阅读说明：本技术 融合多种拓扑信息的生物网络比对方法 (Biological network comparison method fusing multiple topology information ) 是由 陈璟 刘晓 于 2020-05-25 设计创作，主要内容包括：本发明公开了一种融合多种拓扑信息的生物网络比对方法。本发明一种融合多种拓扑信息的生物网络比对方法,包括：步骤1读取网络及其序列相似性得分,分别计算两个网络的相关值矩阵,并对网络进行模块划分,同一模块内的结点具有较高的相似性；步骤2计算模块内结点的相似性得分,并对模块进行两两结点比对；步骤3计算模块间的相似性得分,并对模块进行比对；步骤4将步骤2,3中得到的结点映射关系进行整合,筛选,得到1对1的结点映射关系。本发明的有益效果：(1)从网络结构、度、结点邻居、特征向量中心性、局部边保守等方面充分挖掘了结点的拓扑相似性,提高了比对的拓扑质量。(The invention discloses a biological network comparison method fusing various topological information. The invention discloses a biological network comparison method fusing multiple topological information, which comprises the following steps: step 1, reading networks and sequence similarity scores thereof, respectively calculating correlation value matrixes of the two networks, and carrying out module division on the networks, wherein nodes in the same module have higher similarity; step 2, calculating similarity scores of nodes in the modules, and comparing every two nodes of the modules; step 3, calculating similarity scores among the modules and comparing the modules; and 4, integrating and screening the node mapping relations obtained in the steps 2 and 3 to obtain the node mapping relation of 1 to 1. The invention has the beneficial effects that: (1) the topological similarity of the nodes is fully mined from the aspects of network structure, degree, node neighborhood, feature vector centrality, local edge conservation and the like, and the comparison topological quality is improved.)

1. A biological network comparison method fusing multiple topological information is characterized by comprising the following steps:

step 1, reading networks and sequence similarity scores thereof, respectively calculating correlation value matrixes of the two networks, and carrying out module division on the networks, wherein nodes in the same module have higher similarity;

step 2, calculating similarity scores of nodes in the modules, and comparing every two nodes of the modules;

step 3, calculating similarity scores among the modules and comparing the modules;

step 4, integrating and screening the node mapping relations obtained in the steps 2 and 3 to obtain a node mapping relation of 1 to 1;

and 5, deleting the compared nodes, repeating the steps 2-5, comparing all the nodes in the small network, or keeping the similarity score between the modules to be 0, and stopping the algorithm.

2. The method for comparing biological networks fusing multiple topological information according to claim 1, wherein the module division is as follows:

module partitioning is done for a single network;

firstly, calculating a correlation value matrix of the network, wherein the matrix gives similarity relation among nodes, and the invention gives four definitions of the relation among the nodes, namely strong correlation, weak correlation and irrelevance;

if an edge is connected between two nodes, the pair of nodes is called as strongly related;

if the nodes do not have edges which are directly connected but can be indirectly connected through other nodes, weak correlation is called;

nodes that correspond to strong and weak correlations are also called correlations;

the nodes without correlation relation are all called as irrelevant;

the correlation value calculation formula is as follows:

wherein, theta is a strong correlation node set, phi is a weak correlation node set,a set of unrelated nodes; max {1, | Φ^*|_MDenotes the maximum value of the number of intermediate edges passed in all relevant nodes,refers to the number of intermediate edges passed by from node u to v; the formula (1) is a normalized correlation value, and the larger the value is, the higher the similarity between nodes is;

then, according to the correlation value matrix, respectively carrying out modularization division on G1 and G2 to obtain a module set C_G1，C_G2(ii) a The detailed steps are as follows:

a) constructing a correlation value matrix psi for all node pairs in the network G ═ (V, E);

b) for theInitializing | V | pieces respectivelyModule being module-centric, note

c) ModuleThe construction method comprises the following steps: obtaining other nodes andthe correlation values are arranged in descending order, and the nodes with the correlation values of the top 25 percent are selected and added into the moduleOther modules are constructed in a similar way to finally obtain

