Ribonucleic acids (RNAs) play a central role in cellular processes by interacting with proteins, small molecules, and other RNAs. Accurate prediction of these interactions is critical for understanding post-transcriptional regulation and advancing RNA-targeted therapeutics. However, existing computational methods are limited by their reliance on hand-crafted features, modality-specific architectures, and often require structural or physicochemical data, which are experimentally challenging to obtain and unavailable for many RNA molecules. These constraints hinder generalizability and fail to capture the complex, context-dependent semantics of RNA interactions. We present BioLLMNet, a unified sequence-only framework that leverages pretrained biological language models to encode rich, contextualized representations for both RNA molecules and their interacting partners, including proteins, small molecules, and other RNAs. Our key innovation is the introduction of a novel learnable gating mechanism, which dynamically computes feature-wise weights to adaptively integrate multimodal embeddings based on input context. This mechanism, proposed here for the first time in RNA interaction modeling, enables the model to emphasize the most informative features from each partner and achieves seamless fusion of heterogeneous modalities. As a result, BioLLMNet represents a unified framework and can flexibly and consistently model all three types of interaction (RNA-protein, RNA-small molecule, and RNA-RNA) within a shared architecture, eliminating the need for modality-specific designs. Comprehensive evaluations on benchmark data sets demonstrate that BioLLMNet achieves state-of-the-art performance across all three types of interaction. Our results underscore the power of language model-based representations combined with dynamic feature fusion for generalizable, modality-aware RNA interaction prediction.

The subcellular localization of messenger RNAs (mRNAs) plays a crucial role in gene regulation, ensuring precise spatial and temporal control of protein synthesis. Traditional computational approaches for mRNA localization have primarily relied on single-label classification models, which fail to capture the inherent multi-label nature of mRNA localization. Recent advancements have introduced deep learning-based multi-label prediction frameworks; however, existing methods often lack an effective way to model the relationships between multiple localizations. In this paper, we propose Localization with Supervised Contrastive Learning (LOCAS), a novel approach for multi-label mRNA subcellular localization prediction. LOCAS integrates an RNA language model (RiNALMo) to generate high-quality sequence embeddings and employs supervised contrastive learning (SCL) to refine the embedding space, ensuring biologically meaningful clustering of RNA sequences. To handle overlapping labels, we introduce an overlap-threshold-based similarity measure during contrastive training. Finally, we leverage an ML-Decoder, which utilizes a cross-attention mechanism to enhance multi-label classification performance. We evaluate LOCAS on two benchmark datasets, RNALocate and RNALocate V2.0, demonstrating state-of-the-art performance across all evaluation metrics. Extensive ablation studies validate the effectiveness of our approach, highlighting the contributions of contrastive learning and ML-decoder in improving multi-label classification. Our results suggest that integrating RNA sequence representation learning with SCL offers a powerful and scalable solution for mRNA localization prediction.

RNA torsion and pseudo-torsion angles are critical in determining the three-dimensional conformation of RNA molecules, which in turn governs their biological functions. However, current methods are limited by RNA's structural complexity as well as flexibility, with experimental techniques being costly and computational approaches struggling to capture the intricate sequence dependencies needed for accurate predictions. To address these challenges, we introduce DeepRNA-Twist, a novel deep learning framework designed to predict RNA torsion and pseudo-torsion angles directly from sequence. DeepRNA-Twist utilizes RNA language model embeddings, which provides rich, context-aware feature representations of RNA sequences. Additionally, it introduces 2A3IDC module (Attention Augmented Inception Inside Inception with Dilated CNN), combining inception networks with dilated convolutions and multi-head attention mechanism. The dilated convolutions capture long-range dependencies in the sequence without requiring a large number of parameters, while the multi-head attention mechanism enhances the model's ability to focus on both local and global structural features simultaneously. DeepRNA-Twist was rigorously evaluated on benchmark datasets, including RNA-Puzzles, CASP-RNA, and SPOT-RNA-1D, and demonstrated significant improvements over existing methods, achieving state-of-the-art accuracy. Source code is available at https://github.com/abrarrahmanabir/DeepRNA-Twist.

