Xiaoya Fan

Genome annotation currently requires performing dozens of molecular assays in hundreds of cell and tissue samples, an expensive endeavor which is impractical to replicate across all species and conditions of interest. Here, we introduce BioSeq2Seq, a deep learning framework that infers cell-line-specific molecular assays widely used for genome annotation by leveraging a tri-modal input: evolutionarily conserved DNA sequence features, together with cell-line-specific transcriptional activity and directionality captured by a single run-on sequencing assay. BioSeq2Seq enables flexible genome annotation tasks through parameterized configurations of input features and output targets, combined with gradient-guided architectural refinement for specific biological objectives. Our model demonstrates high accuracy across four downstream tasks, showing improvements of 14.27% in histone modification prediction, 2.50% in functional element identification, and 2.90% in gene expression prediction compared to state-of-the-art methods. In transcription factor binding site (TFBS) prediction, it maintains performance comparable to that of leading existing approaches. By achieving competitive performance across tasks with single-cell-line input data, BioSeq2Seq provides an efficient and low-cost alternative for genome annotation.

Motivation: N6-methyladenine (6 mA) is an important epigenetic modification of DNA that regulates biological processes such as gene expression, transcription, replication, DNA repair, and cell cycle without altering the DNA sequence. It also plays a key role in many diseases including cancer and autoimmune diseases. Although experimental approaches such as SMRT sequencing and methylated DNA immunoprecipitation can identify 6 mA sites, they suffer from drawbacks including suboptimal sequencing quality, low signal-to-noise ratios, high costs, and time-consuming procedures. In recent years, deep learning approaches have demonstrated significant advantages in predicting 6 mA sites; however, their generalization ability still requires further improvement. Results: Inspired by the state space model Mamba, we propose a novel model for 6 mA site prediction, named Mamba6mA. In the Mamba6mA model, we design position-specific linear layers to replace traditional convolutional layers to facilitate capture specific positional information. Meanwhile, we construct a multi-scale feature extraction module and integrate features captured by sliding windows of different scales, feeding them into the classifier for prediction. Experimental results show that Mamba6mA achieves the best MCC on 9 out of 11 species datasets, surpassing existing state-of-the-art models. Ablation studies confirm that the position-specific linear layers and the multi-scale fusion module contribute MCC performance gains of 2.36% and 2.31%, respectively. Feature visualization analysis further reveals that the model effectively captures sequence patterns upstream and downstream of 6 mA sites providing a new technical approach for studying epigenetic modification mechanisms. Availability and implementation: The source code for Mamba6mA is available at: https://github.com/XploreAI-Lab/Mamba6mA.

Genome annotation currently requires performing dozens of molecular assays in hundreds of cell and tissue samples, an expensive endeavor which is impractical to replicate across all species and conditions of interest. Here, we introduce BioSeq2Seq, a deep learning model that infers cell-type-specific molecular assays widely used for genome annotation by leveraging a tri-modal input: evolutionarily conserved DNA sequence features, together with cell-type-specific transcriptional activity and directionality captured by a single run-on sequencing assay. BioSeq2Seq enables flexible genome annotation tasks through parameterized configurations of input features and output targets, combined with gradient-guided architectural refinement for specific biological objectives. Our model demonstrates high accuracy across four downstream tasks, showing improvements of 10.37% in histone modification prediction, 2.25% in functional element prediction, and 5.02% in gene expression prediction compared to state-of-the-art methods. In transcription factor binding site (TFBS) prediction, it maintains performance comparable to that of leading existing approaches. By achieving competitive performance across tasks with minimal input data, BioSeq2Seq provides an efficient and low-cost alternative for genome annotation. To facilitate broader application, an online prediction service based on BioSeq2Seq is publicly available at https://dreg.dnasequence.org.

DNA methylation is a key epigenetic modification involved in biological processes and disease development. The accurate analysis of DNA methylation site information is of significant biological importance. Despite advances in single-cell sequencing, data sparsity due to low cytosine-phosphate-guanine (CpG) coverage remains a challenge. To address this, we introduce MambaCpG, a single-cell DNA methylation state imputation model based on the Mamba block. MambaCpG integrates the methylation matrix and DNA sequence context and uses bidirectional Mamba blocks to obtain embeddings, effectively capturing the long-range dependencies between CpG sites within DNA methylation patterns. Experiments on seven datasets of various scales demonstrate that MambaCpG outperforms existing models on large, highly sparse datasets while showing competitive performance on smaller datasets. MambaCpG has lower parameter and memory requirements, making it suitable for practical applications. MambaCpG also reveals ultra-long-range dependencies and provides new insights into DNA methylation patterning.

The widespread reuse of code in the open-source community has led to the proliferation of homologous vulnerabilities, which are security flaws propagated across diverse software systems through the reuse of vulnerable code. Such vulnerabilities pose serious cybersecurity risks, as attackers can exploit the same weaknesses across multiple platforms. Deep learning has emerged as a promising approach for detecting homologous vulnerabilities in binary code due to their automated feature extraction and high efficiency. However, existing deep learning methods often struggle to capture deep semantic features in binary code, limiting their effectiveness. To address this limitation, this paper presents GMN+, which is a novel graph matching neural network with enhanced attention for detecting homologous vulnerabilities. This method comprehensively considers the information contained in instructions and incorporates types of input instruction. Masked Language Modeling and Instruction Type Prediction are developed as pre-training tasks to enhance the ability of GMN+ in extracting semantic information from basic blocks. GMN+ utilizes an attention mechanism to focus concurrently on the critical semantic information within functions and differences between them, generating robust function embeddings. Experimental results indicate that GMN+ outperforms state-of-the-art methods in various tasks and achieves notable performance in real-world vulnerability detection scenarios.

The risk of gastric cancer can be predicted by gastroscopic manifestation recognition and the Kyoto Gastritis Score. This study aims to validate the applicability of AI approaches for recognizing gastroscopic manifestations according to the definition of Kyoto Gastritis Score, with the goal of improving early gastric cancer detection and reducing gastric cancer mortality. In this retrospective study, 29013 gastric endoscopy images were collected and carefully annotated into five categories according to the Kyoto Gastritis Score, i.e. atrophy (A), diffuse redness (DR), enlarged folds (H), intestinal metaplasia (IM), and nodularity (N). As a multi-label recognition task, we propose a deep learning approach composed of five GAM-EfficientNet models, each performing a multiple classification to quantify gastroscopic manifestations, i.e. no presentation or the severity score 0–2. This approach was compared with endoscopists of varying years of experience in terms of accuracy, specificity, precision, recall, and F1 score. The approach demonstrated good performance in identifying the five manifestations of the Kyoto Gastritis Score, with an average accuracy, specificity, precision, recall, and F1 score of 78.70%, 91.92%, 80.23%, 78.70%, and 0.78, respectively. The average performance of five experienced endoscopists was 72.63%, 90.00%, 77.68%, 72.63%, and 0.73, while that of five less experienced endoscopists was 66.60%, 87.44%, 70.88%, 66.60%, and 0.66, respectively. The sample t-test indicates that the approach’s average accuracy, specificity, precision, recall, and F1 score for identifying the five manifestations were significantly higher than those of less experienced endoscopists, experienced endoscopists, and all endoscopists on average (p < 0.05). Our study demonstrates the potential of deep learning approaches on gastric manifestation recognition over junior, even senior endoscopists. Thus, the deep learning approach holds potential as an auxiliary tool, although prospective validation is still needed to assess its clinical applicability.