3. The method for comparing biological networks fusing multiple topological information according to claim 1, wherein the intra-module node comparison is as follows:

modularizing the networks G1 and G2 to obtain two module sets C1 and C2 respectively; comparing each module in C1 with each module in C2 by using a seed expansion method respectively to obtain a comparison result of | C1 |. C2| to the modules, wherein | C1|, | C2| refers to the number of the modules respectively;

the node similarity score function used in the module comparison process is as follows:

b (s, t) is the sequence similarity score of the node (s, t), which is calculated by BLAST + + tool to evaluate the biological similarity between nodes, the larger the value, the higher the node similarity;the topological similarity score between nodes is formed by a topological vector tuple based on the centrality of the feature vectorIs calculated to be in, wherein

1)Representation nodeTo a degree of (i) thatThe number of neighbors of (2);

2)representation nodeThe centrality of the feature vector is used for measuring the centrality position of the node in the network;

3)representation nodeThe centrality of the average feature vector of the neighbor;

thus, the topological similarity score of the node pair (s, t)The specific calculation mode formula is as formula (3); the smaller the value is, the more similar the nodes are;

using seed extension method to combine C_G1Respectively with C_G2Module of (1)The detailed steps of pairwise intra-module alignment are as follows:

a) input module to be compared

b) Firstly, the first step is toComparing;

c) separately acquireThe neighbor of (a) is (b),

d) computingSimilarity of nodal point pairsAnd use the Hungarian algorithm willThe nodes are compared, whereinIs composed ofAndthe cartesian product of (a);

e) will have expanded the nodeRemoving, and aligning the remaining aligned junctionsRepeating the step c) d) for the point pairs in sequence;

f) obtaining the comparison result of the nodes in the module

4. The method for comparing biological networks fusing multiple topological information according to claim 1, wherein the inter-module comparison is as follows:

regarding each module as a node, constructing a complete bipartite graph, wherein the weight of each edge is the similarity score between the modules; then, performing module matching by using a maximum weighted bipartite graph matching algorithm to obtain a comparison result between modules; wherein the inter-module similarity score is calculated as follows:

the number of the aligned node pairs in the alignment result in one module obtained in step 2,is the sum of the sequence similarities of the binding pairs in the alignment.

5. The method for comparing biological networks fusing topology information according to claim 4, wherein,the local edge conservation score between modules is used for measuring the edge conservation of the comparison result, and is specifically calculated as follows:

let e_ijRepresenting the local edge set of module C (j) in network Gi, Ei is the edge set of Gi, V (C (j)) is the node set of module C (j), e_ijIs represented as follows:

e_i，j＝{(s1，s2)|s1，s2∈V(C(j))∧(s1，s2)∈Ei}

5)

for networks G1 ═ (V1, E1), G2 ═ V2, E2,if it is notThen (s, m) and (t, n) are a pair of module conservative edges;

presentation moduleThe conservative edge logic matrix of (2) has each element calculated as follows:

moduleThe local edge conservation score of (a) is calculated as follows:

6. the method of claim 1, wherein the pair C is determined according to the similarity score of the modules_G1，C_G2The detailed procedure for the alignment between modules is as follows:

a) module set C for input networks G1, G2_G1，C_G2；

b) C is to be_G1，C_G2Each module of (a) is respectively regarded as a node, and a complete bipartite graph is constructedThe weight of an edge is a similarity score

c) Using Hungarian algorithm pairsSolving is carried out, and one-to-one module comparison can be obtained

7. The method for comparing biological networks fusing topology information as claimed in claim 1, wherein the existing comparison data is processed as follows:

and constructing a hypergraph by using the existing node mapping relation, wherein the nodes of the hypergraph are compared nodes, the comparison result of each pair of modules is abstracted into a hyper-arc of the hypergraph, and the node mapping relation of 1 to 1 is obtained by using a hypergraph matching algorithm.

8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 7 are implemented when the program is executed by the processor.

9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.

10. A processor, characterized in that the processor is configured to run a program, wherein the program when running performs the method of any of claims 1 to 7.

Technical Field

The invention relates to the field of biological networks, in particular to a biological network comparison method fusing multiple topological information.

