Protein classification by Mounir Messaoudi

This Repo contains :

• a pdf resuming all the work done
• data analysis (data_analysis.ipynb)
• code for preprocessing (preprocessign.py)
• code for training (train.py)
• code for evaluation (test.py)
• evaluation analysis (evaluation.ipynb)

Achieve 99% test accuracy trained by ResNet with Adam optimizer, for classifying 250 classes.

Hardware

---

• Windows 10
• Intel(R) Core(TM) i5
• Memory: 16G
• NVIDIA GeForce GTX 1060 * 1 (4617 MB memory)

Pipeline

---

To reproduce the result, there are three steps:

1. Dataset Preparation, run preprocessing.py
2. Training, for each backcbone, run train.py
3. Evaluation, for each backbone, run test.py

Environment

---

Requirements

#Install requirements
pip install -r requirements.txt

#In case of problems
intall tensorflow, Bio and propythia using pip and it should work.

Project directory structured

+-- assets/
|   +-- family_distribution.png
|   +-- ...
+-- backbone/
|   +-- layers/
|   |   +--  ResidualBlock.py
|   |   +--  ...
|   +-- resnet.py
|   +-- ...
+-- logs/
+-- output/
|   +-- exp1
|   +-- ...
+-- preprocessed_data
|   +-- full/
|   |   +-- ...
|   +-- sample/
|   |   +-- ...
+-- random_split/
|   +-- train
|   +-- dev
|   +-- test
+-- utils/
|   +-- dataloader.py
|   +-- model.py
|   +-- tools.py
+-- preprocessing.py
+-- README.md
+-- requirements.txt
+-- train.py
+-- test.py
+-- pfam Classification - Mounir Messaoudi.pdf
+-- evaluation.ipynb
+--data_analysis.ipynb

Dataset preparation

---

Download Dataset

If no folder random_split, download and put the folder random_plit inside this repo. "Kaggle: Pfam seed random split"

Extract random_split.zip to dataset directory.

After downloading and pre-process, the data directory is structured as:

+-- dataset/
|   +-- train
|   +-- |   +-- data-00000-of-00080
|   +-- |   +-- ...
|   +-- dev
|   +-- |   +-- data-00000-of-00010
|   +-- |   +-- ...
|   +-- test
|   +-- |   +-- data-00000-of-00010
|   +-- |   +-- ...

Dataset Analysis

---

Look at the notebook data_analysis.ipynb

Training

---

Usage

usage : preprocessing.py

It will preprocess the data from random_split/, and save the preprocessed data in preprocessed_data/.

## Training
---------------
### Usage

usage: train.py [--output_version OUTPUT_VERSION] [--data_dir DATA_DIR] [--backbone BACKBONE] (REQUIRE) [--num_epochs NUM_EPOCHS] [--batch_size BATCH_SIZE] [--seed SEED]

value for backbone : resnet, mobilnetv2, light_mobilnetv2, LSTM
The rest of the parameters are optional
### Example Usage

Train resnet

```python
python train.py --output_version resnet1 --num_epochs 5 --batch_size 256 --backbone resnet 

Train LSTM

python train.py --output_version LSTM1 --num_epochs 5 --batch_size 256 --backbone LSTM

Visualize

tensorboard --logdir "./logs"

Training Random Forest

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Usage

open RF_Training.ipynb and run it all

Evaluate (Test)

---

Usage

usage: test.py [--model_dir DATA_DIR] (REQUIRE)
               [--OUTPUT_dir TRAIN_DIR] [--output_version OUTPUT_VERSION]

Example Usage

test trained model

python test.py --model_dir output/20240107-094325

Result (with adam optimizer)

---

	Test Acc
resnet	0.9999
LSTM	0.9947
mobilenetV2 light	0.9998
mobilenetV2	0.9997
Random Forest	0.02

Reference

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• Using Deep Learning to Annotate the Protein Universe
• Kaggle: Pfam seed random split
• Automatic structure classification of small proteins using random forest
• Efficacy of different protein descriptors in predicting protein functional families
• Protein Sequence Classification
• Deep Learning to Annotate the Protein Universe
• Using Deep Learning to Annotate the Protein Universe
• Deep Dive into Machine Learning Models for Protein Engineering
• Automatic structure classification of small proteins using random forest
• Pytorch gene sequence classification by protCNN
• A comprehensive framework for advanced protein classification and function prediction using synergistic approaches: Integrating bispectral analysis, machine learning, and deep learning