DataSheet_1_Gene Expression Value Prediction Based on XGBoost Algorithm.docx

Gene expression profiling has been widely used to characterize cell status to reflect the health of the body, to diagnose genetic diseases, etc. In recent years, although the cost of genome-wide expression profiling is gradually decreasing, the cost of collecting expression profiles for thousands of genes is still very high. Considering gene expressions are usually highly correlated in humans, the expression values of the remaining target genes can be predicted by analyzing the values of 943 landmark genes. Hence, we designed an algorithm for predicting gene expression values based on XGBoost, which integrates multiple tree models and has stronger interpretability. We tested the performance of XGBoost model on the GEO dataset and RNA-seq dataset and compared the result with other existing models. Experiments showed that the XGBoost model achieved a significantly lower overall error than the existing D-GEX algorithm, linear regression, and KNN methods. In conclusion, the XGBoost algorithm outperforms existing models and will be a significant contribution to the toolbox for gene expression value prediction.</p

Table1_An Interpretable Double-Scale Attention Model for Enzyme Protein Class Prediction Based on Transformer Encoders and Multi-Scale Convolutions.XLSX

Background Classification and annotation of enzyme proteins are fundamental for enzyme research on biological metabolism. Enzyme Commission (EC) numbers provide a standard for hierarchical enzyme class prediction, on which several computational methods have been proposed. However, most of these methods are dependent on prior distribution information and none explicitly quantifies amino-acid-level relations and possible contribution of sub-sequences.Methods In this study, we propose a double-scale attention enzyme class prediction model named DAttProt with high reusability and interpretability. DAttProt encodes sequence by self-supervised Transformer encoders in pre-training and gathers local features by multi-scale convolutions in fine-tuning. Specially, a probabilistic double-scale attention weight matrix is designed to aggregate multi-scale features and positional prediction scores. Finally, a full connection linear classifier conducts a final inference through the aggregated features and prediction scores.Results On DEEPre and ECPred datasets, DAttProt performs as competitive with the compared methods on level 0 and outperforms them on deeper task levels, reaching 0.788 accuracy on level 2 of DEEPre and 0.967 macro-F1 on level 1 of ECPred. Moreover, through case study, we demonstrate that the double-scale attention matrix learns to discover and focus on the positions and scales of bio-functional sub-sequences in the protein.Conclusion Our DAttProt provides an effective and interpretable method for enzyme class prediction. It can predict enzyme protein classes accurately and furthermore discover enzymatic functional sub-sequences such as protein motifs from both positional and spatial scales.</p

Table2_An Interpretable Double-Scale Attention Model for Enzyme Protein Class Prediction Based on Transformer Encoders and Multi-Scale Convolutions.XLSX

Background Classification and annotation of enzyme proteins are fundamental for enzyme research on biological metabolism. Enzyme Commission (EC) numbers provide a standard for hierarchical enzyme class prediction, on which several computational methods have been proposed. However, most of these methods are dependent on prior distribution information and none explicitly quantifies amino-acid-level relations and possible contribution of sub-sequences.Methods In this study, we propose a double-scale attention enzyme class prediction model named DAttProt with high reusability and interpretability. DAttProt encodes sequence by self-supervised Transformer encoders in pre-training and gathers local features by multi-scale convolutions in fine-tuning. Specially, a probabilistic double-scale attention weight matrix is designed to aggregate multi-scale features and positional prediction scores. Finally, a full connection linear classifier conducts a final inference through the aggregated features and prediction scores.Results On DEEPre and ECPred datasets, DAttProt performs as competitive with the compared methods on level 0 and outperforms them on deeper task levels, reaching 0.788 accuracy on level 2 of DEEPre and 0.967 macro-F1 on level 1 of ECPred. Moreover, through case study, we demonstrate that the double-scale attention matrix learns to discover and focus on the positions and scales of bio-functional sub-sequences in the protein.Conclusion Our DAttProt provides an effective and interpretable method for enzyme class prediction. It can predict enzyme protein classes accurately and furthermore discover enzymatic functional sub-sequences such as protein motifs from both positional and spatial scales.</p

Comparison of the significance of scatters obtained by DLPCA with <i>β</i>₁ = <i>β</i>₂, DLPCA with <i>β</i>₁ > <i>β</i>₂, and MLPCA.

<p>Each grouped bar chart represents the results of different approaches with the same numbers of seeds.</p