Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2018
The goals of this workshop are to discuss challenges in explainability of current Machine Leaning and Deep Analytics (MLDA) used in biocomputing and to start the discussion on ways to improve it. We define explainability in MLDA as easy to use inform...
PURPOSE: For diffusion data sets including low and high b-values, the intravoxel incoherent motion model is commonly applied to characterize tissue. The aim of the present study was to show that machine learning allows a model-free approach to determ...
OBJECTIVES: Identifying subgroups of ICU patients with similar clinical needs and trajectories may provide a framework for more efficient ICU care through the design of care platforms tailored around patients' shared needs. However, objective methods...
While only recently developed, the ability to profile expression data in single cells (scRNA-Seq) has already led to several important studies and findings. However, this technology has also raised several new computational challenges. These include ...
MOTIVATION: Cellular Electron CryoTomography (CECT) enables 3D visualization of cellular organization at near-native state and in sub-molecular resolution, making it a powerful tool for analyzing structures of macromolecular complexes and their spati...
MOTIVATION: Recent advances in mass cytometry allow simultaneous measurements of up to 50 markers at single-cell resolution. However, the high dimensionality of mass cytometry data introduces computational challenges for automated data analysis and h...
We demonstrate that model-based unsupervised learning can uniquely discriminate single-cell subpopulations by their gene expression distributions, which in turn allow us to identify specific genes for focused functional studies. This method was appli...
RATIONALE: Timely detection of pseudoprogression (PSP) is crucial for the management of patients with high-grade glioma (HGG) but remains difficult. Textural features of O-(2-[18F]fluoroethyl)-L-tyrosine positron emission tomography (FET-PET) mirror ...
Studies in health technology and informatics
Jan 1, 2017
In clinical practice, many patients may have unknown or missing values for some predictors, causing that the developed risk models cannot be directly applied on these patients. In this paper, we propose an incremental learning approach to apply a dev...
Studies in health technology and informatics
Jan 1, 2017
Dividing patients into similar groups plays a significant role in implementing personalized care. Clinicians and researchers have been applying patient grouping techniques in disease phenotyping, risk stratification, and personalized medicine. Howeve...
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