CN114512185A - Donkey population natural selection classification system for variant data dimension reduction input - Google Patents
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Abstract
The invention belongs to the field of biological information data mining, and particularly discloses a donkey population natural selection classification system for variable data dimension reduction input. The system comprises: the donkey genome sequence data processing module comprises an input module, a donkey genome sequence data processing module and a classification module; the input module is used for acquiring donkey genome sequence data; donkey genome sequence data processing module, including: the donkey genome sequence data preprocessing unit and the donkey genome sequence data fusion unit are used for processing donkey genome sequence data acquired by the input module and converting the donkey genome sequence data into variable locus fusion data; the classification module comprises a model construction unit and a model prediction unit, and utilizes a convolutional neural network to construct a natural selection classification model, utilizes the variation site fusion data to perform data dimension reduction, and then performs natural selection classification on the donkey population. The method has the functional advantage of analyzing the influence of natural selection on the donkey population by excavating the donkey population genome data, and has few model parameters and high accuracy.
Description
Technical Field
The invention relates to the field of biological population genome data mining, in particular to a natural selection classification system based on a neural network.
Background
Group genetics is the life science of studying the genetic characteristics and rules of biological groups. In agricultural production, the method has great economic value for pest and disease management and seed selection breeding; in medical treatment, the medicine has great contribution to the infection law of diseases; has great scientific significance for biodiversity protection and research.
At present, some systems for natural selection and classification of drosophila and fish appear at home and abroad, the systems separately process variation node position information and variation matrix data to form a multi-input network, and the variation node position information is combined through a fully-connected network, so that the problems of overlarge parameter quantity and overfitting are caused, the accuracy of a model is influenced, and the system is difficult to apply to specific production practice. In addition, there are few natural selection classification systems for donkey populations, and these systems do not solve the problem of natural selection classification of donkey populations well. The problems restrict the agricultural production, seed selection and breeding efficiency of donkey and restrict the scientific breeding of donkey.
Disclosure of Invention
The invention provides a donkey population natural selection classification system for variable data dimension reduction input, which aims at solving the problems of difficult model training and low model accuracy caused by overlarge neural network parameters acting on natural selection classification and the condition that natural selection research on donkey populations by current domestic and foreign models is little, and aims at researching the natural selection influence on donkey populations and improving the condition of model accuracy and applying the conditions to specific production practice. And (4) outputting a natural selection classification result through the variable locus fusion data and the neural network. The system adopts a CNN neural network to carry out natural selection judgment.
The technical scheme adopted by the invention is as follows:
a donkey population natural selection classification system for variant data dimension reduction input comprises: the donkey genome sequence data processing system comprises an input module, a donkey genome sequence data processing module and a classification module;
the input module is used for acquiring donkey genome sequence data;
the donkey genome sequence data processing module is connected with the input module and is used for processing the genome sequence data acquired by the input module and outputting the variation site position data and the variation matrix data of the donkey genome sequence;
the donkey genome sequence data processing module comprises: the donkey genome sequence data preprocessing unit and the donkey genome sequence data fusion unit;
the donkey genome sequence data preprocessing unit is used for dividing and cleaning donkey genome sequence data;
the donkey genome sequence data preprocessing unit comprises: the donkey genome sequence data slicer, the mutation node position calculator, the donkey genome sequence data converter and the donkey genome sequence data washer;
the donkey genome sequence data slicer is used for slicing donkey genome sequences into a plurality of equal-size fragments;
the variant node position calculator is used for calculating the relative position of the locus in the donkey gene segment in the corresponding segment;
the donkey genome sequence data converter is used for converting the divided donkey genome segment data into a 0,1 binary data matrix, namely variation matrix data, wherein 0 represents an ancestor gene, and 1 represents a variation gene;
the donkey genome sequence data cleaner is used for deleting over-short and over-long data, merging repeated locus data and carrying out OR operation on the repeated locus data to obtain a result;
the donkey genome sequence data fusion unit is used for calculating the position data of the mutation sites and the position data of the mutation matrix to generate mutation site fusion data, and the specific calculation process comprises the following steps:
Mij＝Hij*posj
wherein HijRefers to the ith row and jth column data, pos, of the mutation matrixjRefers to the relative position of the jth mutation site in the fragment interval, which represents multiplication, MijRefers to the ith row and jth column data of the mutation site fusion data;
the classification module comprises: the donkey genome sequence model building unit and the donkey genome sequence model classifying unit are characterized in that the classifying module builds a donkey population natural selection classifying model by using a convolutional neural network and performs natural selection classification on donkey populations by using data output by the donkey genome sequence data processing module;
the donkey genome sequence model building unit adopts a convolutional neural network to build a natural selection model, and the model building is carried out according to the following sequence: calling an Input layer, a CNN layer and a Dropout layer to build a classification model; the CNN layer is used for vector characterization learning, the Dropout layer is used for preventing model overfitting, then the weight is adjusted according to each feature, and finally the weight and the feature value vector are multiplied and then summed and output;
the calculation process of the CNN layer is as follows:
V＝conv(W,X)+b
