iCpG-Pos: an accurate computational approach for identification of CpG sites using positional features on single-cell whole genome sequence data

CpGFuse: a holistic approach for accurate identification of methylation states of DNA CpG sites

Anomalous DNA methylation has wide-ranging implications, spanning from neurological disorders to cancer and cardiovascular complications. Current methods for single-cell DNA methylation analysis face limitations in coverage, leading to information loss and hampering our understanding of disease associations. The primary goal of this study is the imputation of CpG site methylation states in a given cell by leveraging the CpG states of other cells of the same type. To address this, we introduce CpGFuse, a novel methodology that combines information from diverse genomic features. Leveraging two benchmark datasets, we employed a careful preprocessing approach and conducted a comprehensive ablation study to assess the individual and collective contributions of DNA sequence, intercellular, and intracellular features. Our proposed model, CpGFuse, employs a convolutional neural network with an attention mechanism, surpassing existing models across HCCs and HepG2 datasets. The results highlight the effectiveness of our approach in enhancing accuracy and providing a robust tool for CpG site prediction in genomics. CpGFuse's success underscores the importance of integrating multiple genomic features for accurate identification of methylation states of CpG site.

SB-Net: Synergizing CNN and LSTM networks for uncovering retrosynthetic pathways in organic synthesis

Retrosynthesis is vital in synthesizing target products, guiding reaction pathway design crucial for drug and material discovery. Current models often neglect multi-scale feature extraction, limiting efficacy in leveraging molecular descriptors. Our proposed SB-Net model, a deep-learning architecture tailored for retrosynthesis prediction, addresses this gap. SB-Net combines CNN and Bi-LSTM architectures, excelling in capturing multi-scale molecular features. It integrates parallel branches for processing one-hot encoded descriptors and ECFP, merging through dense layers. Experimental results demonstrate SB-Net's superiority, achieving 73.6 % top-1 and 94.6 % top-10 accuracy on USPTO-50k data. Versatility is validated on MetaNetX, with rates of 52.8 % top-1, 74.3 % top-3, 79.8 % top-5, and 83.5 % top-10. SB-Net's success in bioretrosynthesis prediction tasks indicates its efficacy. This research advances computational chemistry, offering a robust deep-learning model for retrosynthesis prediction. With implications for drug discovery and synthesis planning, SB-Net promises innovative and efficient pathways.

4mCPred-GSIMP: Predicting DNA N4-methylcytosine sites in the mouse genome with multi-Scale adaptive features extraction and fusion

The epigenetic modification of DNA N4-methylcytosine (4mC) is vital for controlling DNA replication and expression. It is crucial to pinpoint 4mC's location to comprehend its role in physiological and pathological processes. However, accurate 4mC detection is difficult to achieve due to technical constraints. In this paper, we propose a deep learning-based approach 4mCPred-GSIMP for predicting 4mC sites in the mouse genome. The approach encodes DNA sequences using four feature encoding methods and combines multi-scale convolution and improved selective kernel convolution to adaptively extract and fuse features from different scales, thereby improving feature representation and optimization effect. In addition, we also use convolutional residual connections, global response normalization and pointwise convolution techniques to optimize the model. On the independent test dataset, 4mCPred-GSIMP shows high sensitivity, specificity, accuracy, Matthews correlation coefficient and area under the curve, which are 0.7812, 0.9312, 0.8562, 0.7207 and 0.9233, respectively. Various experiments demonstrate that 4mCPred-GSIMP outperforms existing prediction tools.