scholarly journals A comparison of physical mapping algorithms based on the maximum likelihood model

2003 ◽  
Vol 19 (11) ◽  
pp. 1303-1310 ◽  
Author(s):  
J. Huang ◽  
S. M. Bhandarkar
Keyword(s):  
Maximum Likelihood ◽  
Physical Mapping ◽  
Mapping Algorithms ◽  
Likelihood Model

scholarly journals Real-time solar image classification: Assessing spectral, pixel-based approaches

2019 ◽  
Vol 9 ◽  
pp. A38
Author(s):  
J. Marcus Hughes ◽  
Vicki W. Hsu ◽  
Daniel B. Seaton ◽  
Hazel M. Bain ◽  
Jonathan M. Darnel ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Random Forest ◽  
Image Classification ◽  
Real Time ◽  
Data Science ◽  
Extreme Ultraviolet ◽  
Machine Learning Algorithms ◽  
The Sun ◽  
Solar Image ◽  
Likelihood Model

In order to utilize solar imagery for real-time feature identification and large-scale data science investigations of solar structures, we need maps of the Sun where phenomena, or themes, are labeled. Since solar imagers produce observations every few minutes, it is not feasible to label all images by hand. Here, we compare three machine learning algorithms performing solar image classification using Extreme Ultraviolet (EUV) and Hα images: a maximum likelihood model assuming a single normal probability distribution for each theme from Rigler et al. (2012) [Space Weather 10(8): 1–16], a maximum-likelihood model with an underlying Gaussian mixtures distribution, and a random forest model. We create a small database of expert-labeled maps to train and test these algorithms. Due to the ambiguity between the labels created by different experts, a collaborative labeling is used to include all inputs. We find the random forest algorithm performs the best amongst the three algorithms. The advantages of this algorithm are best highlighted in: comparison of outputs to hand-drawn maps; response to short-term variability; and tracking long-term changes on the Sun. Our work indicates that the next generation of solar image classification algorithms would benefit significantly from using spatial structure recognition, compared to only using spectral, pixel-by-pixel brightness distributions.

scholarly journals Revealing the architecture of genetic and epigenetic regulation: a maximum likelihood model

2013 ◽  
Vol 15 (6) ◽  
pp. 1028-1043 ◽  
Author(s):  
F. Wang ◽  
S. Zhang ◽  
Y. Wen ◽  
Y. Wei ◽  
H. Yan ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Epigenetic Regulation ◽  
Likelihood Model

GENETIC PARAMETERS FOR CALVING EASE IN AYRSHIRES

1990 ◽  
Vol 70 (1) ◽  
pp. 67-71 ◽  
Author(s):  
R. I. CUE
Keyword(s):  
Maximum Likelihood ◽  
Key Words ◽  
Genetic Correlation ◽  
Direct Effect ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Random Effect ◽  
Restricted Maximum Likelihood ◽  
Calving Ease ◽  
Likelihood Model

Estimates of genetic parameters of calving ease were obtained in Ayrshires. A restricted maximum likelihood model was used with the fixed effects of herd, month-season of calving, sex of calf and dam weight, and the random effect of sire (of calf). The heritability of the direct effect in heifers and in adult cows was approximately 2%, with a genetic correlation between the direct effect in heifers and in adult cows of close to 70%. Key words: Variance, heritability, calving ease, Ayrshire

Assessment of patterns of temperature-dependent sex determination using maximum likelihood model selection

Ecoscience ◽  
2003 ◽  
Vol 10 (3) ◽  
pp. 265-272 ◽  
Author(s):  
Matthew H. Godfrey ◽  
Virginie Delmas ◽  
Marc Girondot
Keyword(s):  
Maximum Likelihood ◽  
Model Selection ◽  
Sex Determination ◽  
Temperature Dependent ◽  
Likelihood Model

scholarly journals Maximum-likelihood model fitting for quantitative analysis of SMLM data

2021 ◽  
Author(s):  
Yu-Le Wu ◽  
Philipp Hoess ◽  
Aline Tschanz ◽  
Ulf Matti ◽  
Markus Mund ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Maximum Likelihood ◽  
Single Molecule ◽  
Model Fitting ◽  
Super Resolution ◽  
Likelihood Estimation ◽  
Nuclear Pore ◽  
Analysis Workflow ◽  
Likelihood Model ◽  
Protein Density

Quantitative analysis is an important part of any single-molecule localization microscopy (SMLM) data analysis workflow to extract biological insights from the coordinates of the single fluorophores, but current approaches are restricted to simple geometries or do not work on heterogenous structures. Here, we present LocMoFit (Localization Model Fit), an open-source framework to fit an arbitrary model directly to the localization coordinates in SMLM data. Using maximum likelihood estimation, this tool extracts the most likely parameters for a given model that best describe the data, and can select the most likely model from alternative models. We demonstrate the versatility of LocMoFit by measuring precise dimensions of the nuclear pore complex and microtubules. We also use LocMoFit to assemble static and dynamic multi-color protein density maps from thousands of snapshots. In case an underlying geometry cannot be postulated, LocMoFit can perform single-particle averaging of super-resolution structures without any assumption about geometry or symmetry. We provide extensive simulation and visualization routines to validate the robustness of LocMoFit and tutorials based on example data to enable any user to increase the information content they can extract from their SMLM data.

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