A collection of tasks to probe the effectiveness of protein sequence representations in modeling aspects of protein design
- protein
- protein-sequences
- protein-design
- representation-learning
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A collection of tasks to probe the effectiveness of protein sequence representations in modeling aspects of protein design
TensorFlow Lite models for MIRNet for low-light image enhancement.
REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets --- https://arxiv.org/abs/2004.07999
tutorials made for biologists to learn deep learning
Cyber Defence Monitoring Course Suite :: Suricata, Arkime (and others in the past)
Compilado de recursos sobre Computação Quântica em português
Detecting cancers on mobile phones
A database of paths that represent the mechanism of action from a drug to a disease in an indication.
Code repository for The Automation Ahead series, showcasing practical examples for GenAI-driven automation in investments. Each installment dives into specific tools, techniques, and use cases to help professionals assess and apply GenAI effectively.
Peax is a tool for interactive visual pattern search and exploration in epigenomic data based on unsupervised representation learning with autoencoders
Various Jupyter notebooks about Common Crawl data
Research experiments exploring uncommon quant techniques.
Repository for paper scMulan: a multitask generative pre-trained language model for single-cell analysis.
Classification and Gradient-based Localization of Chest Radiographs using PyTorch.
Deep learning model for single-cell inference of multi-omic profiles from a single input modality.
The repository host gives no description.
CancerFoundation: A single-cell RNA sequencing foundation model to decipher drug resistance in cancer
Sources for the Network Science Cheatsheets
Signal generators ( AD9833, AD9834, AD9850, ADF4351) tools box.
Collection of ASR models for English TFLite models for faster inference.
UV-first benchmark for protein-ligand docking with reproducible annotation, pocket similarity, and HPC workflows.
Comparing the performance of Stacked Ensemble Learning & machine learning algorithms like Random Forest, Decision Tree, Adaboost, Gradient Boost and XGBoost Classifier in Python for Stock Market Trend Prediction.