Background

In recent years, with the development of biological experiments, a large amount of biological network data is generated, a network comparison is used for analyzing the biological network, the interaction among different species is compared, the evolutionary relationship among the species can be better understood, the conservative functional components are found, and the functional prediction is realized. The invention studies the comparison of two networks, namely a source network and a target network. The node number of the source network is less than that of the target network, and the node functions are known; the function of the protein in the target network is unknown. The biological significance is that the function transfer between the source network and the target network is realized through the paired network comparison, so that the function of the protein in the target network is better mined. The network comparison algorithm consists of a score function and a search algorithm, wherein the score function is used for evaluating the similarity between nodes; the search algorithm searches for the comparison result with the greatest global similarity according to the score function.

The existing search algorithm is mainly divided into a two-step algorithm and a search algorithm based on an objective function, wherein the first step of the two-step algorithm is to calculate the node similarity of two different input networks, so that a node similarity score matrix is obtained. And converting the network comparison problem into a bipartite graph matching problem with the maximum weight according to the similarity score matrix, wherein the node similarity score calculated in the first step is the weight of the bipartite graph. And in the second step of the two-step algorithm, solving the bipartite graph matching problem of the maximum weight by the traditional classical algorithm or greedy algorithm. The strategy of extracting the comparison result in the second step determines the quality of the final comparison. Most of the proposed classical algorithms belong to the two-step algorithm. Such as IsoRank, MI-GRAAL, SPINAL, and HubAlign. The search algorithm based on the target function firstly proposes a target function, and then omits and optimizes the target function by utilizing various heuristic search strategies. Algorithms belonging to this class include SANA, MAGNA, MAGNA + +, and the like. The scoring function is divided into a topological score and a biological score, the existing topological score calculation methods include GDV, Importance and the like, and the sequence score generally adopts a sequence similarity score generated by BLAST + +.

The traditional technology has the following technical problems:

1. the prior art is insufficient in mining node topological information, so that the quality of the finally compared biological functions is high, but the topological quality is poor.

2. In the prior art, biological similarity among nodes is mined through module information, but a modularized method is not properly selected, so that wrong biological similarity scores are generated, and the quality of biological functions of the nodes is reduced.

3. In the prior art, the similarity scores of blast sequences required to be used in modularization are difficult to obtain, most databases only contain the similarity scores of blast sequences among nodes in different networks, and the similarity scores of blast sequences among the nodes in the same network are rarely contained.

4. The prior art has the problems of topological and biological mass imbalance, and the improvement of one mass usually means the reduction of the other mass.

Disclosure of Invention

The invention aims to provide a biological network comparison method fusing various topological information, and (1) the topological similarity between nodes is fully mined to improve the topological quality of comparison. (2) And (4) determining a modularization method to ensure that the quality of the compared biological function is not reduced. (3) The improvement of universality solves the problem that the similarity score of blast sequences among nodes in the same network is lack. (4) Solving the problem of unbalance between topological quality and biological quality so as to improve one quality index without reducing the other quality index.

In order to solve the technical problem, the invention provides a biological network comparison method fusing multiple kinds of topological information, which comprises the following steps:

step 1, reading networks and sequence similarity scores thereof, respectively calculating correlation value matrixes of the two networks, and carrying out module division on the networks, wherein nodes in the same module have higher similarity;

step 2, calculating similarity scores of nodes in the modules, and comparing every two nodes of the modules;

step 3, calculating similarity scores among the modules and comparing the modules;

step 4, integrating and screening the node mapping relations obtained in the steps 2 and 3 to obtain a node mapping relation of 1 to 1;

and 5, deleting the compared nodes, repeating the steps 2-5, comparing all the nodes in the small network, or keeping the similarity score between the modules to be 0, and stopping the algorithm.

In one embodiment, the module division is specifically as follows:

module partitioning is done for a single network;

firstly, calculating a correlation value matrix of the network, wherein the matrix gives similarity relation among nodes, and the invention gives four definitions of the relation among the nodes, namely strong correlation, weak correlation and irrelevance;

if an edge is connected between two nodes, the pair of nodes is called as strongly related;

if the nodes do not have edges which are directly connected but can be indirectly connected through other nodes, weak correlation is called;

nodes that correspond to strong and weak correlations are also called correlations;

the nodes without correlation relation are all called as irrelevant;

the correlation value calculation formula is as follows:

