Academic Research
Comparative deep learning study classifying proteins into 18 functional categories.
Applied ML Research · Bioinformatics EPFL / Academic
Multi-architecture comparison using amino acid sequences and 3D structural data.
Stack
PyTorchGNNCNNLSTMscikit-learn
Context
Bioinformatics challenge classifying proteins into 18 functional categories using both amino acid sequences and 3D structural graph data.
Highlights
- ●Graph Neural Networks (HGP-SL) for 3D protein structure
- ●Fusion models combining GNN + MLP approaches
- ●Benchmark across GNN, CNN, LSTM, and logistic regression
How Frema Labs applies this for you
Have sequence and structured data to classify? We benchmark graph, sequence, and fusion models to find what genuinely fits your data.
Working on a similar problem?
Let's talk about how production-grade ML can move your roadmap forward.