CN110993037A - Protein activity prediction device based on multi-view classification model - Google Patents
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Abstract
The invention discloses a protein activity prediction device based on a multi-view classification model, which comprises a computer memory, a computer processor and a computer program, wherein the computer memory is stored with a trained multi-view classification model, and the multi-view classification model comprises a pooling unit, a graph convolution unit and a full connection unit; the computer processor when executing the computer program performs the steps of: constructing an original protein graph representing a protein structure by taking atoms of the protein structure as nodes and chemical bonds between the atoms as connecting edges; performing pooling operation on the original protein map by using a pooling unit to obtain a plurality of views with different dimensions; performing graph convolution operation on the multiple views and the original protein graph by using a trained graph convolution unit to obtain multiple feature vectors; and cascading the plurality of feature vectors to obtain a fusion feature vector, performing full-connection operation on the fusion feature vector by using the trained full-connection unit, and outputting a protein activity prediction result.
Description
Technical Field
The invention belongs to the field of bioinformatics, and particularly relates to a protein activity prediction device based on a multi-view classification model.
Background
A graph consists of a series of nodes and con-vertions. Graph analysis is applied to research directions such as node classification, link prediction, clustering, and graph classification as a kind of non-euclidean data. Graph neural networks (Graph neural networks) are a deep learning method based on Graph domain analysis, and rely on information transfer between nodes in a Graph to capture dependency relationships in the Graph, so as to obtain characteristics of each node. The node features it generates can be used as input to any differentiable prediction layer to train the entire model in an end-to-end fashion.
Some of the studies on GNN focus on feature Learning at the node level (see document 1: Hamilton W L, YingR, Leskovec J. inductive replication Learning on Large Graphs [ J ]. 2017.; i.e., generalized Representation Learning of Large Networks), while others focus on feature Learning at the Graph level (see document 2: Henaff M, Bruna J, Lecun Y. deep computational Networks on Graph-Structured Data [ J ]. Computer science, 2015.; i.e., deep convolution Networks of Graph structure Data). The method for learning the characteristics of the concerned nodes brings improvement to tasks such as node classification and link prediction, and the method for learning the characteristics of the graph mainly promotes graph classification and the like. The task of graph classification is to predict the labels of a given graph using the relevant features of the nodes and the topology of the graph.
When applying GNN to graph classification, the standard approach is to generate features for all nodes in the graph and then to globally assemble all these node features together, such as David Duvenaud's implementation to input molecular structures of arbitrary size and shape into a deep neural network to get a molecular fingerprint vector of fixed dimensions (see document 3: Duvenaud D, Maclaurin D, agiuilera-iparaguerre J. relational network on Graphs for learning molecular Fingerprints 2015.) these methods treat all nodes equally when using node features. In other words, the structural information of the entire graph is completely ignored in this process, and by default, the importance of each node in the graph is the same. But in practice different nodes have different degrees of importance in the graph, and they should contribute differently to the graph characteristics.
Disclosure of Invention
The invention aims to provide a protein activity prediction device based on a multi-view classification model, and the compound function prediction device can realize the prediction of the functions of compounds based on the multi-view classification model.
In order to achieve the above object, the present invention provides a compound function prediction device based on a multi-view classification model, comprising a computer memory, a computer processor and a computer program stored in the computer memory and executable on the computer processor, wherein the computer memory stores a trained multi-view classification model, and the multi-view classification model comprises a pooling unit, a graph volume unit and a full connection unit;
the computer processor, when executing the computer program, performs the steps of:
constructing an original protein graph representing a protein structure by taking atoms of the protein structure as nodes and chemical bonds between the atoms as connecting edges;
performing pooling operation on the original protein map by using a pooling unit to obtain a plurality of views with different dimensions;
performing graph convolution operation on the multiple views and the original protein graph by using a trained graph convolution unit to obtain multiple feature vectors;
and cascading the plurality of feature vectors to obtain a fusion feature vector, performing full-connection operation on the fusion feature vector by using the trained full-connection unit, and outputting a protein activity prediction result.
The invention has the technical effects that:
the invention skillfully converts the protein structure into the graph structure, realizes the prediction of the protein activity by extracting and classifying the graph structure characteristics, mainly utilizes the graph convolution graph network to learn to obtain the node characteristics containing the attribute characteristics and the topological structure, classifies and aggregates part of nodes into a cluster according to the node characteristics, performs pooling dimensionality reduction on the protein graph, then performs weighted summation on the node characteristics of each view, converts the node characteristics into the graph characteristics and performs characteristic fusion, and finally outputs the class mark of the predicted graph through full connection, namely the prediction of the protein activity. The multi-view classification model not only considers the spatial structure and node attribute characteristics of the initial graph, but also considers the influence of the deeper graph structure obtained from the initial graph on the prediction result, so that the accuracy of protein activity prediction is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of protein activity prediction based on a multi-view classification model provided by the examples;
fig. 2 is an illustrative diagram of a new node connectivity representation.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the detailed description and specific examples, while indicating the scope of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
In bioinformatics, compounds are formed by connecting atoms through chemical bonds, and can be converted into graph networks by combining the existing graph networks, and some properties of the compounds are researched by utilizing the idea of processing the graph networks.
