Repository Index

A knowledge graph and a set of tools for drug repurposing

• drug-repurposing
• knowledge-graph
• graph-neural-networks
• knowledge-graph-embeddings

A light-weight deep reinforcement learning framework for portfolio management. This project explores the possibility of applying deep reinforcement learning algorithms to stock trading in a highly modular and scalable framework.

Benchmarks for Low Latency (Streaming) solutions including Apache Storm, Apache Spark, Apache Flink, ...

• benchmarks
• low-latency
• streaming

🍇 GRAPE is a Rust/Python Graph Representation Learning library for Predictions and Evaluations

• graph
• high-performance
• machine-learning
• knowledge-graph

food image to recipe with deep convolutional neural networks.

• convolutional-neural-networks
• keras
• chef
• deep-learning

Mostly experiments based on "Advances in financial machine learning" book

Cleora AI is a general-purpose open-source model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data. Created by Synerise.com team.

• ai
• graphs
• synerise
• embeddings

Sources codes for: Mastering Python for Finance, Second Edition

• machine-learning
• python
• ai

ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission (CHIL 2020 Workshop)

• clinicalbert
• hospital-readmission
• prediction

Tutorials, datasets, and other material associated with textbook "A First Course in Network Science" by Menczer, Fortunato & Davis

• social-network
• indiana-university
• network-science
• textbook

TranslatedTensorFlow in practice: using LSTM to predict lottery results

Genomic Pretrained Network - GPN, GPN-MSA, PhyloGPN, GPN-Star

• dna
• language-model
• variant-effect-prediction
• genomics

Deep Reinforcement Learning for de-novo Drug Design

• deeplearning
• reinforcement-learning
• cheminformatics
• molecular-modeling

Tensorflow implementation of "Learning Deep Features for Discriminative Localization"

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.

MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction (Bioinformatics)

This trading-gym is the first trading for agent to train with episode of short term trading itself.

This is the code for "Bitcoin Prediction" by Siraj Raval on Youtube

Analysis of High Frequency Trading on Bitcoin exchanges

A python Two Source Energy Balance model for estimation of evapotranspiration with remote sensing data

• source-energy-balance
• canopy
• soil
• radiometric-temperatures

🏃 The go-to single-cell Foundation Model

• batch-correction
• cell-biology
• celltype-annotation
• denoising

Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.

• disentanglement
• drug-discovery
• genomics
• perturbation

⬛ Python Individual Conditional Expectation Plot Toolbox

• machine-learning
• interpretability

Optimising chemical reactions using machine learning

• chemistry
• machine-learning
• optimization
• bayesian-optimization