w is the weight matrix, X is the mutation site fusion data, b is the bias,
is the activation function, conv is the convolution function, V is the convolution function output, Y is the activation function output;
the donkey genome sequence model classification unit inputs the variable locus fusion data obtained by the donkey genome sequence data processing module into the model constructed by the genome sequence model construction unit, trains the model by using the training set data, and reads the test set into the trained model for natural selection classification; wherein, the natural selection prediction classification process is carried out according to the following sequence: (1) reading in the variable locus fusion data output by the donkey genome sequence data processing module, dividing the read-in data into a training set and a test set according to the ratio of 8:2, (2) coding the discrete type data by adopting a single-hot coding mode to finally obtain the vector representation of the variable locus data, (3) inputting the training set data converted into the vector representation into a model for model training, (4) reading in the test set data by utilizing the trained model, and performing natural selection classification.
Compared with the prior art, the invention has the beneficial effects that:
the donkey population natural selection classification system with the dimensionality reduction input of the variation data can analyze the natural selection classification of donkey populations, the variation site fusion data can reduce the input size so as to reduce neural network parameters, and the variation site fusion data of the donkey populations are automatically extracted and analyzed through the neural network, so that high-accuracy and reliable results are obtained, and the system has application values in the aspects of analyzing the natural selection effect received by the variation nodes of the donkey populations, agricultural production, seed selection and breeding, scientific feeding and the like.
Drawings
FIG. 1 is a donkey population natural selection classification system with variable data dimension reduction input.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Fig. 1 shows a donkey population natural selection classification system with variable data dimension reduction input according to an embodiment of the present invention.
Referring to fig. 1, the system for natural selection and classification of donkey populations with variant data input in a dimensionality reduction manner according to the embodiment of the present invention includes: the system comprises an input module, a data processing module and a classification module;
the input module is used for acquiring donkey genome sequence data;
the donkey genome sequence data processing module is connected with the input module and is used for processing the genome sequence data acquired by the input module and outputting the mutation site position data and the mutation matrix data of the donkey genome sequence;
the donkey genome sequence data processing module comprises: the donkey genome sequence data preprocessing unit and the donkey genome sequence data fusion unit;
the donkey genome sequence data preprocessing unit is used for dividing and cleaning donkey genome sequence data;
the donkey genome sequence data preprocessing unit comprises: the donkey genome sequence data slicer, the mutation node position calculator, the donkey genome sequence data converter and the donkey genome sequence data washer;
the donkey genome sequence data slicer is used for slicing donkey genome sequences into a plurality of fragments with equal sizes;
the variant node position calculator is used for calculating the relative position of the locus in the donkey gene segment in the corresponding segment;
the donkey genome sequence data converter is used for converting the divided donkey genome segment data into a 0,1 binary data matrix, namely variation matrix data, wherein 0 represents an ancestor gene, and 1 represents a variation gene;
the donkey genome sequence data cleaner is used for deleting over-short and over-long data, merging repeated locus data and carrying out OR operation on the repeated locus data to obtain a result;
the donkey genome sequence data fusion unit is used for calculating the position data of the mutation sites and the position data of the mutation matrix to generate mutation site fusion data, and the specific calculation process comprises the following steps:
Mij＝Hij*posj
wherein HijRefers to the ith row and jth column data, pos, of the mutation matrixjRefers to the relative position of the jth mutation site in the fragment interval, which represents multiplication, MijRefers to the ith row and jth column data of the mutation site fusion data;
the classification module comprises: the donkey genome sequence model building unit and the donkey genome sequence model classifying unit are characterized in that the classifying module builds a donkey population natural selection classifying model by using a convolutional neural network and performs natural selection classification on donkey populations by using data output by the donkey genome sequence data processing module;
the donkey genome sequence model building unit adopts a convolutional neural network to build a natural selection model, and the model building is carried out according to the following sequence: calling an Input layer, a CNN layer and a Dropout layer to build a classification model; the CNN layer is used for vector characterization learning, the Dropout layer is used for preventing model overfitting, then the weight is adjusted according to each feature, and finally the weight and the feature value vector are multiplied and then summed and output;
the calculation process of the CNN layer is as follows:
V＝conv(W,X)+b
w is the weight matrix, X is the mutation site fusion data, b is the bias,
is the activation function, conv is the convolution function, V is the convolution function output, Y is the activation function output;
the donkey genome sequence model classification unit inputs the variable locus fusion data obtained by the donkey genome sequence data processing module into the model constructed by the genome sequence model construction unit, trains the model by using the training set data, and reads the test set into the trained model for natural selection classification; wherein, the natural selection prediction classification process is carried out according to the following sequence: (1) reading in the variable locus fusion data output by the donkey genome sequence data processing module, dividing the read-in data into a training set and a test set according to the ratio of 8:2, (2) coding the discrete type data by adopting a single-hot coding mode to finally obtain the vector representation of the variable locus data, (3) inputting the training set data converted into the vector representation into a model for model training, (4) reading in the test set data by utilizing the trained model, and performing natural selection classification.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may occur to those skilled in the art without departing from the principle of the invention, and are considered to be within the scope of the invention.