The embodiment provides a protein activity prediction device based on a multi-view classification model, which comprises a computer memory, a computer processor and a computer program stored in the computer memory and executable on the computer processor, wherein the computer memory stores a trained multi-view classification model, and the multi-view classification model comprises a pooling unit, a graph volume unit and a full connection unit.
The graph convolution unit mainly comprises a plurality of convolution layers and is mainly used for extracting features of an input graph, learning a local topological structure and self attributes of each node on the graph, obtaining feature vectors with the same dimensionality and storing the feature vectors in a feature matrix form;
the pooling unit is used for classifying and aggregating nodes with similar characteristics and attributes in the initial graph to form new nodes, then acquiring a linkage relation between the new nodes according to the mapping between the nodes of the initial graph and the new nodes, reducing the number of the nodes and the linkage edges of the obtained new graph to a certain extent, realizing low-dimensional representation of the initial graph, and making the constructed new graph capable of serving as the initial graph to complement representation forms and characteristics under different views, so that the partial characteristics are fully utilized to help the graph classification task to a certain extent;
the input of the full-connection module unit is connected with the output of each graph convolution module, dimension normalization and feature fusion are carried out on the initial graph and the graph features of the multiple views obtained after node aggregation, feature representation containing multiple view information is obtained, and accurate classification of the initial graph is achieved.
Before the multi-view classification model is applied, training samples are required to be adopted for training the multi-view classification model, and when the size and weight of the neighborhood in a good-image convolution unit, the number of view layers in a pooling unit and the hyper-parameters in a full-connection unit are determined, the classification result can be more accurate.
The computer program when executed by a computer processor implements the steps of:
s101, an original protein map showing the protein structure is constructed by using atoms of the protein structure as nodes and chemical bonds between the atoms as connecting edges.
The original protein map G is represented by (a, H), where a represents the adjacency matrix and H represents the node characteristics.
S102, performing pooling operation on the original protein map by using a pooling unit to obtain a plurality of views with different dimensions.
Specifically, pooling the original protein map to obtain multiple views of different dimensions includes:
multiplying the node characteristics H by the weight W to obtain an initial distribution matrix S of the nodes, and normalizing each column in the initial distribution matrix S by using a softmax function to obtain a pooling distribution matrix S', wherein the specific process is as follows:
S＝HW
wherein s isijTo assign an element, s, in the matrixijRepresenting the probability that the node i in the previous layer view is aggregated into the node j in the next layer view after being subjected to pooling;
calculating an adjacent matrix A 'and a node characteristic H' of the next layer view according to the adjacent matrix A and the node characteristic H corresponding to the adjacent matrix A and the node characteristic H before aggregation and the pooling distribution matrix S ', wherein the node characteristic H' represents the self attribute of the node in the next layer view:
A′＝(S′)TAS′
H′＝(S′)TH
and the dimension of the adjacency matrix A 'and the node characteristic H' is smaller than that of the adjacency matrix A and the node characteristic H of the previous layer view.
S103, carrying out graph convolution operation on the multiple views and the original protein graph by using the trained graph convolution unit to obtain multiple feature vectors.
Specifically, the obtaining a plurality of feature vectors by performing a graph convolution operation on the plurality of views and the original protein graph by using a trained graph convolution unit includes:
updating the node characteristics of the input graph by using the following formula, wherein the updated node characteristics are the characteristic vectors:
H″＝ReLU(LHW)
wherein L represents a Laplace matrix L ═ I-D-1/2AD-1/2D is the value matrix of the input adjacent matrix A, H is the input node feature, I is an identity matrix, H' represents the node feature obtained after updating, and W is the value matrix in the graph convolution unitAnd (4) weighting the parameters.
In the embodiment, through k times of graph convolution operations, it can also be understood that node features are obtained through k cascaded graph convolution modules, and each time the graph convolution operation is performed, a neighborhood range captured by the node features is expanded by one node.
And S104, cascading the plurality of feature vectors to obtain a fusion feature vector, performing full-connection operation on the fusion feature vector by using the trained full-connection unit, and outputting a protein activity prediction result.
Specifically, a plurality of feature vectors corresponding to a plurality of views are superimposed by using the following formula to obtain a fusion feature vector H*：
H*＝concat(W″iH″i,i＝0,1,2,3，…，n-1，n)
Where concat (. cndot.) represents a cascade function, Wi"represents the weight parameter corresponding to the ith view, and H" represents the node characteristic corresponding to the ith view; the sequence number 0 indicates the original protein map.
After obtaining the fusion feature vector H*On the basis, carrying out full-connection operation on the fusion characteristic vector according to the following formula to obtain a protein activity prediction result:
C＝soft max(H*W*)
where C represents the matrix of prediction outputs, each element in C represents the probability of whether the function of the protein map is active, W*Representing the weight of the parameter in the fully connected unit.
