Various neural networks used in healthcare and hormonal research their GitHub repository and python code

 

Here’s a refined overview of neural networks and open-source Python tools in healthcare and hormonal research, including GitHub repositories and example code frameworks:
Key Healthcare AI Toolkits & Repositories
1. PyHealth
A comprehensive deep learning toolkit for healthcare predictive modeling, supporting EHR data from datasets like MIMIC-III, MIMIC-IV, eICU, and more.
Offers built-in neural network models including MLP, CNN, RNN (LSTM/GRU), Transformer, RETAIN, GAMENet, MICRON, SafeDrug, MoleRec, Deepr, and others
GitHub
pyhealth.readthedocs.io
.
Easy to install via PyPI and designed for reproducible research and benchmarking
GitHub
arXiv
.
2. Thyroid-Predict-ML (GitHub: yehonatanke/Thyroid-Predict-ML)
Predicts thyroid function (hyperthyroid, hypothyroid, euthyroid) using classical ML with preprocessing, visualization, and classification
GitHub
.
3. Thyroid-Disease-Neural-Network-Classifier (GitHub: AmoKorankye/...)
Implements a PyTorch-based neural network for classifying thyroid disease using demographics and blood tests; includes preprocessing, model training, and evaluation pipelines
GitHub
.
4. Neural Network for Diabetes Prediction (GitHub: KruthiShetty/...)
Uses forward neural networks in TensorFlow and PyTorch to predict diabetes. Includes model architecture, regularization (batch norm, dropout), and monitoring via TensorBoard
GitHub
.
5. PCOS Classification Project (GitHub: leahsaurus/...)
Uses hormone levels and physical indicators to predict PCOS via various ML techniques (Naive Bayes, SVM, Decision Trees) along with a neural network model in PyTorch
GitHub
.
6. HormoNet (from BMC Bioinformatics)
A deep learning model using CNNs to predict hormone–drug interactions (HDIs) from protein sequence and receptor data. Evaluated against RF, SVC, and XGBoost baselines
BioMed Central
.
Supporting Biological & Signal Processing Libraries
DGL-LifeSci
Toolkit for applying graph neural networks (GNNs) to life sciences—ideal for molecular property prediction, drug interactions, and hormone-related graph modeling
arXiv
.
DeepChem & Deep Neural Projects
Collections for applying deep learning in drug discovery, genomics, omics, methylation, and single-cell analysis using tools like autoencoders, graph networks, and CNNs
GitHub
.
NeuroKit2
A Python toolbox for physiological signal processing (EEG, ECG, HRV), offering fractal analysis, QRS detection, and more—useful in hormonal stress response modeling
Wikipedia
.
Summary Table of Resources
Task / Domain Tool / Repo
Healthcare predictive modeling PyHealth – wide array of AI models
Thyroid function classification Thyroid-Predict-ML & Thyroid NN Classifier
Diabetes prediction Neural Network for Diabetes Prediction
PCOS classification leahsaurus/PCOS_Classification_Project
Hormone-drug interaction prediction HormoNet (BMC Bioinformatics)
Molecular & hormonal graph modeling DGL-LifeSci
Drug discovery & omics modeling DeepChem & related NN tools
Physiological signal analysis NeuroKit2
Example: Using PyHealth for Hormonal Prediction
Here’s a simplified snippet to illustrate how you might set up a model using PyHealth for, say, hormone level prediction:
from pyhealth.datasets import MIMIC3Dataset
from pyhealth.models import MLP
from pyhealth import Utils

# Load dataset (replace with hormone dataset)
// mimic3 = MIMIC3Dataset(...)

# Initialize model
model = MLP(input_dim=100, hidden_dims=[64, 32], output_dim=1)

# Train model
Utils.train(model, mimic3, epochs=10, lr=0.001)

# Evaluate
Utils.evaluate(model, mimic3)
You can replace MLP with any of the available models (Transformer, RETAIN, SafeDrug, etc.) and adapt to your hormone dataset.
Would you like help—such as a direct link to a HormoNet-like GitHub repo, or a fully-coded example using DGL-LifeSci or NeuroKit2?
You said:
Example
ChatGPT said:
Sure! Here’s a detailed and structured overview of neural network tools and repositories focused on healthcare and hormonal (or related drug–protein) research. These resources include official GitHub repositories and example Python usage to help you get started.
1. HormoNet — Deep Learning for Hormone–Drug Interaction Prediction
A novel deep learning model named HormoNet predicts hormone–drug interactions (HDI) and assigns risk levels using CNN-based modeling over encoded protein features.
Features include amino acid composition (AAC) and pseudo-AAC (PseAAC) from hormone receptors and drug targets
BioMed Central
.
Handles class imbalance with SMOTE, compared CNN, MLP, and LSTM—CNN performed best
PMC
.
The model and benchmark datasets are openly available on GitHub: EmamiNeda/HormoNet
BioMed Central
.
Example Python Snippet (Using HormoNet)
# clone and install (example shell commands)
!git clone https://github.com/EmamiNeda/HormoNet.git
%cd HormoNet
# assume requirements.txt or environment setup exists

from hormonet import build_model, load_data, train, evaluate

# Load your HDI dataset
X_train, y_train, X_test, y_test = load_data('HDI_dataset.csv')

# Build CNN model
model = build_model(input_shape=X_train.shape[1], model_type='CNN')

# Train (epochs=50, lr=0.00025, batch=16)
train(model, X_train, y_train, epochs=50, lr=2.5e-4, batch_size=16)

# Evaluate
metrics = evaluate(model, X_test, y_test)
print(metrics)
2. GNN for Drug–Drug Interaction Prediction
A Graph Neural Network (GNN) approach to predict drug–drug interactions using molecular representations and drug graphs.
Repository: RagaLG/GNN-Drug-Interactions on GitHub
GitHub
.
Uses Graph Attention Networks (GAT) for encoding drug features and a classification head for interaction prediction.
Sample Python Workflow
# clone and install
!git clone https://github.com/RagaLG/GNN-Drug-Interactions.git
%cd GNN-Drug-Interactions

# Open Jupyter notebooks to run the model
# e.g. DDI(adj_node_edge).ipynb, variation AE_adj.ipynb
3. Deep Learning for Drug–Protein Interaction (CYP450)
A Python repo using Keras/TensorFlow to predict whether drugs are substrates of Cytochrome P450 enzymes based on drug and protein sequence features.
GitHub: Jhird/Deep-Learning-For-Drug-Protein-Interactions
GitHub
.
Contains notebooks handling dataset preparation (from KEGG API), model training, and performance visualization.
Example Use
# clone and install
!git clone https://github.com/Jhird/Deep-Learning-For-Drug-Protein-Interactions.git
%cd Deep-Learning-For-Drug-Protein-Interactions

# Step through the Jupyter notebooks in order to preprocess and run models
Summary Table
Task / Domain Tool / Repository
Hormone–Drug Interaction Prediction HormoNet – CNN-based model; GitHub: EmamiNeda/HormoNet
Drug–Drug Interaction Prediction GNN–Drug–Interactions – GAT-based graph model
Drug–Protein (CYP450) Interaction Deep-Learning-For-Drug-Protein-Interactions – Keras/TensorFlow
Next Steps
Clone the repository of interest and run demo notebooks or Python scripts.
Experiment with your own datasets, fine-tuning hyperparameters or adapting model architectures.
Extend for hormonal or protein interaction research by feeding your own datasets or adding new features.
Let me know if you'd like help translating one of these models to hormonal-specific scenarios, or crafting a Jupyter notebook template for your use!