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Motivation: Prediction of synergistic effects of drug combinations has traditionally been relied on phenotypic response data.
Motivation: Prediction of protein function from protein interaction networks has received attention in the post-genomic era.
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Dopamine may be more critical in motivation, anticipation of rewards and prediction error signalling than in consummatory processing, which has been linked to opioid receptor activation [ 7, 36].
This analysis will reveal that dual-systems views are in need of fundamental re-thinking, and its elements will be amalgamated with current views on action-oriented predictive processing into a novel integrative theoretical framework (IMPPACT: Impetus, Motivation, and Prediction in Perception Action Coordination theory).
Motivation: Promoter prediction is an important task in genome annotation projects, and during the past years many new promoter prediction programs (PPPs) have emerged.
Motivation: The prediction of RNA 3D structures from its sequence only is a milestone to RNA function analysis and prediction.
With this motivation, the prediction error of the AR (alternatively the linear predication) model was used in each time window as another feature for detecting the P-waves.
Exergames may be one viable way to increase child physical activity, but investigation of long term motivation, and prediction of adherence has seen little research attention.
Motivation: The prediction and annotation of the genomic regions involved in gene expression has been largely explored.
Motivation: The prediction of receptor ligand pairings is an important area of research as intercellular communications are mediated by the successful interaction of these key proteins.
Motivation: The prediction of biologically active compounds is of great importance for high-throughput screening (HTS) approaches in drug discovery and chemical genomics.
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