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.

Object recognition from electroencephalography (EEG) responses to visual stimuli has received growing attention but has remained challenging, with prior methods achieving only marginally above-chance accuracy for randomized EEG trials. This paper introduces GVFL-EEG (Guided Visual Feature Learning from EEG), a novel framework that leverages well-trained computer vision models to enhance EEG object decoding. Specifically, a pre-trained image encoder is used to guide EEG feature learning via contrastive learning, aligning EEG embeddings with image representations. We present EEGMambaformer, a custom EEG encoder incorporating a residual Mamba block to capture temporal dynamics, an inverted transformer encoder to extract spatial dependencies, a temporal-spatial convolution block for feature fusion, and a projection layer for dimension alignment with the image encoder. Evaluation on the EEG40000 dataset show that our framework achieves 60.94% accuracy in 40-way classification, outperforming existing state-of-the-art methods. This work advances neural decoding and provides insights into brain-computer interface development. The code is available at https://github.com/xuehaixiao/GVFL_EEG/tree/main/GVFL.

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.

Accurately predicting the secondary structure of Non-coding RNAs is crucial for understanding their biological roles. However, current methods face challenges in accuracy and computational efficiency, particularly for long RNA sequences. Here, we present a novel three-step framework for RNA secondary structure prediction, PPA (Partition, Predict, and Assemble). It first partitions the RNA sequence into independent fragments based on exterior loops, then independently predicts the secondary structure of each fragment, and finally assembles them to construct the complete RNA secondary structure. Following the PPA framework, we introduce ELPBert, a model for RNA exterior loop prediction. We partition the RNA sequence at the central nucleotide of the predicted exterior-loop bases. The performance of six state-of-the-art RNA secondary structure prediction methods with and without RNA partition using our ELPBert were compared. Results demonstrate a great improvement of these methods with RNA partition, especially for long sequences. We believe our PPA framework could serve as an universal framework for RNA secondary structure prediction, particularly for long RNA sequences. Data and code are provided at https://github.com/Kali-ym/PPAwithELPBert.

Seizure detection and type classification from electroencephalogram (EEG) has the potential to improve the diagnosis and treatment of epilepsy. Although the task of seizure detection has been well-investigated, seizure type classification remains largely unexplored. The intricate nature of seizure dynamics presents a significant challenge in effectively extracting distinguishing features from noisy and high-dimensional EEG signals. Previous studies mainly focus on extracting features from temporal, spectral or special domain. Extracting temporal-spatial-spectral features simultaneously from EEG remains challenging. In this paper, we introduce an attention-based neural network to effectively extract temporal-spatial-spectral EEG features, for seizure type classification. It utilizes an attention module with one-shot aggregation to extract multi-level temporal-spatial-spectral EEG features, aiming to differentiate the complex patterns of various seizure types. Specifically, we construct the 3-dimensional representation of EEG by stacking the time-frequency matrices obtained from short time Fourier transform. The attention module consists of paralleled temporal and spatial-spectral attention blocks, allowing the model to focus on the most distinctive time stamps, sensor locations, and frequency bands. The proposed approach is validated on the largest public seizure EEG database, TUSZ v1.5.2. Five-fold cross validation demonstrates that our framework achieved 0.951 weighted F1 score on seizure type classification, achieving state-of-the-art performance. Ablation study confirmed the effectiveness of the temporal and spatial-spectral attention blocks.

A novel temporal convolutional network (TCN) model is utilized to reconstruct the central aortic blood pressure (aBP) waveform from the radial blood pressure waveform. The method does not need manual feature extraction as traditional transfer function approaches. The data acquired by the SphygmoCor CVMS device in 1,032 participants as a measured database and a public database of 4,374 virtual healthy subjects were used to compare the accuracy and computational cost of the TCN model with the published convolutional neural network and bi-directional long short-term memory (CNN-BiLSTM) model. The TCN model was compared with CNN-BiLSTM in the root mean square error (RMSE). The TCN model generally outperformed the existing CNN-BiLSTM model in terms of accuracy and computational cost. For the measured and public databases, the RMSE of the waveform using the TCN model was 0.55 ± 0.40 mmHg and 0.84 ± 0.29 mmHg, respectively. The training time of the TCN model was 9.63 min and 25.51 min for the entire training set; the average test time was around 1.79 ms and 8.58 ms per test pulse signal from the measured and public databases, respectively. The TCN model is accurate and fast for processing long input signals, and provides a novel method for measuring the aBP waveform. This method may contribute to the early monitoring and prevention of cardiovascular disease.

Secondary structure plays an important role in determining the function of noncoding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine learning (ML) technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on ML technologies and a tabularized summary of the most important methods in this field. The current pending challenges in the field of RNA secondary structure prediction and future trends are also discussed.

RNA plays an important role in various biological processes; hence, it is essential when determining the functions of RNA to research its secondary structures. So far, the accuracy of RNA secondary structure prediction remains an area in need of improvement. This paper presents a novel method for predicting RNA secondary structure based on an RNA folding simulation model. This model assumes that the process of RNA folding from the random coil state to full structure is staged and in every stage of folding, the final state of an RNA is determined by the optimal combination of helical regions, which are urgently essential to dynamics of RNA formation. This paper proposes the First Large Free Energy Difference (FLED) in order to find the helical regions most urgently needed for optimal final state formation among all the possible helical regions. Tests on the datasets with known structures from public databases demonstrate that our method can outperform other current RNA secondary structure prediction methods in terms of prediction accuracy.