Background: Sparse representation has achieved tremendous success recently. Low-rank representation is one of the successful methods. It is aimed to capture underlying low-dimensional structures of high dimensional data and attracted much attention in the area of the pattern recognition and signal processing. Such successful applications were mainly to its effectiveness in exploring lowdimensional manifolds embedded in data, which can be naturally characterized by low rankness of the data matrix. Objective: In this paper, we review the theoretical and numerical models based on low rank representation and hope the review can attract more research in bioinformatics. Method: Low rank representation is particularly well suited to big data analysis in bioinformatics. The first reason is that the interested objects are naturally sparse, like copy number variations. The second reason is that there exist strong correlations among various modalities for the same object, like DNA, RNA and methylation. Results and Conclusion: Its applications in bioinformatics area, including mining of key genes subset, finding common patterns across various modalities and biomedical image analysis were categorically summarized. Keywords: Sparse representation, low-rank representation, big data analysis, bioinformatics, low-dimensional structures, high dimensional data.

Representation

Rank (graph theory)

External Data Representation

Modalities

10.2174/1574893612666171121155347

Cite

Citations (6)

Using nonlinear diffusion and mean shift to detect and connect cross-sections of axons in 3D optical microscopy images

Medical Image Analysis (2008)

Hongmin Cai Xiaoyin Xu Ju Lu Jeff W. Lichtman Siu Pang Yung

10.1016/j.media.2008.03.002

Cite

Citations (25)

Incorporating Discrete Wavelet Transformation Decomposition Convolution into Deep Network to Achieve Light Training

Lecture notes in computer science (2021)

Guihua Tao Wentao Rong Wanlin Weng Tingting Dan Bin Zhang

Convolution (computer science)

10.1007/978-3-030-86340-1_25

Cite

Citations (3)

Reconstruction of 3D Retina from Multi-viewed Stereo Fundus Images via Dynamic Registration

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2020)

Tingting Dan Zhihao Fan Yu Hu Bin Zhang Guihua Tao

The human retinal surface resembles to a sphere while it is captured by two-dimensional (2D) planar imaging to have a stereo sequence in clinical practice. Reconstructing its three-dimensional (3D) structure from the 2D planar retinal images is crucial for analyzing the relationship between the topological morphology and clinical implication. In this regard, we propose to reconstruct the 3D retina structure from 2D stereo fundus images via dynamic registration. The fundus images from different viewpoints are first co-registrated by using multi-scale deep convolutional feature and geometric structure feature by building their transformation function. The aligned images are then mosaicked together and a 3D reconstruction is obtained by a learned weighted smoothing project the registered images onto 3D coordinates. We compare the proposed registration method with five state-of-the-art methods. Extensive experimental results demonstrate that the proposed framework achieves superior performances, even with challenging scenarios in which the tested images are severely degraded by illness, large eyeball rotation and low resolutions.

Smoothing

Fundus (uterus)

Image registration

Feature (linguistics)

10.1109/bibm49941.2020.9313171

Cite

Citations (2)