Claims (1)
1. A donkey population natural selection classification system for variant data dimension reduction input is characterized in that the classification system comprises: the donkey genome sequence data processing system comprises an input module, a donkey genome sequence data processing module and a classification module;
the input module is used for acquiring donkey genome sequence data;
the donkey genome sequence data processing module is connected with the input module and is used for processing the genome sequence data acquired by the input module and outputting the variation site position data and the variation matrix data of the donkey genome sequence;
the donkey genome sequence data processing module comprises: the donkey genome sequence data preprocessing unit and the donkey genome sequence data fusion unit;
the donkey genome sequence data preprocessing unit is used for dividing and cleaning donkey genome sequence data;
the donkey genome sequence data preprocessing unit comprises: the donkey genome sequence data slicer, the mutation node position calculator, the donkey genome sequence data converter and the donkey genome sequence data washer;
the donkey genome sequence data slicer is used for slicing donkey genome sequences into a plurality of equal-size fragments;
the variant node position calculator is used for calculating the relative position of the locus in the donkey gene segment in the corresponding segment;
the donkey genome sequence data converter is used for converting the divided donkey genome segment data into a 0,1 binary data matrix, namely variation matrix data, wherein 0 represents an ancestor gene, and 1 represents a variation gene;
the donkey genome sequence data cleaner is used for deleting over-short and over-long data, merging repeated locus data and carrying out OR operation on the repeated locus data to obtain a result;
the donkey genome sequence data fusion unit is used for calculating the position data of the mutation sites and the position data of the mutation matrix to generate mutation site fusion data, and the specific calculation process comprises the following steps:
Mij＝Hij*posj
wherein HijRefers to the ith row and jth column data, pos, of the mutation matrixjRefers to the relative position of the jth mutation site in the fragment interval, which represents multiplication, MijFinger mutation site fusionThe ith row and the jth column of data;
the classification module comprises: the donkey genome sequence model building unit and the donkey genome sequence model classifying unit are characterized in that the classifying module builds a donkey population natural selection classifying model by using a convolutional neural network and performs natural selection classification on donkey populations by using data output by the donkey genome sequence data processing module;
the donkey genome sequence model building unit adopts a convolutional neural network to build a natural selection model, and the model building is carried out according to the following sequence: calling an Input layer, a CNN layer and a Dropout layer to build a classification model; the CNN layer is used for vector characterization learning, the Dropout layer is used for preventing model overfitting, then the weight is adjusted according to each feature, and finally the weight and the feature value vector are multiplied and then summed and output;
the calculation process of the CNN layer is as follows:
V＝conv(W,X)+b
w is a weight matrix, X is mutation site fusion data, b is an offset,
is the activation function, conv is the convolution function, V is the convolution function output, Y is the activation function output;
the donkey genome sequence model classification unit inputs the variable locus fusion data obtained by the donkey genome sequence data processing module into the model constructed by the genome sequence model construction unit, trains the model by using the training set data, and reads the test set into the trained model for natural selection classification; wherein, the natural selection prediction classification process is carried out according to the following sequence: (1) reading in the variable locus fusion data output by the donkey genome sequence data processing module, dividing the read-in data into a training set and a test set according to the ratio of 8:2, (2) coding the discrete type data by adopting a single-hot coding mode to finally obtain the vector representation of the variable locus data, (3) inputting the training set data converted into the vector representation into a model for model training, (4) reading in the test set data by utilizing the trained model, and performing natural selection classification.
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