7 research outputs found
Parea: multi-view ensemble clustering for cancer subtype discovery
Multi-view clustering methods are essential for the stratification of
patients into sub-groups of similar molecular characteristics. In recent years,
a wide range of methods has been developed for this purpose. However, due to
the high diversity of cancer-related data, a single method may not perform
sufficiently well in all cases. We present Parea, a multi-view hierarchical
ensemble clustering approach for disease subtype discovery. We demonstrate its
performance on several machine learning benchmark datasets. We apply and
validate our methodology on real-world multi-view cancer patient data. Parea
outperforms the current state-of-the-art on six out of seven analysed cancer
types. We have integrated the Parea method into our developed Python package
Pyrea (https://github.com/mdbloice/Pyrea), which enables the effortless and
flexible design of ensemble workflows while incorporating a wide range of
fusion and clustering algorithms
On the usage of health records for the design of virtual patients: a systematic review
Augmentor: Image augmentation library in Python for machine learning
<p>Augmentor is an image augmentation library in Python for machine learning. It aims to make image augmentation platform and framework independent, more convenient, less error prone, and reproducible. It employs a stochastic approach using building blocks that allow for operations to be pieced together in a pipeline.</p