In protein maps, atoms (nodes) are linked by bonds (edges), and single atoms or chemical bonds have little effect on the activity of the entire protein, but some local structures consisting of groups of atoms and their chemical bonds may represent specific functional units that are critical to the function of the protein, and are then important in characterizing the extracted map. In order to highlight the importance of these functional units, the present embodiment aggregates partial nodes that may have some functions into a new node through a hierarchical pooling process, and then the obtained multi-view with fewer nodes and connecting edges is composed of some functional units, and features extracted on the basis better reflect the functions of proteins. Specifically, the classification of the presence or absence of the activity of the protein may be reflected, including the functions of catalysis, carcinogenicity, and the like.
The multi-view classification model not only considers the topological structure and node attributes of the initial graph, but also considers the structural characteristics of different views obtained by aggregation of partial nodes, and supplements the influence of a deeper graph structure on a prediction result, so that the prediction of protein activity can be better realized.
The above-mentioned embodiments are intended to illustrate the technical solutions and advantages of the present invention, and it should be understood that the above-mentioned embodiments are only the most preferred embodiments of the present invention, and are not intended to limit the present invention, and any modifications, additions, equivalents, etc. made within the scope of the principles of the present invention should be included in the scope of the present invention.
Claims (6)
1. A protein activity prediction apparatus based on a multi-view classification model, comprising a computer memory, a computer processor, and a computer program stored in the computer memory and executable on the computer processor, wherein the computer memory has stored therein a trained multi-view classification model comprising a pooling unit, a volume unit, and a full-connected unit;
the computer processor, when executing the computer program, performs the steps of:
constructing an original protein graph representing a protein structure by taking atoms of the protein structure as nodes and chemical bonds between the atoms as connecting edges;
performing pooling operation on the original protein map by using a pooling unit to obtain a plurality of views with different dimensions;
performing graph convolution operation on the multiple views and the original protein graph by using a trained graph convolution unit to obtain multiple feature vectors;
and cascading the plurality of feature vectors to obtain a fusion feature vector, performing full-connection operation on the fusion feature vector by using the trained full-connection unit, and outputting a protein activity prediction result.
2. The multi-view classification model-based protein activity prediction apparatus of claim 1, wherein the original protein graph G is represented by (a, H), where a represents the adjacency matrix and H represents the node characteristics.
3. The multi-view classification model-based protein activity prediction apparatus of claim 2, wherein the pooling of the original protein map to obtain multiple views of different dimensions comprises:
multiplying the node characteristics H by the weight W to obtain an initial distribution matrix S of the nodes, and normalizing each column in the initial distribution matrix S by using a softmax function to obtain a pooling distribution matrix S', wherein the specific process is as follows:
S＝HW
wherein s isijTo assign an element, s, in the matrixijRepresenting the probability that the node i in the previous layer view is aggregated into the node j in the next layer view after being subjected to pooling;
calculating an adjacent matrix A 'and a node characteristic H' of the next layer view according to the adjacent matrix A and the node characteristic H corresponding to the adjacent matrix A and the node characteristic H before aggregation and the pooling distribution matrix S ', wherein the node characteristic H' represents the self attribute of the node in the next layer view:
A′＝(S′)TAS′
H′＝(S′)TH
and the dimension of the adjacency matrix A 'and the node characteristic H' is smaller than that of the adjacency matrix A and the node characteristic H of the previous layer view.
4. The multi-view classification model-based protein activity prediction apparatus of claim 1, wherein the performing a graph convolution operation on the plurality of views and the original protein graph respectively by using the trained graph convolution unit to obtain a plurality of feature vectors comprises:
updating the node characteristics of the input graph by using the following formula, wherein the updated node characteristics are the characteristic vectors:
H″＝ReLU(LHW)
wherein L represents a Laplace matrix L ═ I-D-1/2AD-1/2D is a value matrix of the input adjacent matrix A, H is the input node characteristic, I is an identity matrix, H' represents the node characteristic obtained after updating, and W is the parameter weight in the graph convolution unit.
5. The multi-view classification model-based protein activity prediction device of claim 1, wherein the fusion feature vector H is obtained by superposing a plurality of feature vectors corresponding to a plurality of views according to the following formula*：
H*＝concat(Wi″H″i,i＝0,1,2,3，…，n-1，n)
Where concat (. cndot.) represents a cascade function, Wi"represents the weight parameter corresponding to the ith view, and H" represents the node characteristic corresponding to the ith view; the sequence number 0 indicates the original protein map.
6. The multi-view classification model-based protein activity prediction apparatus of claim 1, wherein the fusion feature vectors are subjected to full-join operation according to the following formula to obtain a protein activity prediction result:
C＝soft max(H*W*)
where C represents the matrix of prediction outputs, each element in C represents the probability of whether the function of the protein map is active, W*Representing the weight of the parameter in the fully connected unit.
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