Hanxi Zhu

Prediction of protein secondary structure by multi-modal neural networks

We developed a multi-modal feed-forward neural network to predict the secondary structure of proteins. Several neural networks are used together and the final prediction results are decided by majority rule. We used 6137 residues to train and test the method. The average accuracy of the prediction is 66%, which is about 6.9% higher than single neural network.

A multimodal neural network with single-state predictions for protein secondary structure

Prediction of protein secondary structure is considered to be an important step toward elucidating the three-dimensional structure and function of proteins. We have developed a multimodal neural network (MNN) to predict protein secondary structure. The MNN is composed of several subclassifiers for single-state predictions using neural networks and a decision neural network (DNN). Each subclassifier employs a number of subnetworks to predict the single-state of the secondary structure individually and produces the final results by majority decision. The DNN uses a three-layer neural network to produce the final overall prediction from the outputs of the single-state predictions. The MNN gives an overall accuracy of 71.1% with corresponding Matthews correlation coefficients of CH = 0.62 and CE = 0.53. The prediction test is based on a database of 126 nonhomologous protein sequences.

Simulations of construction learning for neuron-computer resources

It is well known that information processing in the brain depends on neuron systems. Simple neuron systems are neural networks, and their learning methods have been studied. However, we believe that research on large-scale neural network systems is still incomplete. Here, we propose a learning method for millions of neurons as resources for a neuron computer. The method is a type of recurrent path-selection, so the neural network objective must have nesting structures. This method is executed at high speed. When information processing is executed by analogue signals, the accumulation of errors is a grave problem. We equipped a neural network with a digitizer and AD/DA (Analogue Digital) converters constructed of neurons. They retain all information signals and guarantee precision in complex operations. By using these techniques, we generated an image shifter constructed of 8.6 million neurons. We believe that there is the potential to design a neuron computer using this scheme.