Fusing Histology and Genomics via Deep Learning - IEEE TMI
- pathomic
- fusion
- multimodal
- pathology
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Fusing Histology and Genomics via Deep Learning - IEEE TMI
An OCaml kernel for Jupyter (IPython) notebook
Every day, millions of traders around the world are trying to make money by trading stocks. These days, physical traders are also being replaced by automated trading robots. Algorithmic trading market has experienced significant growth rate and large number of firms are using it. I have tried to build a Deep Q-learning reinforcement agent model to do automated stock trading.
Pan-Cancer Integrative Histology-Genomic Analysis via Multimodal Deep Learning - Cancer Cell
MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction (Bioinformatics)
Open source remote sensing dataset with benchmarks
Materials for GWU DNSC 6279 and DNSC 6290.
This trading-gym is the first trading for agent to train with episode of short term trading itself.
Usiigaci: stain-free cell tracking in phase contrast microscopy enabled by supervised machine learning
Wrapper for RDKit's RunReactants to improve stereochemistry handling
scPerturb: A resource and a python/R tool for single-cell perturbation data
ChatGPT Python Applications integrated with third party libraries and modules
Breaking captchas using torch
Repository for Nicheformer: a foundation model for single-cell and spatial omics
Analysis of High Frequency Trading on Bitcoin exchanges
This repository contains notebooks that show the usage of TensorFlow Lite for quantizing deep neural networks.
A python Two Source Energy Balance model for estimation of evapotranspiration with remote sensing data
🏃 The go-to single-cell Foundation Model
Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.
Tools for pen plotting in Python
⬛ Python Individual Conditional Expectation Plot Toolbox
Optimising chemical reactions using machine learning
InferPy: Deep Probabilistic Modeling with Tensorflow Made Easy
A collection of tasks to probe the effectiveness of protein sequence representations in modeling aspects of protein design