Multi-Modal Interpretable Representation for Non-Coding RNA Classification and Class Annotation

Non-coding RNAs (ncRNAs) play critical roles in cellular processes and disease development. Although genome sequencing projects have revealed a vast number of non-coding genes, the functional classification of ncRNAs remains a complex and challenging issue. The diversity, complexity, and functionality of ncRNAs make them important subjects in biome...

DeepES: Deep Learning-Based Enzyme Screening for Identifying Orphan Enzyme Genes

Academic Background With the rapid advancement of sequencing technology, scientists have been able to obtain a vast amount of protein sequence data, including many enzyme sequences. However, despite the establishment of large enzyme databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) and BRENDA, sequence information for many enzyme...

CryoTEN: Efficiently Enhancing Cryo-EM Density Maps Using Transformers

Academic Background Cryogenic Electron Microscopy (Cryo-EM) is a crucial experimental technique for determining the structures of macromolecules such as proteins. However, the effectiveness of Cryo-EM is often hindered by noise and missing density values caused by experimental conditions such as low contrast and conformational heterogeneity. Althou...

Sul-BERTGRU: An Ensemble Deep Learning Method Integrating Information Entropy-Enhanced BERT and Directional Multi-GRU for S-Sulfhydration Sites Prediction

Background Introduction Post-Translational Modifications (PTMs) are crucial mechanisms for regulating cellular activities, including gene transcription, DNA repair, and protein interactions. Among these, cysteine, a rare amino acid, participates in various PTMs through its thiol group, playing a significant role in redox balance and signal transduc...

APNet: An Explainable Sparse Deep Learning Model to Discover Differentially Active Drivers of Severe COVID-19

Academic Background The COVID-19 pandemic has had a significant impact on global public health systems. Although the pandemic has somewhat subsided, its complex immunopathological mechanisms, long-term sequelae (such as “long COVID”), and the potential for similar threats in the future continue to drive in-depth research. Severe COVID-19 cases are ...

Deep Learning to Quantify the Pace of Brain Aging in Relation to Neurocognitive Changes

As the global aging problem intensifies, the incidence of neurodegenerative diseases (such as Alzheimer’s Disease, AD) is increasing year by year. Brain aging (Brain Aging, BA) is one of the significant risk factors for neurodegenerative diseases, but it does not completely align with chronological age (Chronological Age, CA). Traditional methods f...

Spectro-Temporal Modulations Incorporated Two-Stream Robust Speech Emotion Recognition

Research on Two-Stream Robust Speech Emotion Recognition Based on Spectro-Temporal Modulation Features Academic Background Speech Emotion Recognition (SER) is a technology that identifies emotions by analyzing the emotional content in human speech. It has broad application potential in areas such as human-computer interaction, customer service mana...

Multi-Task Aquatic Toxicity Prediction Model Based on Multi-Level Features Fusion

Academic Background With the growing threat of organic compounds to environmental pollution, studying the toxic responses of different aquatic organisms to these compounds has become crucial. Such research not only helps assess the potential ecological impacts of pollutants on the overall aquatic ecosystem but also provides significant scientific f...

A Spatiotemporal Style Transfer Algorithm for Dynamic Visual Stimulus Generation

Research Report on the Spatiotemporal Style Transfer Algorithm for Dynamic Visual Stimulus Generation Academic Background The encoding and processing of visual information has been a significant focus in the fields of neuroscience and vision science. With the rapid development of deep learning techniques, investigating the similarities between arti...

Comprehensive Prediction and Analysis of Human Protein Essentiality Based on a Pretrained Large Language Model

Comprehensive Prediction and Analysis of Human Protein Essentiality Based on a Pretrained Large Language Model Academic Background Human Essential Proteins (HEPs) are crucial for individual survival and development. However, experimental methods for identifying HEPs are often costly, time-consuming, and labor-intensive. Additionally, existing compu...