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ML for Genetic Engineering

ML for Genetic Engineering

We built a classification ML model to help plant breeders cultivate the healthiest, high-yielding crops.

See winning solutions here:

The problem:

Phenotype Prediction Challenge

Build a machine learning model to predict the value of a certain numeric phenotypes given a high-dimensional genotype information,
You will be given a training dataset consisting of numerically encoded genotype data for genetically distinct rice varieties, along with records of certain agronomically important traits for the same varieties (such as grain yield, plant height, days to flowering). Your goal is to implement a model that would predict the values of these phenotypes given genotype data of an unseen rice sample. (It can be a single model for all traits or one model for each trait. You may use any programming language or ML framework. We will also provide a baseline model against which you can test your model. Once your model(s) is ready for submission, you will need to run it on the test dataset which we will provide. Submitted models will be evaluated using metrics such as mean absolute error and the r-squared score. For each phenotype, we will compare your model with a baseline linear model and the models of other participants.


Shanelle Recheta

March 19, 2021

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  1. DEEP LEARNING 3 Phases of the Pipeline * for High-Throughput

    Phenotyping CLASSIFICATION into different groups PREDICTION of different plant characteristics IDENTIFICATION of genetic make up of various plants Multilayer Perceptron (MLP) and Artificial Neural Network (ANN)
  2. Future Work Key features to be implemented: • Crowdsourced Phenotype

    Dataset • Crop Geotagging with KYC Verification • Automated Standardization • Hello World Phenotyping • Cross validation, Ensemble Models & Feature Engineering
  3. THE TEAM Alec Xavier Manabat Design Engineer Analog Devices, Inc.

    Shanelle Grace Recheta Data Science Consultant FTW Foundation | Cropital TOWARDS INCLUSIVITY IN RICE SCIENCE!