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Pairwise interaction data were represented as binary presence/absence values; where applicable, interaction profile similarities were calculated between genes from binary data using an inner product.
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In this way, we developed a binary feature for 'interacts with' between genes and binary features for any phrase occurring between the genes in the training data.
The MDR method is a non-parametric and genetic model-free approach that efficiently identifies higher-order interactions between genes and/or gene environmental factors with binary phenotype.
We used logistic regression modeling in this work as the response variables, which represent association between diseases and genes, are binary.
The EDGAR corpus contains annotations for genes, drugs and cells, including binary relationships between genes and drugs, genes and cells, and drugs and cells.
The MDR method is a computationally efficient method for detecting higher-order interactions between genes (and/or gene-environmental factors) and a binary phenotype.
The constructs used for transient gene expression and localization studies were derived from binary vector pK7FWG2, which allows C-terminal fusion of protein of choice with GFP [43].
Prior to differential gene expression analysis, read count tables were generated from binary sequence alignment/map (BAM) files using HTseq software (http://pypi.python.org/pypi/HTSeq).org/pypi/HTSeq
This EMT Spectrum will serve as a very informative tool, given that most of the current knowledge about EMT derives from binary separation between the epithelial and mesenchymal phenotypes.
Given the boolean gene-protein-reaction associations, the COBRA Toolbox automatically produces a binary matrix G describing the associations between genes and reactions.
An image is then produced by representing eight binary values (obtained from binary coefficients) as the integer value between 0 to 255.
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