Analysis of predicted interaction networks in context of expression correlation in various conditions reveals the dynamic changes associated with a number of biological processes. Full-size image (31K) View Within Article Figure 1 The performance of machine learning methods on gold standard data using ...
we reconstructed protein–protein interaction (PPI) networks centered around pigmentation gene products (‘pigmentation proteins’) and supplemented the PPI networks with protein expression information obtained by mass spectrometry in a panel of melanoma cell lines (both pigment producing and non-pigment pr...
In the current study, we examined HLHS within the mechanistic framework of the protein–protein interaction (PPI) network or protein ‘interactome’. Proteins fuel the cellular machinery, and their interactions reflect the functions that they subserve. This can be informative of disease mechanisms and...
(4) Combining with high-throughput omics technology: by integrating large-scale protein interaction data and expression data, a more accurate and complete gene regulatory network model is constructed. (5) Utilizing molecular-docking and machine-learning methodologies for the prediction of PPI networks ...
Durrant and McCammon proposed two models using neural networks, NNScore 1.0 and NNScore 2.0 [107,108]. NNScore 1.0 employed a simple neural network composed of only one hidden layer with five neurons to classify the active and inactive compounds based on 194 features including both interaction ...
2.3. Determination of Candidate Interaction Partners of MAPK14 in Living HL-60 Cells by Means of smarTCPA As we showed that similar dose–responses are informative to differentiate between primary and secondary binding effects (here: PPI), we were curious to reveal if our findings could be trans...
effective and practical way of altering plant-based proteins. Keywords: casein proteins;lentil proteins;structural interaction;protein quality;solubility;water kefir-assisted fermentation