A proof-of-concept demo of deep-learning for diagnostics (using microbiome data).
Currently, work is in under progress for this proof-of-concept project. The README file along with the Python scripts will keep on changing accordingly.
This is a proof-of-concept pipeline based on the TensorFlow library developed by Google. The idea is simple: using a publicly available microbiome dataset, we wish to develop a deep-learning, diagnostic platform based on TensorFlow.
For this purpose, dataset corresponding to a microbiome study by Lozupone et al., was obtained from the Qiita database.
A microbiome dataset consists of a count matrix in which the bacteria characterized by Next Generation Sequencing are the rows and the samples from which the bacterial count information is obtained are columns. Additionally, there is meta-data associated with the samples. On a technical note, the bacterial count here refers to OTUs.
In this POC analysis, we are using the infection status of the individuals (HIV positive vs. negative) as the response variable and count of bacteria (OTUs) as explanatory variables in the model building process. For simple illustrative purposes, we will assess the accuracy.
- Enthought Canopy Python 2.7.6 | 64-bit
- virtualenv 15.0.1
- TensorFlow 0.9.0
- NumPy 1.11.1
- pandas 0.18.1
The Mac OS X, CPU only, Python 2.7 version of TensorFlow was installed via pip in a virtualenv on my MacBook Pro having OS X El Capitan Version 10.11.4.
Additionally, I'm using scikit-learn 0.18.dev0 to compare performance of more classical machine learning algorithms with deep learning methods in TensorFlow.
There are two main scripts in the src folder, each of which implement and run a separate model in TensorFlow. They can be simply run by the following command:
python softmax.py
python cnn.py
The output will be the accuracy of the model.
In addition to this, there is a sub-directory called sklearn-comparison, which has a bunch of scripts. The two mains scripts to run are:
python sklearn_models.py
python blending.py
sklearn_models.py will run 5 different models implemented in scikit-learn and print the accuracy of each model. blending.py will perform blending of the 5 models and run logistic regression on top of it and return the final accuracy.
Accuracy of Softmax Regression: 0.909
Accuracy of Convolutional Neural Network: 0.773
Accuracy of Random Forest Classifier: 0.864
Accuracy of SVM: 0.864
Accuracy of Gradient Boosting Classifier: 0.773
Accuracy of Gaussian Naive Bayes Classifier: 0.909
Accuracy of Multi-layer Perceptron Classifier: 0.864
Accuracy of Blending: 0.909