Background Copy number variation (CNV) is a type of structural variation, which is a gain or loss event with abnormal changes in copy number. Methods to predict the pathogenicity of CNVs are required to realize the relationship between these variants and clinical phenotypes. ClassifyCNV, X-CNV, StrVCTVRE, etc. have been trained to predict the pathogenicity of CNVs, but few studies have been reported based on the deleterious significance of features. Results From single nucleotide polymorphism (SNP), gene and region dimensions, we collected 79 informative features that quantitatively describe the characteristics of CNV, such as CNV length, the number of protein genes, the number of three prime untranslated region. Then, according to the deleterious significance, we formulated quantitative methods for features, which fall into two categories: the first is variable type, including maximum, minimum and mean; the second is attribute type, which is measured by numerical sum. We used Gradient Boosted Trees (GBT) algorithm to construct dbCNV, which can be used to predict pathogenicity for five-tier classification and binary classification of CNVs. We demonstrated that the distribution of most feature values was consistent with the deleterious significance. The five-tier classification model accuracy for 0.85 and 0.79 in loss and gain CNVs, which proved that it has high discrimination power in predicting the pathogenicity of five-tier classification CNVs. The binary model achieved area under curve (AUC) values of 0.96 and 0.81 in the validation set, respectively, in gain and loss CNVs. Conclusion The performance of the dbCNV suggest that functional deleteriousness-based model of CNV is a promising approach to support the classification prediction and to further understand the pathogenic mechanism.
In order to research the effect of the steel fiber volume and the rubber powder diameter on the mechanics performance of the layer steel fiber reinforced rubber concrete, the cube compression tests, splitting tensile tests and four-point bending tests are performed on steel fiber reinforced rubber concrete with steel fiber content of 0.5%, 1%, 1.5%, 2%, 2.5% and rubber powder mesh 20, 40, 60. When 40 mesh rubber powder is added and the volume ratio of steel fiber is 1.5%, the compressive strength of laminated steel fiber rubber concrete reaches the peak value. The splitting tensile strength of laminated steel fiber rubber concrete mixed with2.0% steel fiber and mesh 40 rubber powder is the best. The flexural strength of laminated steel fiber rubber concrete mixed with 2.5% steel fiber and mesh 40 rubber powder is highest in this paper.
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