Jekyll2024-10-29T18:32:48+00:00https://pykeen.github.io/feed.xmlPyKEENPredictions for the People
PyKEEN Project TeamUsing Clinical Data to Embed Patients2020-08-29T08:00:00+00:002020-08-29T08:00:00+00:00https://pykeen.github.io/2020/08/29/clepThe expression of each gene is often measured in groups of patients with a given
disease to compare to healthy patients. It is then calculated which genes are
higher, lower, or similar to healthy patients. We’ve used these calculations
to introduce patients into a biomedical knowledge graph containing genes
so we could generate an embedding for each patient using PyKEEN. After,
we showed these embeddings are useful for classifying new patients and other
downstream ML tasks.
]]>Charles Tapley HoytBenchmarking Study2020-08-07T08:00:00+00:002020-08-07T08:00:00+00:00https://pykeen.github.io/2020/08/07/benchmarkingWe’ve run an unprecedented large benchmarking study. This image describes the results
on the FB15k237 dataset across several knowledge graph embedding models, loss functions,
training approaches, and usages of explicit modeling of inverse triples. This is just one
of several datasets analyzed in this study. In our manuscript, we also assess the reproducibility
of old models’ best reported hyperparameters.
]]>Charles Tapley HoytMetaresearch Recommendations2020-06-23T07:00:00+00:002020-06-23T07:00:00+00:00https://pykeen.github.io/2020/06/23/metaresearch-recommendationsWe used PyKEEN to train a scholarly recommendations system to suggest
papers to read, grants to apply to, and collaborations to make.
]]>Charles Tapley HoytPathway Crosstalk Predictions2019-02-15T08:00:00+00:002019-02-15T08:00:00+00:00https://pykeen.github.io/2019/02/15/pathway-crosstalkWe used PyKEEN to train a pathway crosstalk analysis platform that identifies
which biological pathways are connected, giving further insight into normal
human pathophysiology and potentially leading to novel hypotheses for understanding
the aetiology of complex disease leading to novel drug discovery.