wherein, theta is a strong correlation node set, phi is a weak correlation node set,a set of unrelated nodes; max {1, | Φ^*|_MDenotes the maximum value of the number of passed intermediate edges in all relevant nodes, | Φ^* _(u，v)I refers to the number of intermediate edges passed from node u to v; the formula (1) is a normalized correlation value, and the larger the value is, the higher the similarity between nodes is;

then, according to the correlation value matrix, respectively carrying out modularization division on G1 and G2 to obtain a module set C_G1，C_G2(ii) a The detailed steps are as follows:

a) constructing a correlation value matrix psi for all node pairs in the network G ═ (V, E);

b) for theInitializing | V | pieces respectivelyModule being module-centric, note

c) ModuleThe construction method comprises the following steps: obtaining other nodes andthe correlation values are arranged in descending order, and the nodes with the correlation values of the top 25 percent are selected and added into the moduleOther modules are constructed in a similar way to finally obtain

In one embodiment, the intra-module node alignment is as follows:

modularizing the networks G1 and G2 to obtain two module sets C1 and C2 respectively; comparing each module in C1 with each module in C2 by using a seed expansion method respectively to obtain a comparison result of | C1 |. C2| to the modules, wherein | C1|, | C2| refers to the number of the modules respectively;

the node similarity score function used in the module comparison process is as follows:

b (s, t) is the sequence similarity score of the node (s, t), which is calculated by BLAST + + tool to evaluate the biological similarity between nodes, the larger the value, the higher the node similarity;the topological similarity score between nodes is formed by a topological vector tuple based on the centrality of the feature vectorIs calculated to be in, wherein

1)Representation nodeTo a degree of (i) thatThe number of neighbors of (2);

2)representation nodeThe centrality of the feature vector is used for measuring the centrality position of the node in the network;

3)representation nodeThe centrality of the average feature vector of the neighbor;

thus, the topological similarity score of the node pair (s, t)The specific calculation mode formula is as formula (3); the smaller the value is, the more similar the nodes are;

using seed extension method to combine C_G1Respectively with C_G2The detailed steps of comparing every two modules in the module in the method are as follows:

a) input module to be compared

b) Firstly, the first step is toComparing;

c) separately acquireThe neighbor of (a) is (b),

d) computingSimilarity of nodal point pairsAnd use the Hungarian algorithm willThe nodes are compared, whereinIs composed ofAndthe cartesian product of (a);

e) will have expanded the nodeRemoving and repeating the step c) d) for the rest compared node pairs in sequence;

f) acquisition moduleInternal node comparison result

In one embodiment, the inter-module alignment is as follows:

regarding each module as a node, constructing a complete bipartite graph, wherein the weight of each edge is the similarity score between the modules; then, performing module matching by using a maximum weighted bipartite graph matching algorithm to obtain a comparison result between modules; wherein the inter-module similarity score is calculated as follows:

the number of the aligned node pairs in the alignment result in one module obtained in step 2,is the sum of the sequence similarities of the binding pairs in the alignment.

In one of the embodiments, wherein,the local edge conservation score between modules is used for measuring the edge conservation of the comparison result, and is specifically calculated as follows:

let e_ijRepresenting the local edge set of module C (j) in network Gi, Ei is the edge set of Gi, V (C (j)) is the node set of module C (j), e_ijIs represented as follows:

e_i，j＝{(s1，s2)|s1，s2∈V(C(j))∧(s1，s2)∈Ei}

5)

for networks G1 ═ (V1, E1), G2 ═ V2, E2,if it is notThen callA pair of module conservative edges;

presentation moduleThe conservative edge logic matrix of (2) has each element calculated as follows:

moduleThe local edge conservation score of (a) is calculated as follows:

in one embodiment, among others, C is scored according to module similarity_G1，C_G2The detailed procedure for the alignment between modules is as follows:

a) module set C for input networks G1, G2_G1，C_G2；

b) C is to be_G1，C_G2Each module of (a) is respectively regarded as a node, and a complete bipartite graph is constructedThe weight of an edge is a similarity score

c) Using Hungarian algorithm pairsSolving is carried out, and one-to-one module comparison can be obtained

In one embodiment, the existing alignment data processing is as follows:

and constructing a hypergraph by using the existing node mapping relation, wherein the nodes of the hypergraph are compared nodes, the comparison result of each pair of modules is abstracted into a hyper-arc of the hypergraph, and the node mapping relation of 1 to 1 is obtained by using a hypergraph matching algorithm.

