LOG-Means

Fritz, Manuel; Behringer, Michael; Schwarz, Holger

doi:10.14778/3407790.3407813

Cited by 13 publications

(4 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To better highlight the genes that are most impacted by Ume6 depletion, we applied k-means clustering, which groups genes by their Euclidian distance while minimizing variation. Using the “elbow method,” we found k = 3 to be optimal for subdividing our 144 genes in the Ume6 regulon ( Thorndike 1953 ; Fritz et al . 2020 ).…”

Section: Resultsmentioning

confidence: 99%

The transcriptional regulator Ume6 is a major driver of early gene expression during gametogenesis

Harris

Ünal

2023

Genetics

View full text Add to dashboard Cite

The process of gametogenesis is orchestrated by a dynamic gene expression program, where a vital subset constitutes the early meiotic genes (EMGs). In budding yeast, the transcription factor Ume6 represses EMG expression during mitotic growth. However, during the transition from mitotic to meiotic cell fate, EMGs are activated in response to the transcriptional regulator Ime1 through its interaction with Ume6. While it is known that binding of Ime1 to Ume6 promotes EMG expression, the mechanism of EMG activation remains elusive. Two competing models have been proposed whereby Ime1 either forms an activator complex with Ume6 or promotes Ume6 degradation. Here, we resolve this controversy. First, we identify the set of genes that are directly regulated by Ume6, including UME6 itself. While Ume6 protein levels increase in response to Ime1, Ume6 degradation occurs much later in meiosis. Importantly, we found that depletion of Ume6 shortly before meiotic entry is detrimental to EMG activation and gamete formation, whereas tethering of Ume6 to a heterologous activation domain is sufficient to trigger EMG expression and produce viable gametes in the absence of Ime1. We conclude that Ime1 and Ume6 form an activator complex. While Ume6 is indispensable for EMG expression, Ime1 primarily serves as a transactivator for Ume6.

show abstract

Section: Resultsmentioning

confidence: 99%

The transcriptional regulator Ume6 is a major driver of early gene expression during gametogenesis

Harris

Ünal

2023

Genetics

View full text Add to dashboard Cite

show abstract

“…In the specific cases presented in this work, prior information guided the decision on the number of clusters, i.e., the number of synthetically generated intervals in the synthetic example guided us to select four clusters, and the number of lithologies described in the Elvira deposit in the real example guided us to select 18 clusters. In the case of not having any prior information, there are methodologies to estimate in advance the number of classes needed to capture the highest percentage of variability possible, i.e., elbow [30] or Log-Mean [31] methods.…”

Section: Unsupervised Domainingmentioning

confidence: 99%

Automated Multi-Scale and Multivariate Geological Logging from Drill-Core Hyperspectral Data

et al. 2022

View full text Add to dashboard Cite

Hyperspectral drill-core scanning adds value to exploration campaigns by providing continuous, high-resolution mineralogical data over the length of entire boreholes. However, multivariate mineralogical data must be transformed into lithological domains such that it is compatible with interpolation techniques and be usable for geomodeling. Manual interpretation of multivariate drill-core data is a challenging, time-consuming and subjective task, and automated or semi-automated approaches are needed. However, naive machine-learning techniques that ignore the distinct spatial structure and multi-scale nature of geological systems tend to produce geologically unreasonable results. Automated geological logging and multi-scale hierarchical domaining of drill-cores has been previously addressed in several studies by means of scalograms from a wavelet transform and tessellation, albeit exploiting only univariate information. The methodology involves the extraction of the local first principal component at a neighborhood of each observation, and the segmentation of the resulting series of scores with a continuous wavelet transform for boundary detection. In this way, the correlation pattern between the variables is incorporated into the segmentation. The scalogram accurately locates the geological boundaries at depth and yields hierarchical geological domains with mineralogical composition characteristics. The performance of this approach is demonstrated on a synthetic as well as a real multivariate dataset. The real dataset consists of mineral abundances derived from drill-core hyperspectral imaging data acquired in Elvira, a shale-hosted volcanogenic massive sulfide deposit located in the Iberian Pyrite Belt, where 7000 m of drill-core were acquired along 80 boreholes. The extracted domains are sensible from a geological point of view and spatially coherent across the boreholes in cross-sections. The results at relevant scales were qualitatively validated by comparing against the lithological log. This method is fast, is appropriate for multivariate geological data along boreholes, and provides a choice of scales for hierarchical geological domains along boreholes with mineralogical composition characteristics that can be modeled in 3D. Our approach provides an automatic way to transform hyperspectral image-derived mineral maps into vertically coherent geological units that are appropriate inputs for 3D geological modeling workflows. Moreover, the method improves the boundary detection and geological domaining by making use of multivariate information.

show abstract

“…A variety of cluster validity indices have been proposed to determine the cluster number. For instance, DBI, SI, DU, Gap statistic, XBI, PBM, CHI, Elbow method, Hypersphere density-based method [16], Log_means [17], and so on.…”

Section: Indices For Determining Cluster Number and Validation Ofmentioning

confidence: 99%

A New Method to Determine Cluster Number Without Clustering for Every K Based on Ratio of Variance to Range in K-Means

Ri¹,

Kang²,

Kim³

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In many clustering algorithms such as K-means and FCM, the cluster number K needs to be known beforehand. In this paper, we propose a new method to determine the cluster number without clustering for every K in K-means. We introduce a new statistics RVR (ratio of variance to range) and conduct Monte Carlo analysis of its characteristics. Based on the RVR, we propose an algorithm to determine the cluster number K and perform clustering utilizing it. We evaluate its effectiveness by performing a simulation test with different types of datasets; first, with real datasets, whose real number of clusters and components are known and second, with synthetic datasets. We observe a significant improvement in speed and quality of determining the cluster number and therefore clustering. Finally, we hope the proposed algorithm to be used efficiently and widely for clustering of multidimensional data.

show abstract

LOG-Means

Cited by 13 publications

References 36 publications

The transcriptional regulator Ume6 is a major driver of early gene expression during gametogenesis

The transcriptional regulator Ume6 is a major driver of early gene expression during gametogenesis

Automated Multi-Scale and Multivariate Geological Logging from Drill-Core Hyperspectral Data

A New Method to Determine Cluster Number Without Clustering for Every K Based on Ratio of Variance to Range in K-Means

Contact Info

Product

Resources

About