Localizing macromolecules in crowded cellular cryo-electron tomography (cryo-ET) images or tomograms is crucial for determining their in situ structures. Traditional template matching-based approaches for this task suffer from template-specific biases and have low throughput. Given these problems, learning-based solutions are necessary. However, the paucity of annotated data for training poses substantial challenges for such learning-based methods. Moreover, preparing extensively annotated cellular tomograms for training macromolecule localization methods is extremely time-consuming and burdensome due to the large volume and low signal-to-noise ratio of the tomograms. In this work, we developed TomoPicker, an annotation-efficient macromolecule localization method for tomograms. To achieve such annotation-efficiency, TomoPicker regards macromolecule localization as a voxel classification problem and solves it with two different positive-unlabeled learning approaches. We evaluated TomoPicker on two experimental cryo-ET datasets of crowded eukaryotic cells and one experimental dataset of relatively less crowded prokaryotic cell. We observed that, with only 10 annotated macromolecule locations, TomoPicker with positive unlabeled learning achieved a performance comparable to that of state-of-the-art supervised methods trained with several hundred annotations. In other words, TomoPicker achieved plausible segmentation with up to 98% less data compared with supervised learning-based methods. Furthermore, it demonstrated substantial improvements over existing learning-based macromolecule localization methods under sparse annotation scenarios.

Automated classification of cardiovascular diseases from electrocardiogram (ECG) signals using deep learning has gained significant interest due to its wide range of applications. However, existing deep learning approaches often overlook inter-channel shared information or lose time-sequence dependent information when considering 1D and 2D ECG representations, respectively. Moreover, besides considering spatial dimension, it is necessary to understand the context of the signals from a global feature space. We propose MD-CardioNet, an efficient deep learning architecture that captures temporal, spatial, and volumetric features from multi-lead ECG signals using multidimensional (1D, 2D, and 3D) convolutions to address these challenges. Sequential feature extractors capture time-dependent information, while a 2D convolution is applied to form an image representation from the multi-channel ECG signal, extracting inter-channel features. Additionally, a volumetric feature extraction network is designed to incorporate intra-channel, inter-channel, and inter-filter global space information. To reduce computational complexity, we introduce a practical knowledge distillation framework that reduces the number of trainable parameters by up to eight times ( from 4,304,910 parameters to 94,842 parameters) while maintaining satisfactory performance compatible with the other existing approaches. The proposed architecture is evaluated on a large publicly available dataset containing ECG signals from over 10,000 patients, achieving an accuracy of 97.3% in classifying six heartbeat rhythms. Our results surpass the performance of some state-of-the-art approaches. This paper presents a novel deep-learning approach for ECG classification that addresses the limitations of existing methods. The experimental results highlight the robustness and accuracy of MD-CardioNet in cardiovascular disease classification, offering valuable insights for future research in this field.

Derailments present a frequent phenomenon in several developing countries, which result in massive loss of property along with death tolls. For preventing derailments, a real-time automated system is needed to detect uprooted or faulty rail blocks. One of the solutions in this context is to sense the vibration of the rail track having an incoming train and transmit the information to the train notifying it about the condition of the rail track ahead. However, existing studies in this regard are yet to present a pragmatic solution that enables much-demanded long-distance networking to transmit the sensed data. The demand for long-distance network communication between the sensor nodes and the incoming train is unavoidable, as stopping the train after sensing an uprooted or faulty rail block ahead needs a considerable response time and distance. Therefore, in this paper, we develop a low-cost, long-range, and highly reliable mobile multi-hop networking scheme to successfully transmit data sensed from rail tracks to an approaching train at a distance of around 2000m. By considering the effect of Fresnel’s Region in our study, we determine the suitable placement of the networking module on the rail track, which leads us to achieve a delivery ratio of more than 99%. We confirm this finding through rigorous experiments over a real testbed scenario enabling mobile multi-hop networking.