Digging deep into Golgi phenotypic diversity with unsupervised machine learning

Digging deep into Golgi phenotypic diversity with unsupervised machine learning

The synthesis of glycans and the sorting of proteins are critical functions of the Golgi apparatus and depend on its highly complex and compartmentalized architecture. High-content image analysis coupled to RNA interference screening offers opportunities to explore this organelle organization and the gene network underlying it. To date, image-based Golgi screens have based on a single parameter...

Unsupervised Machine Learning

Digging deep into “dirty” drugs – modulation of the methylation machinery

DNA methylation and histone modification are epigenetic mechanisms that result in altered gene expression and cellular phenotype. The exact role of methylation in myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML) remains unclear. However, aberrations (e.g. loss-/gain-of-function or up-/down-regulation) in components of epigenetic transcriptional regulation in general, and of the ...

Digging deep into weighted patient data through multiple-level patterns

Large data volumes have been collected by healthcare organizations at an unprecedented rate. Today both physicians and healthcare system managers are very interested in extracting value from such data. Nevertheless, the increasing data complexity and heterogeneity prompts the need for new efficient and effective data mining approaches to analyzing large patient datasets. Generalized association...

CliqueCNN: Deep Unsupervised Exemplar Learning

Exemplar learning is a powerful paradigm for discovering visual similarities in an unsupervised manner. In this context, however, the recent breakthrough in deep learning could not yet unfold its full potential. With only a single positive sample, a great imbalance between one positive and many negatives, and unreliable relationships between most samples, training of Convolutional Neural networ...