GPU Implementation of Pairwise Gaussian Mixture Models for Multi-Modal Gene Co-Expression Networks
نویسندگان
چکیده
منابع مشابه
Multi-modal Background Subtraction Using Gaussian Mixture Models
Background subtraction is a common first step in the field of video processing and it is used to reduce the effective image size in subsequent processing steps by segmenting the mostly static background from the moving or changing foreground. In this paper previous approaches towards background modeling are extended to handle videos accompanied by information gained from a novel 2D/3D camera. T...
متن کاملNon-rigid multi-modal object tracking using Gaussian mixture models
This work presents an approach to visual tracking based on dividing a target into multiple regions, or fragments. The target is represented by a Gaussian mixture model in a joint featurespatial space, with each ellipsoid corresponding to a different fragment. The fragment set and its cardinality are automatically adapted to the image data using an efficient region-growing procedure and updated ...
متن کاملNovel Methods for Surface EMG Analysis and Exploration Based on Multi-Modal Gaussian Mixture Models
This paper introduces a new method for data analysis of animal muscle activation during locomotion. It is based on fitting Gaussian mixture models (GMMs) to surface EMG data (sEMG). This approach enables researchers/users to isolate parts of the overall muscle activation within locomotion EMG data. Furthermore, it provides new opportunities for analysis and exploration of sEMG data by using the...
متن کاملGene co-expression networks via biclustering Differential gene co-expression networks via Bayesian biclustering models
Identifying latent structure in large data matrices is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are locally co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-re...
متن کاملAn open source C++ implementation of multi-threaded Gaussian mixture models, k-means and expectation maximisation
Modelling of multivariate densities is a core component in many signal processing, pattern recognition and machine learning applications. The modelling is often done via Gaussian mixture models (GMMs), which use computationally expensive and potentially unstable training algorithms. We provide an overview of a fast and robust implementation of GMMs in the C++ language, employing multi-threaded ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2951284