scikit-fusion
scikit-fusion is a Python module for data fusion and learning over heterogeneous datasets. The core of scikit-fusion are recent collective latent factor models and large-scale joint matrix factorization algorithms.
[News:] Fast CPU and GPU-accelerated implementatons of some of our methods.
[News:] Scikit-fusion, collective latent factor models, matrix factorization for data fusion and learning over hetnets.
[News:] fastGNMF, fast implementation of graph-regularized non-negative matrix factorization using Facebook FAISS.
Dependencies
scikit-fusion is tested to work under Python 3.
The required dependencies to build the software are Numpy >= 1.7, SciPy >= 0.12, PyGraphviz >= 1.3 (needed only for drawing data fusion graphs) and Joblib >= 0.8.4.
Install
This package uses distutils, which is the default way of installing python modules. To install in your home directory, use:
python setup.py install --user
To install for all users on Unix/Linux:
python setup.py build
sudo python setup.py install
For development mode use:
python setup.py develop
Use
Let's generate three random data matrices describing three different object types:
>>> import numpy as np
>>> R12 = np.random.rand(50, 100)
>>> R13 = np.random.rand(50, 40)
>>> R23 = np.random.rand(100, 40)
Next, we define our data fusion graph:
>>> from skfusion import fusion
>>> t1 = fusion.ObjectType('Type 1', 10)
>>> t2 = fusion.ObjectType('Type 2', 20)
>>> t3 = fusion.ObjectType('Type 3', 30)
>>> relations = [fusion.Relation(R12, t1, t2),
fusion.Relation(R13, t1, t3),
fusion.Relation(R23, t2, t3)]
>>> fusion_graph = fusion.FusionGraph()
>>> fusion_graph.add_relations_from(relations)
and then collectively infer the latent data model:
>>> fuser = fusion.Dfmf()
>>> fuser.fuse(fusion_graph)
>>> print(fuser.factor(t1).shape)
(50, 10)
Afterwards new data might arrive:
>>> new_R12 = np.random.rand(10, 100)
>>> new_R13 = np.random.rand(10, 40)
for which we define the fusion graph:
>>> new_relations = [fusion.Relation(new_R12, t1, t2),
fusion.Relation(new_R13, t1, t3)]
>>> new_graph = fusion.FusionGraph(new_relations)
and transform new objects to the latent space induced by the fuser:
>>> transformer = fusion.DfmfTransform()
>>> transformer.transform(t1, new_graph, fuser)
>>> print(transformer.factor(t1).shape)
(10, 10)
scikit-fusion contains several applications of data fusion:
>>> from skfusion import datasets
>>> dicty = datasets.load_dicty()
>>> print(dicty)
FusionGraph(Object types: 3, Relations: 3)
>>> print(dicty.object_types)
{ObjectType(GO term), ObjectType(Experimental condition), ObjectType(Gene)}
>>> print(dicty.relations)
{Relation(ObjectType(Gene), ObjectType(GO term)),
Relation(ObjectType(Gene), ObjectType(Gene)),
Relation(ObjectType(Gene), ObjectType(Experimental condition))}
Selected publications (Methods)
- Data fusion by matrix factorization: http://dx.doi.org/10.1109/TPAMI.2014.2343973
- Jumping across biomedical contexts using compressive data fusion: https://academic.oup.com/bioinformatics/article/32/12/i90/2240593
- Survival regression by data fusion: http://www.tandfonline.com/doi/abs/10.1080/21628130.2015.1016702
- Gene network inference by fusing data from diverse distributions: https://academic.oup.com/bioinformatics/article/31/12/i230/216398
- Fast optimization of non-negative matrix tri-factorization: https://doi.org/10.1371/journal.pone.0217994
Selected publications (Applications)
- A comprehensive structural, biochemical and biological profiling of the human NUDIX hydrolase family: https://www.nature.com/articles/s41467-017-01642-w
- Gene prioritization by compressive data fusion and chaining: http://dx.doi.org/10.1371/journal.pcbi.1004552
- Discovering disease-disease associations by fusing systems-level molecular data: http://www.nature.com/srep/2013/131115/srep03202/full/srep03202.html
- Matrix factorization-based data fusion for gene function prediction in baker's yeast and slime mold: http://www.worldscientific.com/doi/pdf/10.1142/9789814583220_0038
- Matrix factorization-based data fusion for drug-induced liver injury prediction: http://www.tandfonline.com/doi/abs/10.4161/sysb.29072
- Collective pairwise classification for multi-way analysis of disease and drug data: https://doi.org/10.1142/9789814749411_0008
Tutorials
- Large-scale data fusion by collective matrix factorization, Basel Computational Biology Conference, [BC]^2 [Slides] [Handouts]
- Data fusion of everything, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC [Slides] [Handouts]