Abstract
BackgroundAt present, the bioinformatics research on the relationship between aging-related diseases and genes is mainly through the establishment of a machine learning multi-label model to classify each gene. Most of the existing methods for predicting pathogenic genes mainly rely on specific types of gene features, or directly encode multiple features with different dimensions, use the same encoder to concatenate and predict the final results, which will be subject to many limitations in the applicability of the algorithm. Possible shortcomings of the above include: incomplete coverage of gene features by a single type of biomics data, overfitting of small dimensional datasets by a single encoder, or underfitting of larger dimensional datasets.MethodsWe use the known gene disease association data and gene descriptors, such as gene ontology terms (GO), protein interaction data (PPI), PathDIP, Kyoto Encyclopedia of genes and genomes Genes (KEGG), etc, as input for deep learning to predict the association between genes and diseases. Our innovation is to use Mashup algorithm to reduce the dimensionality of PPI, GO and other large biological networks, and add new pathway data in KEGG database, and then combine a variety of biological information sources through modular Deep Neural Network (DNN) to predict the genes related to aging diseases.Result and conclusionThe results show that our algorithm is more effective than the standard neural network algorithm (the Area Under the ROC curve from 0.8795 to 0.9153), gradient enhanced tree classifier and logistic regression classifier. In this paper, we firstly use DNN to learn the similar genes associated with the known diseases from the complex multi-dimensional feature space, and then provide the evidence that the assumed genes are associated with a certain disease.
Highlights
At present, the bioinformatics research on the relationship between aging-related diseases and genes is mainly through the establishment of a machine learning multi-label model to classify each gene
Comparison of Mashup and deep learning (MDL) with other methods we compare our traditional machine learning and deep learning algorithm with several other baseline algorithms, including: (1) existing Deep Neural Network (DNN) algorithm, including using a single feature type and concatenating all data sets for training; (2) gradient boosting tree algorithm; (3) logistic regression classifier (LR), using L2 regularization and other default parameters; (4) In order to verify the effectiveness of the Mashup algorithm, we compare the predictive performance of the DNN algorithm using an Encoder 1 module where Mashup was used to process the gene ontology terms (GO) and protein– protein interaction (PPI) features together against the DNN algorithm without using Mashup
M is the combination of PathDIP+genotype-tissue expression (GTEx)+Mashup+Kyoto Encyclopedia of genes and genomes Genes (KEGG), and Concat
Summary
The bioinformatics research on the relationship between aging-related diseases and genes is mainly through the establishment of a machine learning multi-label model to classify each gene. One of the main problems in the study of human aging is that it is much more difficult to carry out experiments due to obvious ethical reasons and long experimental time, so animal models with shorter life are usually preferred. This problem creates the opportunity to deploy bioinformatics methods to study human aging. It is important to propose a more effective multi label learning algorithm to reduce the workload of experimental verification in the field of biology [4, 5]
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