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Predicting function

It should be possible to extend the DNA microarray-binding experiment to whole-genome analysis of transcription factor binding sites. The authors suggest that a microarray spotted with 12,000 one-kilobase sequences would span the entire Saccharomyces cerevisiae genome (Bulyk et al., 2001). Such an array could be used to characterize the sequence specificity of S. cerevisiae transcription factors. These experiments would be useful for predicting functions of previously uncharacterized transcription... [Pg.100]

Bork, P., Dandekar, T., Diaz-Lazcoz, Y., Eisenhaber, F., Huynen, M. and Yuan, Y. (1998), Predicting function From genes to genomes and back , J. Mol. Biol., 283, 707-725. [Pg.64]

Profiling of the transcriptome and proteome has received some criticism in its ability to predict function. The transcriptome has a singular purpose, which is to transport a message for a protein that is to be translated however, increases in the... [Pg.35]

In addition to this classification of cellular function by domain cooccurrence, analyses of domain combinations can also be used to improve the prediction of a protein s function. The RhoGEF domain, for example, is invariably found N-terminally to a PH domain. The cooccurrence of these two domains appears to be correlated with altered electrostatic potential, thereby resulting in prevention of the PH domain from binding phospholipids (Blomberg et al., 1999). As this is a frequent function of the PH domain, the determination of a protein s domain architecture can assist in discounting a specific predicted function. [Pg.236]

Bork, P., andKoonin, E. V. (1998). Predicting functions from protein sequences—where are the bottlenecks Nat. Genet. 18, 313—318. [Pg.271]

Predicting Functional Responses In Vivo from In Vitro Data. 112... [Pg.103]

Since the first concept of viability, FDG imaging by PET has been the gold standard for myocardial viability assessment due to its proven value in predicting functional outcomes after revascularization [61, 64, 89, 90] and in risk assessment for those patients with viable myocardium who are treated conservatively [76,82,91]. This leading role for PET in assessing viability has continued into current literature with further advances in PET imaging in... [Pg.27]

Mathematically, the model is a function that predicts the learned property from the descriptor vector. Building the model means finding the optimal prediction function... [Pg.29]

TABLE 16.4. Computational approaches used to predict functions of unknown proteins... [Pg.437]

Considering the limitations presented thus far that are inherent in interpreting data reported in the literature, perhaps the pattern of response rather than the individual measurements is of importance in predicting functionality. Information will be presented that relates water absorption to other functional properties and examines the effect of the physical and chemical environment on the water absorption response patterns of various protein ingredients. This presentation will be brief and with limited explanation. Original references may be consulted by those desiring greater depth. [Pg.186]

Barnett, M.J. Fisher, R.F. Jones, T. et al. Nucleotide sequence and predicted functions of the entire Sinorhizobium meliloti pSymA megaplasmid. Proc. Natl. Acad. Sci. USA, 98, 9883-9888 (2001)... [Pg.461]


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Binding modes, scoring function prediction

Density functional theory interaction prediction

Function prediction method

Functional prediction

Functional prediction

Gene and Function Prediction by Conservation of Genomic Context

Homology modeling sequence-structure-function prediction

Inferences for Predicted Functions

Knowledge-based prediction scoring functions

Kohn-Sham Density Functional Theory Predicting and Understanding Chemistry

Learning for Protein Structure and Function Prediction

Ligand identification sequence-structure-function prediction

Ligand-based approach predicting functional sites

Model acceptance for transfer-function-based technique predictability

Models for predicting functions

Modulation Transfer Function prediction

Pair interactions sequence-structure-function prediction

Predictable case wave functions

Prediction function zeroing

Prediction of Higher Order Function

Prediction techniques biochemical function

Prediction techniques sequence-structure-function

Prediction tubular function

Predictions by density functional

Predictive cost function

Protein function predictions

Protein-based approach predicting functional sites

Scoring functions prediction

The Challenge of Affinity Prediction Scoring Functions for Structure-Based Virtual Screening

The prediction results with Kyte-Doolittle preference functions

Unknown proteins, predicting functions

Validation of the predicting function

Variables and predicting functions

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