Adrián Bazaga's Projects
Rails frontend to The Genome Institute's drug gene interaction database.
Reference implementation of Diffusion2Vec (Complenet 2018).
Deep Learning GPU Training System
Docker image based on Debian Jessie specifically for SA-MP plugin building
A toolkit to learn how to model and interpret regulatory sequence data using deep learning.
A light-weight, no-dependency, vanilla JavaScript engine to drive the user's focus across the page
A little Python library for making simple Electron-like HTML/JS GUI apps
Official Elasticsearch Docker image
Publication-ready volcano plots with enhanced colouring and labeling
Genomic variation pipeline for the European Variation Archive, implemented using Spring Batch
Tools compatible with the infrastructure of the European Variation Archive pipeline
European Variation Archive REST web services API
Home for Elasticsearch examples available to everyone. It's a great way to get started.
The fastai deep learning library, plus lessons and and tutorials
Fast Gene Set Enrichment Analysis
Focus: a minimalist presentation theme for LaTeX Beamer.
Framework to solve Divide and Conquer problems in an easy way, you just need to define the specific Problem characteristics and the Framework will solve it for you with a D&C approach
:books: Freely available programming books
Implementation of Graph Auto-Encoders in TensorFlow
A framework for reproducible reinforcement learning research
Implementation of Graph Convolutional Networks in TensorFlow
A small project to add ggplot2 extensions
Collection of functions to enhance ggplot2 plots with results from statistical tests.
"Good Luck With That" Public License
links to conference publications in graph-based deep learning
Autoencoders for Link Prediction and Semi-Supervised Node Classification
Transformer for Graph Classification (in Pytorch and Tensorflow)
Gaussian node embeddings. Implementation of "Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking".
A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs" (MLGWorkshop 2017).