Based on the same inventive concept, the present application also provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any of the methods when executing the program.

Based on the same inventive concept, the present application also provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of any of the methods.

Based on the same inventive concept, the present application further provides a processor for executing a program, wherein the program executes to perform any one of the methods.

The invention has the beneficial effects that:

(1) the topological similarity of the nodes is fully mined from the aspects of network structure, degree, node neighborhood, feature vector centrality, local edge conservation and the like, and the comparison topological quality is improved.

(2) And a proper modularization method is selected, a related value concept is provided, and the quality of the compared biological functions is ensured.

(3) The related value concept is provided, the problem that the blast sequence similarity score between nodes in the same network is lack is solved, and the universality of the algorithm is improved.

(4) The three similarity score functions are provided, so that the quality of biological functions of the algorithm is not reduced while the topological quality is improved.

Drawings

FIG. 1 is a flowchart of a biological network comparison method fusing various topological information according to the present invention.

FIG. 2 is an exemplary diagram of the similarity relationship between nodes of the biological network comparison method fusing various topology information according to the present invention.

FIG. 3 is a schematic diagram of the comparison result between the present invention and AligNet in the biological network comparison method of the present invention with various topological information.

FIG. 4 is a topological quality diagram of the present invention and other algorithms of the biological network comparison method fusing various topological information according to the present invention.

FIG. 5 is a biological function quality diagram of the present invention and other algorithms of the biological network comparison method fusing various topology information according to the present invention.

FIG. 6 is a comprehensive representation of the present invention and other algorithms of the biological network comparison method of the present invention incorporating various topological information.

Detailed Description

The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.

The technical solution of the present invention is illustrated by the flow chart shown in fig. 1.

A biological network comparison method fusing multiple topological information comprises the following steps:

step 1, reading networks and sequence similarity scores thereof, respectively calculating correlation value matrixes of the two networks, and carrying out module division on the networks, wherein nodes in the same module have higher similarity;

step 2, calculating similarity scores of nodes in the modules, and comparing every two nodes of the modules;

step 3, calculating similarity scores among the modules and comparing the modules;

step 4, integrating and screening the node mapping relations obtained in the steps 2 and 3 to obtain a node mapping relation of 1 to 1;

and 5, deleting the compared nodes, repeating the steps 2-5, comparing all the nodes in the small network, or keeping the similarity score between the modules to be 0, and stopping the algorithm.

(1) Module partitioning

The module division is made for a single network.

Firstly, a correlation value matrix of the network is calculated, the matrix gives out similarity relation between nodes, the invention gives out four definitions of relation between nodes, which are respectively strong correlation, weak correlation, correlation and irrelevance.

If an edge is connected between two nodes, the pair of nodes is called as strongly related;

if the nodes do not have edges which are directly connected but can be indirectly connected through other nodes, weak correlation is called;

nodes that correspond to strong and weak correlations are also called correlations;

pairs of nodes for which no dependency exists are all referred to as irrelevant.

An exemplary graph of similarity relationships between nodes is shown in FIG. 2:

the correlation value calculation formula is as follows:

wherein, theta is a strong correlation node set, phi is a weak correlation node set,as a set of unrelated nodes. max {1, | Φ^*|_MDenotes the maximum value of the number of passed intermediate edges in all relevant nodes, | Φ^* _(u，v)And | refers to the number of intermediate edges passed from node u to v. The formula (1) is a normalized correlation value, and the larger the value is, the higher the similarity between nodes is.

Then, according to the correlation value matrix, respectively carrying out modularization division on G1 and G2 to obtain a module set C_G1，C_G2. The detailed steps are as follows:

a) constructing a correlation value matrix psi for all node pairs in the network G ═ (V, E);

b) for theInitializing | V | pieces respectivelyModule being module-centric, note

c) ModuleThe construction method comprises the following steps: obtaining other nodes andthe correlation values are arranged in descending order, and the nodes with the correlation values of the top 25 percent are selected and added into the moduleOther modules are constructed in a similar way to finally obtain

(2) Intra-module node comparison

The networks G1 and G2 are modularized respectively to obtain two module sets C1 and C2. Comparing each module in C1 with each module in C2 by using a seed extension method, respectively, to obtain | C1| C2|, which indicates the number of modules, respectively, versus the module comparison result, | C1|, | C2 |.

