gwdidonato
CEO & Co-Founder @GenoGra Postdoctoral researcher @necst - PoliMi Ph.D. in Information Technology @necst Bioengineer @ PoliMi and UIC.
@GenoGraMilano
gwdidonato's Stars
benedekrozemberczki/awesome-graph-classification
A collection of important graph embedding, classification and representation learning papers with implementations.
danielegrattarola/spektral
Graph Neural Networks with Keras and Tensorflow 2.
XiaoxinHe/Awesome-Graph-LLM
A collection of AWESOME things about Graph-Related LLMs.
totogo/awesome-knowledge-graph
A curated list of Knowledge Graph related learning materials, databases, tools and other resources
lh3/minigraph
Sequence-to-graph mapper and graph generator
maickrau/GraphAligner
lh3/gfatools
Tools for manipulating sequence graphs in the GFA and rGFA formats
DeepGraphLearning/pLogicNet
almiheenko/AGB
Interactive visualization of assembly graphs
LucaCappelletti94/lectures-notes
My latex notes on whatever I'm studying. All are available to the public, but please take them with a grain of salt and notify me in case of errors :)
lh3/gwfa
Proof-of-concept implementation of GWFA for sequence-to-graph alignment
albertozeni/LOGAN
LOGAN: High-Performance Multi-GPU X-Drop Long-Read Alignment.
xMAnton/BioGrakn
A Knowledge Graph-based Semantic Database for Biomedical Sciences
AlbertoParravicini/segretini-matplottini
A collection of Matplotlib and Seaborn recipes and utilities collected over years of colorful plot-making
necst/iron
Image Registration on FPGAs
necst/grcuda
Polyglot CUDA integration for the GraalVM
albertozeni/starlight
Starlight: A Kernel Optimizer for GPU Processing
necst/xlnx-project-template
Template Repository for Xilinx HLS design flow
kkgerasimov/CIM2Matpower
Python package which is meant to be executed from a Matlab script in order to transform a CIMv14 ENTSO-E profile transmission system network model to a Matpower case structure.
necst/gpjson
GPU-based JSON data processing system accessible via all GraalVM languages
gwdidonato/KGE-Perf-Results
Experimental results on runtime performances of KGE methods, performed through the KGE-Perf framework. The framework and the obtained results are described in: A.S. Valeriani, G.W. Di Donato and M.D. Santambrogio, “Exploring the runtime performance of knowledge graph embedding methods”, in 2021 IEEE6th International Forum on Research and Technology for Society and Industry (RTSI) (IEEE RTSI 2021), Napoli, Italy, Sep. 2021.
albertozeni/ksw2z-fpga
High-Performance FPGA implementation of the KSW2-z algorithm
albertozeni/XDropXOHW-Public
High-Performance FPGA implementation of the X-drop algorithm
lh3/GFA-spec
Graphical Fragment Assembly (GFA) Format Specification