4,558 research outputs found
Recovery Conditions and Sampling Strategies for Network Lasso
The network Lasso is a recently proposed convex optimization method for
machine learning from massive network structured datasets, i.e., big data over
networks. It is a variant of the well-known least absolute shrinkage and
selection operator (Lasso), which is underlying many methods in learning and
signal processing involving sparse models. Highly scalable implementations of
the network Lasso can be obtained by state-of-the art proximal methods, e.g.,
the alternating direction method of multipliers (ADMM). By generalizing the
concept of the compatibility condition put forward by van de Geer and Buehlmann
as a powerful tool for the analysis of plain Lasso, we derive a sufficient
condition, i.e., the network compatibility condition, on the underlying network
topology such that network Lasso accurately learns a clustered underlying graph
signal. This network compatibility condition relates the location of the
sampled nodes with the clustering structure of the network. In particular, the
NCC informs the choice of which nodes to sample, or in machine learning terms,
which data points provide most information if labeled.Comment: nominated as student paper award finalist at Asilomar 2017. arXiv
admin note: substantial text overlap with arXiv:1704.0210
Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow
We propose a method to classify cardiac pathology based on a novel approach
to extract image derived features to characterize the shape and motion of the
heart. An original semi-supervised learning procedure, which makes efficient
use of a large amount of non-segmented images and a small amount of images
segmented manually by experts, is developed to generate pixel-wise apparent
flow between two time points of a 2D+t cine MRI image sequence. Combining the
apparent flow maps and cardiac segmentation masks, we obtain a local apparent
flow corresponding to the 2D motion of myocardium and ventricular cavities.
This leads to the generation of time series of the radius and thickness of
myocardial segments to represent cardiac motion. These time series of motion
features are reliable and explainable characteristics of pathological cardiac
motion. Furthermore, they are combined with shape-related features to classify
cardiac pathologies. Using only nine feature values as input, we propose an
explainable, simple and flexible model for pathology classification. On ACDC
training set and testing set, the model achieves 95% and 94% respectively as
classification accuracy. Its performance is hence comparable to that of the
state-of-the-art. Comparison with various other models is performed to outline
some advantages of our model
Pathway-Based Genomics Prediction using Generalized Elastic Net.
We present a novel regularization scheme called The Generalized Elastic Net (GELnet) that incorporates gene pathway information into feature selection. The proposed formulation is applicable to a wide variety of problems in which the interpretation of predictive features using known molecular interactions is desired. The method naturally steers solutions toward sets of mechanistically interlinked genes. Using experiments on synthetic data, we demonstrate that pathway-guided results maintain, and often improve, the accuracy of predictors even in cases where the full gene network is unknown. We apply the method to predict the drug response of breast cancer cell lines. GELnet is able to reveal genetic determinants of sensitivity and resistance for several compounds. In particular, for an EGFR/HER2 inhibitor, it finds a possible trans-differentiation resistance mechanism missed by the corresponding pathway agnostic approach
SAFS: A Deep Feature Selection Approach for Precision Medicine
In this paper, we propose a new deep feature selection method based on deep
architecture. Our method uses stacked auto-encoders for feature representation
in higher-level abstraction. We developed and applied a novel feature learning
approach to a specific precision medicine problem, which focuses on assessing
and prioritizing risk factors for hypertension (HTN) in a vulnerable
demographic subgroup (African-American). Our approach is to use deep learning
to identify significant risk factors affecting left ventricular mass indexed to
body surface area (LVMI) as an indicator of heart damage risk. The results show
that our feature learning and representation approach leads to better results
in comparison with others
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