The node similarity score function used in the module comparison process is as follows:

s (S, t) is the total similarity score between nodes (S, t), B (S, t) is the sequence similarity score of nodes (S, t), which is calculated by BLAST + + tool to evaluate the biological similarity between nodes, the larger the value, the more the nodes are relatedThe higher the similarity.The topological similarity score between nodes is formed by a topological vector tuple based on the centrality of the feature vectorIs calculated to be in, wherein

1)Representation nodeTo a degree of (i) thatThe number of neighbors of (2);

2)representation nodeThe centrality of the feature vector is used for measuring the centrality position of the node in the network;

3)representation nodeMean feature vector centrality of neighbors.

Thus, the topological similarity score of the node pair (s, t)The specific calculation mode is shown as formula (3). The smaller its value, the more similar between nodes.

Using seed extension method to combine C_G1Respectively with C_G2The detailed steps of comparing every two modules in the module in the method are as follows:

a) input module to be compared

b) Firstly, the first step is toComparing;

c) separately acquireThe neighbor of (a) is (b),

d) computingSimilarity of nodal point pairsAnd use the Hungarian algorithm willThe nodes are compared, whereinIs composed ofAndthe cartesian product of (a);

e) will have expanded the nodeRemoving and comparing the residueRepeating step c) d) in sequence in comparison with the nodal points;

f) obtaining the comparison result of the nodes in the module

(3) Comparison between modules

And regarding each module as a node, constructing a complete bipartite graph, and taking the weight of the edges as a similarity score between the modules. And then, carrying out module matching by using a maximum weighted bipartite graph matching algorithm to obtain a comparison result between modules. Wherein the inter-module similarity score is calculated as follows:

the number of several pairs of points compared in the comparison result in one module obtained in step 2,

is the sum of the sequence similarities of pairs of nodes in the alignment,

the local edge conservation score between modules is used for measuring the edge conservation of the comparison result, and is specifically calculated as follows:

let e_ijRepresenting the local edge set of module C (j) in network Gi, Ei is the edge set of Gi, V (C (j)) is the node set of module C (j), e_ijIs represented as follows:

e_i，j＝{(s1，s2)|s1，s2∈V(C(j))∧(s1，s2)∈Ei}

5)

for networks G1 ═ (V1, E1), G2 ═ V2, E2,if it is notThen callA pair of module conservative edges.

Presentation moduleThe conservative edge logic matrix of (2) has each element calculated as follows:

moduleThe local edge conservation score of (a) is calculated as follows:

score pair C according to module similarity_G1，C_G2The detailed procedure for the alignment between modules is as follows:

a) module set C for input networks G1, G2_G1，C_G2；

b) C is to be_G1，C_G2Each module of (a) is respectively regarded as a node, and a complete bipartite graph is constructedThe weight of an edge is a similarity score

c) Using Hungarian algorithm pairsSolving is carried out, and one-to-one module comparison can be obtained

(4) Existing alignment data processing

And constructing a hypergraph by using the existing node mapping relation, wherein the nodes of the hypergraph are compared nodes, the comparison result of each pair of modules is abstracted into a hyper-arc of the hypergraph, and the node mapping relation of 1 to 1 is obtained by using a hypergraph matching algorithm.

The invention has the beneficial effects that:

(1) the topological similarity of the nodes is fully mined from the aspects of network structure, degree, node neighborhood, feature vector centrality, local edge conservation and the like, and the comparison topological quality is improved.

(2) And a proper modularization method is selected, a related value concept is provided, and the quality of the compared biological functions is ensured.

(3) The related value concept is provided, the problem that the blast sequence similarity score between nodes in the same network is lack is solved, and the universality of the algorithm is improved.

(4) The three similarity score functions are provided, so that the quality of biological functions of the algorithm is not reduced while the topological quality is improved.

