Restricted Randomness DBSCAN : A faster DBSCAN Algorithm

Pathak, Sashakt; Agarwal, Arushi; Ankita, Ankita; Gurve, Mahendra Kumar

doi:10.1145/3474124.3474204

Cited by 3 publications

(4 citation statements)

References 12 publications

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“…To identify and characterize these conserved biological patterns, we first simplified the Mapper graphs into 18 and 17 nodes for the tissue and stress lens function respectively (Figures 3 and 4). The core tissue-based Mapper graph has discrete nodes for each surveyed plant tissue with a gradual transition of leaves (node 1), to roots (2), fruits (11 and 13) and finally seeds (14,15, and 16; Figure 3a). At the 4th node, the Mapper graph proliferates into terminal branches of flower (node 9), stem (10), fruit (12), and mixtures of uncategorized tissue types (5 and 8).…”

Section: Resultsmentioning

confidence: 99%

The topological shape of gene expression across the evolution of flowering plants

Palande

Kaste

Roberts

et al. 2022

Preprint

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Since they emerged ~125 million years ago, flowering plants have evolved to dominate the terrestrial landscape and survive in the most inhospitable environments on earth. At their core, these adaptations have been shaped by changes in numerous, interconnected pathways and genes that collectively give rise to emergent biological phenomena. Linking gene expression to morphological outcomes remains a grand challenge in biology, and new approaches are needed to begin to address this gap. Here, we implemented topological data analysis (TDA) to summarize the high dimensionality and noisiness of gene expression data using lens functions that delineate plant tissue and stress responses. Using this framework, we created a topological representation of the shape of gene expression across plant evolution, development, and environment for the phylogenetically diverse flowering plants. The TDA-based Mapper graphs form a well-defined gradient of tissues from leaves to seeds, or from healthy to stressed samples, depending on the lens function. This suggests there are distinct and conserved expression patterns across angiosperms that delineate different tissue types or responses to biotic and abiotic stresses. Genes that correlate with the tissue lens function are enriched in central processes such as photosynthetic, growth and development, housekeeping, or stress responses. Together, our results highlight the power of TDA for analyzing complex biological data and reveal a core expression backbone that defines plant form and function.

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Section: Resultsmentioning

confidence: 99%

The topological shape of gene expression across the evolution of flowering plants

Palande

Kaste

Roberts

et al. 2022

Preprint

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“…We experimented with a range of value lengths of the intervals and the size of the overlap to identify the values that produced relatively stable mapper graphs. The clustering was performed using DBSCAN, a commonly used clustering algorithm in Mapper [14].…”

Section: Topological Data Analysis and The Shape Of Plant Gene Expres...mentioning

confidence: 99%

“…To identify and characterize these conserved biological patterns, we first simplified the Mapper graphs into 18 nodes for both the tissue and stress lens functions (Figs 3 and 4). The core tissue-based Mapper graph has discrete nodes for each surveyed plant tissue with a gradual transition of leaves (node 1), to roots (2), fruits (11 and 13), and, finally, seeds (14,15,and 16;Fig 3A). At the fourth node, the Mapper graph proliferates into terminal branches of flower (node 9), stem (10), fruit (12), and mixtures of uncategorized tissue types (5 and 8).…”

Section: Topological Shape Reflects the Underlying Biological Feature...mentioning

confidence: 99%

Topological data analysis reveals a core gene expression backbone that defines form and function across flowering plants

Palande,

Kaste,

Roberts

et al. 2023

PLoS Biol

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Since they emerged approximately 125 million years ago, flowering plants have evolved to dominate the terrestrial landscape and survive in the most inhospitable environments on earth. At their core, these adaptations have been shaped by changes in numerous, interconnected pathways and genes that collectively give rise to emergent biological phenomena. Linking gene expression to morphological outcomes remains a grand challenge in biology, and new approaches are needed to begin to address this gap. Here, we implemented topological data analysis (TDA) to summarize the high dimensionality and noisiness of gene expression data using lens functions that delineate plant tissue and stress responses. Using this framework, we created a topological representation of the shape of gene expression across plant evolution, development, and environment for the phylogenetically diverse flowering plants. The TDA-based Mapper graphs form a well-defined gradient of tissues from leaves to seeds, or from healthy to stressed samples, depending on the lens function. This suggests that there are distinct and conserved expression patterns across angiosperms that delineate different tissue types or responses to biotic and abiotic stresses. Genes that correlate with the tissue lens function are enriched in central processes such as photosynthetic, growth and development, housekeeping, or stress responses. Together, our results highlight the power of TDA for analyzing complex biological data and reveal a core expression backbone that defines plant form and function.

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“…According to the three-dimensional spatial characteristics of the lidar point cloud, Leng Z et al [1] directly chooses to directly distinguish the ground points and non-ground points according to the height of the point cloud. Although the speed is improved, it is easy to be blocked by close obstacles and form local observation.Paigwar A et al [4] uses the neural network GndNet: to return to the ground point, which is highly accurate and faster, but also consumes great computing resources.For road obstacle extraction, Najdataei H et al [6] adopts a clustering algorithm based on Euclidean distance, and Pathak S et al [7] proposes a restricted stochastic DBSCAN clustering algorithm, which all suffers from clustering time and accuracy defects. A. Milioto et al [10] also proposed the neural network RangeNet to complete the obstacle extraction, which also has a huge demand for computational resources.…”

Section: Introductionmentioning

confidence: 99%

Ground extraction and road obstacle segmentation method based on point cloud data

Yang²,

Feng³

et al. 2023

Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022)

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With the rise of artificial intelligence and other technologies, unmanned driving has become an important direction of future automobile development. The ground extraction of roads is a key task. The traditional method of ground extraction is limited to flat roads and the speed is slow. In view of the under-segmentation problem between the ground point cloud and the multi-objective object point cloud, we propose a ground segmentation algorithm for point cloud data based on multi-region segmentation. In this paper, the ground data can be accurately found by using the method of concentric region division for point cloud data. At the same time, it also has good robustness for uneven road surface, so as to provide a good foundation for the subsequent segmentation of road obstacles. For the extraction of road obstacles, we adopt the DBSCAN clustering method based on sub-region. According to the characteristics of near-density and distant point cloud, we adopt adaptive parameter selection for different regions to improve the accuracy of obstacle extraction. In order to verify the results, the use of KITTI data set on ubuntu18.04 using ROS system to complete the test, can maintain about 28fps processing speed.

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Restricted Randomness DBSCAN : A faster DBSCAN Algorithm

Cited by 3 publications

References 12 publications

The topological shape of gene expression across the evolution of flowering plants

The topological shape of gene expression across the evolution of flowering plants

Topological data analysis reveals a core gene expression backbone that defines form and function across flowering plants

Ground extraction and road obstacle segmentation method based on point cloud data

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