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The scale has demonstrated reliability and validity, including prediction of functioning and health care utilization (14).
This type of analysis, used by Cassidy et al. [ 30], allows to evaluate whether RSWGcr alone or PANSS-PNScr continue to contribute to the prediction of functioning and cognitive status when PANSS-TScr remission is taken into account.
Overall, these results confirm that the best prediction of functioning and, at least in part, of cognition, is achieved using remission criteria based on the use of all items of PANSS, followed by criteria based on the use of positive and negative items of the same scale.
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The most reliable prediction-of-function approaches for genomically sequenced protein orfs are obtained using the more computationally intensive HMM modelling and scoring utilities.
The Gene Ontology database (GO, http://www.geneontology.org/), Blast and Interproscan (http://www.ebi.ac.uk/Tools/pfa/iprscan/) were used for prediction of functions of predicted targets.
While there have been great advances in predicting protein structure from primary sequence (see later), the prediction of function from structure (let alone sequence) remains an important (if largely unattained) aim.
With the present modality, the prediction of function via DTI seems promising.
Using the "guilt by association approach", the identification of SMBDs allowed prediction of functions and mode of regulation for a variety of previously uncharacterized proteins.
However, for a substantial number of proteins, and subsequently enzymes, their functions are not known, therefore an important challenge in structural genomics is the prediction of function of these uncharacterized proteins [16].
Whilst prediction of function based on sequence homology can be misleading, a study of the domain architecture of RING and RING-like E3 ligases may highlight additional features.
We show that approaches to remedy these problems have other effects on prediction of function which can be understood by how the methods interact with multifunctionality.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com