FORKS: Finding Orderings Robustly using k-means and Steiner trees

Sharma, MK; H, Li; Sengupta, Debarka; Prabhakar, Shyam; Jayadeva,

doi:10.1101/132811

Cited by 4 publications

(3 citation statements)

References 58 publications

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“…The genetic distance between a subset of 44 cultivars was 0.322 or less, indicating a narrow genetic base and suggesting a recent common progenitor, consistent with monoecy and a poor seed set. Propagation resulted in seeds with different phenotypes (Sharma and Bal 1958). However, 81 percent of the fragments examined were polymorphic, suggesting that cultivars are genetically highly polymorphic.…”

Section: Discussionmentioning

confidence: 99%

Nuaulu Use and Management of Culturally Salient Polymorphisms inCodiaeum variegatum: Explaining the Biocultural Dimensions of Leaf Variegation in a Southeast Asian Ornamental

Ellen

2021

Journal of Ethnobiology

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Codiaeum variegatum has become a well-known ornamental plant in Europe and North America and has long been culturally significant in Southeast Asia and the Pacific, for example as a boundary plant. This paper asks, firstly, how variations in foliage are classified, managed, and valued in one population (Nuaulu on the island of Seram, eastern Indonesia), and how this relates to the range of uses to which these variations are put. Secondly, the paper suggests that this particular case helps shed light on the importance of leaf variegation as an organoleptic quality in the context of biocultural evolution. It is noted that the features that evolved in its area of endemism are those making it attractive as an ornamental globally. While color variations in foliage combining genotypic cultivar differences, clonal differences, and age-dependent differences produce phenotypic instability and are a problem for ornamental plant producers in a commercial context, they are not a problem for Nuaulu.

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Section: Discussionmentioning

confidence: 99%

Nuaulu Use and Management of Culturally Salient Polymorphisms inCodiaeum variegatum: Explaining the Biocultural Dimensions of Leaf Variegation in a Southeast Asian Ornamental

Ellen

2021

Journal of Ethnobiology

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“…Many of these metrics are case‐study‐specific; hence, they cannot be used for the general evaluation of TI methods. For example, TI methods are independently evaluated on whether they are robust to: (i) changes in variables , (ii) subsampling and (iii) their inherent stochasticity . Robustness to changes in the variables we employ for TI cannot be applied in mass cytometry data because the number of measured quantities is low compared to that of single‐cell RNAseq data.…”

Section: Discussionmentioning

confidence: 99%

Learning Pathway Dynamics from Single‐Cell Proteomic Data: A Comparative Study

2020

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Single‐cell platforms provide statistically large samples of snapshot observations capable of resolving intrercellular heterogeneity. Currently, there is a growing literature on algorithms that exploit this attribute in order to infer the trajectory of biological mechanisms, such as cell proliferation and differentiation. Despite the efforts, the trajectory inference methodology has not yet been used for addressing the challenging problem of learning the dynamics of protein signaling systems. In this work, we assess this prospect by testing the performance of this class of algorithms on four proteomic temporal datasets. To evaluate the learning quality, we design new general‐purpose evaluation metrics that are able to quantify performance on (i) the biological meaning of the output, (ii) the consistency of the inferred trajectory, (iii) the algorithm robustness, (iv) the correlation of the learning output with the initial dataset, and (v) the roughness of the cell parameter levels though the inferred trajectory. We show that experimental time alone is insufficient to provide knowledge about the order of proteins during signal transduction. Accordingly, we show that the inferred trajectories provide richer information about the underlying dynamics. We learn that established methods tested on high‐dimensional data with small sample size, slow dynamics, and complex structures (e.g. bifurcations) cannot always work in the signaling setting. Among the methods we evaluate, Scorpius and a newly introduced approach that combines Diffusion Maps and Principal Curves were found to perform adequately in recovering the progression of signal transduction although their performance on some metrics varies from one dataset to another. The novel metrics we devise highlight that it is difficult to conclude, which one method is universally applicable for the task. Arguably, there are still many challenges and open problems to resolve. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

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“…The randomized feature mapping method was proposed by Rahimi and Recht [18,19] and has been widely used in classification applications [21,23]. The input samples can be mapped into a randomized Fourier feature space, in which the inner product of any two transformed vectors is equivalent to a shift-invariant kernel (such as Gaussian kernel) operation.…”

Section: The Random Fourier Feature Mappingmentioning

confidence: 99%

A Kernel Least Mean Square Algorithm Based on Randomized Feature Networks

2018

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Abstract:To construct an online kernel adaptive filter in a non-stationary environment, we propose a randomized feature networks-based kernel least mean square (KLMS-RFN) algorithm. In contrast to the Gaussian kernel, which implicitly maps the input to an infinite dimensional space in theory, the randomized feature mapping transform inputs samples into a relatively low-dimensional feature space, where the transformed samples are approximately equivalent to those in the feature space using a shift-invariant kernel. The mean square convergence process of the proposed algorithm is investigated under the uniform convergence analysis method of a nonlinear adaptive filter. The computational complexity is also evaluated. In Lorenz time series prediction and nonstationary channel equalization scenarios, the simulation results demonstrate the effectiveness of the proposed algorithm.

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FORKS: Finding Orderings Robustly using k-means and Steiner trees

Cited by 4 publications

References 58 publications

Nuaulu Use and Management of Culturally Salient Polymorphisms inCodiaeum variegatum: Explaining the Biocultural Dimensions of Leaf Variegation in a Southeast Asian Ornamental

Nuaulu Use and Management of Culturally Salient Polymorphisms inCodiaeum variegatum: Explaining the Biocultural Dimensions of Leaf Variegation in a Southeast Asian Ornamental

Learning Pathway Dynamics from Single‐Cell Proteomic Data: A Comparative Study

A Kernel Least Mean Square Algorithm Based on Randomized Feature Networks

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