Gene Ontology (GO) Prediction using Machine Learning Methods

10/30/2017
by   Haoze Wu, et al.
0

We applied machine learning to predict whether a gene is involved in axon regeneration. We extracted 31 features from different databases and trained five machine learning models. Our optimal model, a Random Forest Classifier with 50 submodels, yielded a test score of 85.71 the baseline score. We concluded that our models have some predictive capability. Similar methodology and features could be applied to predict other Gene Ontology (GO) terms.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/19/2023

SpotHitPy: A Study For ML-Based Song Hit Prediction Using Spotify

In this study, we approached the Hit Song Prediction problem, which aims...
research
04/15/2021

mlf-core: a framework for deterministic machine learning

Machine learning has shown extensive growth in recent years and is now r...
research
03/30/2022

Predicting Winners of the Reality TV Dating Show The Bachelor Using Machine Learning Algorithms

The Bachelor is a reality TV dating show in which a single bachelor sele...
research
05/06/2019

Analysis of Gene Interaction Graphs for Biasing Machine Learning Models

Gene interaction graphs aim to capture various relationships between gen...
research
04/11/2020

Explaining the Relationship between Internet and Democracy in Partly Free Countries Using Machine Learning Models

Previous studies have offered a variety of explanations on the relations...
research
06/27/2021

Use of Machine Learning Technique to maximize the signal over background for H → ττ

In recent years, artificial neural networks (ANNs) have won numerous con...
research
08/27/2022

Improving debris flow evacuation alerts in Taiwan using machine learning

Taiwan has the highest susceptibility to and fatalities from debris flow...

Please sign up or login with your details

Forgot password? Click here to reset