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Jansen et al. derived negative protein pairs by selecting PPI pairs in different subcellular compartments based on localization attributes, including nucleus, mitochondria, cytoplasm, membrane, and secretory pathway [34, 39].
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We think that this strategy of deriving negative datasets is more sophisticated than Jansen et al.'s [34] approach.
Four experimental conditions (approximately 100 trials each) were derived: explicit negative, explicit positive, assigned negative and assigned positive.
This implies that there is a lineage relationship between spinal cord derived LeX negative and LeX positive NSCs such that LeX negative NSCs give rise to LeX positive NSCs.
We also analytically derived the negative refraction correctness condition that gives the angular region where negative refraction occurs.
Since the quality of training data determines the reliability and robustness of classifiers, we carefully derived a negative dataset based on co-localization enrichment measure.
Irwin ([1941]), followed later by Arbous and Kerrich ([1951]), derived the negative binomial distribution on the hypothesis solving the associated Kolmogorov forward differential equations by a method due to McKendrick ([1925]).
These scores are derived from negative logarithm of the P indicative of the likelihood that focus genes found together in a network due to random chance.
Luciferase activity was measured using single-tube luminometers with injectors based on standard method, normalized to background levels of luciferase activity derived from negative control cells.
Prescription rate ratios (RRs) for potentially addictive drugs derived from negative binomial models, adjusted for age, education, paid employment, marital status, chronic musculoskeletal pain, mental distress and sleep problems.
In an effort to determine how to group PANSS items in meaningful ways beyond the conceptually derived Positive, Negative, and General Psychopathology subscales, several rigorous factor analytic approaches have been applied to large PANSS datasets.
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