which all objects are GREEN and to the left of which all objects are RED. Any new object (white circle) falling to the right is labeled, i.e., classified, as GREEN (or classified as RED should it fall to the left of the separating line).
vector

Data Generation

The data is based on already published paper(Utilization of RAPD Markers to Assess Genetic Diversity of Wild Populations of North American Ginseng ( Panax quinquefolium )Article in Planta Medica 73(1):71-6 · January 2007). It has 111 rows and 120 columns. It is binary data which have just 1 and 0. First, the gnomic DNA was amplified with random primer and converted in 1 and 0 data. The details are in the published paper.

Application Structure

This app is designed to upload file because it can be used for any data for RAPD analysis if the data format is same regardless of number of rows and columns (Fig 1). When upload complete, it separate training and test set by 7 : 3 ratio. And it shows the prediction table and the result in side panel. When it separate into two data set, if one of them has non polymorphic column, it remove the column at both data set.

whole
Fig 1. The result prediction table for whole data

It can chose best 10 column. The ideal data for prediction is extremely skewed. For example, all NY population is one and all non NY population is zero. To find the similar column, data in both NY and non NY group is normalized twice and it find the maximum difference between the frequency. And it fit it in support vector machine (Fig 2). When we use best 10 column, it reduce the experiment cost substantially with the minimum decrease of prediction from 93% to 83%.
best10
Fig. 2. The app chose best 10 column to reduce cost.

One of important feature of this app is predicting unknown data. A molecular biology researcher upload it's data. It predicts whether it came from New York or non New York population.

Conclusion

For conclusion, this app can do
1. choosing best primer or column set(or column) set to reduce cost
2. predicting the origin of samples.

About Author

Wansang Lim

I recently completed MS computer science degree in New York University(Manhattan NY) concentrating on machine learning and big data . Before it, I studied software development and android development. Also, I am Ph.D of Agriculture with a lot...
View all posts by Wansang Lim >

Leave a Comment

No comments found.

View Posts by Categories


Our Recent Popular Posts


View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI