15,974 research outputs found
A shortest-path based clustering algorithm for joint human-machine analysis of complex datasets
Clustering is a technique for the analysis of datasets obtained by empirical
studies in several disciplines with a major application for biomedical
research. Essentially, clustering algorithms are executed by machines aiming at
finding groups of related points in a dataset. However, the result of grouping
depends on both metrics for point-to-point similarity and rules for
point-to-group association. Indeed, non-appropriate metrics and rules can lead
to undesirable clustering artifacts. This is especially relevant for datasets,
where groups with heterogeneous structures co-exist. In this work, we propose
an algorithm that achieves clustering by exploring the paths between points.
This allows both, to evaluate the properties of the path (such as gaps, density
variations, etc.), and expressing the preference for certain paths. Moreover,
our algorithm supports the integration of existing knowledge about admissible
and non-admissible clusters by training a path classifier. We demonstrate the
accuracy of the proposed method on challenging datasets including points from
synthetic shapes in publicly available benchmarks and microscopy data
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Iterative Application of the aiNET Algorithm in the Construction of a Radial Basis Function Neural Network
This paper presents some of the procedures adopted in the construction of a Radial Basis Function Neural Network by iteratively applying the aiNET, an Artificial Immune Systems Algorithm. These procedures have shown to be effective in terms of i) the free determination of centroids inspired by an immune heuristics; and ii) the achievement of appropriate minimal square errors after a number of iterations. Experimental and empirical results are compared aiming at confirming (or not) some hypotheses
Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images
of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL
maps are derived through computational staining using a convolutional neural network trained to
classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and
correlation with overall survival. TIL map structural patterns were grouped using standard
histopathological parameters. These patterns are enriched in particular T cell subpopulations
derived from molecular measures. TIL densities and spatial structure were differentially enriched
among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial
infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic
patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for
the TCGA image archives with insights into the tumor-immune microenvironment
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