Aria Dolatabadian's repositories
Orthogonal-design
In experimental design, an orthogonal design refers to a design where the factors or variables being investigated are independent or orthogonal to each other. In other words, the levels of one factor do not systematically influence or interact with the levels of another factor.
Experimental-designs
Experimental designs in agriculture play a crucial role in conducting scientific studies to evaluate the effects of different treatments or interventions on agricultural systems. Here are a few commonly used experimental designs in agriculture.
Seq-Align-Chart
Sequence alignment is a way of arranging DNA (or protein) sequences to identify regions of similarity that may be a consequence of evolutionary relationships between the sequences.
Contigs-L50
In the context of genomics, L50 is a metric used to assess the quality of an assembly or sequencing data. It is a statistical measure that represents the minimum number of contigs or reads required to cover at least 50% of the total assembly or sequencing length.
Contigs-N50
In the context of contigs and sequencing, N50 is a metric used to assess the quality of an assembly or sequencing data. It is a statistical measure that represents the contig or read length at which at least 50% of the total assembly length is contained in contigs or reads of that length or longer.
Multitrait-Genotype-Ideotype-Distance-Index
MGIDI is used to select genotypes in plant breeding programs based on multiple traits.
NanoR-R-package-to-analyse-visualise-and-compare-ONT-data
NanoR is a package for the statistical language and environment R for fast analysis/visualisation and comparison of basecalled MinION/GridION sequencing data.
Logistic-Regression
A regression model used when the dependent variable is binary or categorical, predicting the probability of an event occurring.
Polynomial-Regression
A regression model that allows for non-linear relationships by including polynomial terms of the independent variables.
Elastic-Net-Regression
A regression model that combines the Ridge and Lasso regularization techniques, providing a balance between them.
Support-Vector-Regression
A regression model that uses support vector machines to find a hyperplane that best fits the data.
Decision-Tree-Regression
A regression model that uses a decision tree structure to make predictions based on feature values.
Random-Forest-Regression
A regression model that combines multiple decision trees to improve prediction accuracy and handle complex relationships.