mims-harvard/nimfa
Nimfa: Nonnegative matrix factorization in Python
This tool helps scientists and researchers analyze large datasets, like gene expression data, by breaking them down into more manageable and interpretable components. You input a complex data matrix, and it outputs two smaller matrices that reveal underlying patterns or 'factors' within your data, making it easier to identify significant relationships or features. It's ideal for biomedical researchers, bioinformaticians, and data scientists working with high-dimensional biological data.
557 stars. No commits in the last 6 months.
Use this if you need to decompose complex datasets, such as genomic or proteomic data, into underlying non-negative patterns or features to gain deeper biological insights.
Not ideal if your data contains negative values that are meaningful to your analysis, or if you are looking for general-purpose matrix decomposition outside of non-negative constraints.
Stars
557
Forks
138
Language
Python
License
—
Category
Last pushed
Feb 12, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/mims-harvard/nimfa"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related tools
eliorc/node2vec
Implementation of the node2vec algorithm.
mims-harvard/decagon
Graph convolutional neural network for multirelational link prediction
ferencberes/online-node2vec
Node Embeddings in Dynamic Graphs
claws-lab/jodie
A PyTorch implementation of ACM SIGKDD 2019 paper "Predicting Dynamic Embedding Trajectory in...
mims-harvard/Raindrop
Graph Neural Networks for Irregular Time Series