6,607 research outputs found
Deepr: A Convolutional Net for Medical Records
Feature engineering remains a major bottleneck when creating predictive
systems from electronic medical records. At present, an important missing
element is detecting predictive regular clinical motifs from irregular episodic
records. We present Deepr (short for Deep record), a new end-to-end deep
learning system that learns to extract features from medical records and
predicts future risk automatically. Deepr transforms a record into a sequence
of discrete elements separated by coded time gaps and hospital transfers. On
top of the sequence is a convolutional neural net that detects and combines
predictive local clinical motifs to stratify the risk. Deepr permits
transparent inspection and visualization of its inner working. We validate
Deepr on hospital data to predict unplanned readmission after discharge. Deepr
achieves superior accuracy compared to traditional techniques, detects
meaningful clinical motifs, and uncovers the underlying structure of the
disease and intervention space
Transduction of DNA information through water and electromagnetic waves
The experimental conditions by which electromagnetic signals (EMS) of low
frequency can be emitted by diluted aqueous solutions of some bacterial and
viral DNAs are described. That the recorded EMS and nanostructures induced in
water carry the DNA information (sequence) is shown by retrieval of that same
DNA by classical PCR amplification using the TAQ polymerase, including both
primers and nucleotides. Moreover, such a transduction process has also been
observed in living human cells exposed to EMS irradiation. These experiments
suggest that coherent long range molecular interaction must be at work in water
so to allow the observed features. The quantum field theory analysis of the
phenomenon is presented.Comment: 10 pages, 6 figure
BioCloud Search EnGene: Surfing Biological Data on the Cloud
The massive production and spread of biomedical data around the web introduces new challenges related to identify computational approaches for providing quality search and browsing of web resources. This papers presents BioCloud Search EnGene (BSE), a cloud application that facilitates searching and integration of the many layers of biological information offered by public large-scale genomic repositories. Grounding on the concept of dataspace, BSE is built on top of a cloud platform that severely curtails issues associated with scalability and performance. Like popular online gene portals, BSE adopts a gene-centric approach: researchers can find their information of interest by means of a simple “Google-like” query interface that accepts standard gene identification as keywords. We present BSE architecture and functionality and discuss how our strategies contribute to successfully tackle big data problems in querying gene-based web resources. BSE is publically available at: http://biocloud-unica.appspot.com/
- …