Our classifier exploits features of the binding partners predicted from amino acid sequence, their functional similarity, and network topology. We find that the ...
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Protein interactions mediate a wide spectrum of functions in various cellular contexts. Functional versatility of protein complexes is due to a broad range ...
Sep 2, 2010 · Just starting from sequences (and no IDs) it will thus allow you get a predicted interaction network. However, the underlying evidence is based ...
Protein interactions mediate a wide spectrum of functions in various cellular contexts. Functional versatility of protein complexes is due to a broad range ...
In this paper, we develop the EResCNN, an effective predictor to predict PPIs based on ensemble residual convolutional neural network.
In this work, we integrate deep learning with feature fusion, harnessing the strengths of both approaches, handcrafted features, and protein sequence embedding.
Jun 26, 2017 · This tool is capable of predicting PPIs for any target protein pair only using their primary sequences, and assigning an interaction probability to each SVM ...
We propose a method for PPI prediction using only the information of protein sequences. This method was developed based on a learning algorithm-support vector ...
Jul 5, 2019 · Sequence-based protein–protein interaction (PPI) prediction represents a fundamental computational biology problem. To address this problem, ...
The DeepFE-PPI [13] method was proposed to predict PPI using a deep neural network (DNN) based on a new protein sequence representation method called Res2vec.