In order to test the comparison effect of the invention, the invention selects the latest version of the widely used IsoBase data set. The invention combines four species of M.musculus (MUS), C.elegans (CEL), D.melanogaster (DME) and S.cerevisiae (SCE) in IsoBase in pairs to obtain 6 species pairs for evaluation.

The invention uses three evaluation indexes EC, AFC and trade-off to respectively evaluate the topological quality, the biological function quality and the comprehensive performance of different algorithms, wherein the higher the AFC value is, the higher the corresponding quality of the algorithms is, the trade-off is the comprehensive ranking of the algorithms, the smaller the value is, the higher the ranking is represented, and the better the comprehensive performance is.

The method of the invention is ECAlign, FIG. 3 is a comparison of the invention and AligNet algorithm, because the invention is started by AligNet, the invention aims to improve the topological similarity of AligNet without reducing the quality of biological function, and as for the topological quality EC, the invention has higher scores on other species pairs than AligNet except for keeping the same topological score on SCE-DME; meanwhile, the invention not only obtains the same biological function quality as AligNet on most species pairs, but also has AFC scores exceeding AligNet on both MUS-DME and CEL-SCE species pairs. Therefore, the invention successfully improves the topological comparison quality on the premise of ensuring that the biological function of AligNet is not reduced.

Fig. 4 and 5 are comparisons of the present invention with other algorithms. As can be seen from FIG. 4, the EC score of the present invention is highest on the species MUS-CEL, MUS-SCE, MUS-DME, especially on MUS-SCE, the alignment quality of AligNet is lower than ModuleAlign, but the present invention by improvement achieves higher quality alignment than ModuleAlign; the expression of the ModuleAlign in other species pairs is only second to that of ModuleAlign, and a good comparison result tends to be generated on a network with a large difference of the number of nodes, but the stability of the ModuleAlign expression in different species pairs is poorer than that of other algorithms, and the comparison quality is different. As can be seen in FIG. 5, the AFC score of the present invention is second only to SPINAL, but actually has a small difference from SPINAL. In summary, the AFC scores of alignnet and SPINAL are very small in the majority of species pairs, and basically remain around 0.02, so that all three algorithms can produce high biological functional quality alignment results, and modulelaign performs the worst on all species pairs.

FIG. 6 is the comprehensive comparison ranking of the present invention with other algorithms, and the present invention obtains the highest ranking, i.e., the present invention performs best.

Through experimental analysis, the beneficial effects of (1), (2), (3) and (4) are further proved by the invention.

A specific application scenario of the present invention is given below:

take two networks of MUS and CEL in the ISOBASE database as an example:

respectively calculating correlation value matrixes of MUS and CEL, and performing module division to obtain a module set C_MUS,C_CEL；

For C_MUSEach of the modules C1, respectively, is connected with C_CELEach module C2 in the system utilizes a seed expansion algorithm to carry out intra-module comparison to obtain a comparison set C;

regarding each module in the comparison set C as a node, regarding similarity scores among the modules as edge weights to construct a complete bipartite graph, and solving the bipartite graph;

screening and integrating the existing comparison results to obtain a comparison result of 1-1;

repeating the steps until all the nodes in the MUS are compared or the similarity between the modules is all 0.

The biological network comparison method for fusing various topological information provided by the invention is described in detail above, and the following points need to be explained:

the invention provides a topological measurement method based on characteristic vector centrality and local edge conservative scoring, fully excavates the topological similarity of nodes, improves the topological quality of comparison and gives a calculation formula as follows:

topological similarity score based on feature vector centrality:

local edge conservation score:

the provided related value concept solves the problem that the blast sequence similarity score between nodes in the same network is lack, and improves the universality of the algorithm. Meanwhile, three similarity score functions of a topological measurement method based on characteristic vector centrality and local edge conservative score are adopted, so that the topological quality is improved, and the biological function quality of the algorithm is not reduced. The correlation value calculation formula is as follows:

the above-mentioned embodiments are merely preferred embodiments for fully illustrating the present invention, and the scope of the present invention is not limited thereto. The equivalent substitution or change made by the technical personnel in the technical field on the basis of the invention is all within the protection scope of the invention. The protection scope of the invention is subject to the claims.