Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 4 appraisals
Cell
Deep learning of functional perturbations from condensate morphology
Biomolecular condensates compartmentalize the interior of cells to organize complex functions, yet linking molecular interactions within condensates to their mesoscale organization remains a major challenge. To bridge this gap, we developed a neural-network-based framework-Deep-Phase (deep learning of phase-separated condensates)-that uses microscopy images to directly measure condensate morphology changes resulting from pharmacological alterations in associated biochemical processes. We use Deep-Phase to precisely quantify time- and concentration-dependent structural perturbations to the multiphase nucleolus and show that they are tightly coupled to potencies of drugs inhibiting ribosomal RNA (rRNA) transcription and processing. Applying Deep-Phase in a chemical screen, we identify a unique nucleolar morphology and discover a role for a DNA topoisomerase in rRNA processing. Mechanistic studies of this morphology provide insights into how the interfaces between nucleolar sub-compartments are maintained. We demonstrate Deep-Phase's adaptability to diverse cell lines, labeling techniques, and condensates, offering a powerful platform for connecting molecular pathways to cellular mesoscale organization.
25 July 2026
Read appraisal →Journal of chemical information and modeling
DeepKbhb: Context-Aware Prediction of Human Lysine β-Hydroxybutyrylation Sites
Lysine β-hydroxybutyrylation (Kbhb) is a metabolism-linked post-translational modification (PTM) that plays a critical role in regulating gene expression, stress responses, and disease progression. Despite its emerging biological significance, identifying Kbhb sites remains limited due to the cost and complexity of experimental methods. Prior work such as KbhbXG is constrained by its reliance on hand-crafted features and lacks the ability to model contextual dependencies within sequences. To address this challenge, we present DeepKbhb, a deep learning framework designed for human Kbhb site identification. By integrating sequence embeddings and six engineered descriptors through a bilinear attention network, DeepKbhb effectively captures position-dependent relationships essential for accurate Kbhb site prediction. On an independent test set, DeepKbhb achieved state-of-the-art performance with an accuracy of 0.856, an F1-score of 0.863, and a Matthews correlation coefficient of 0.716. Experimental results across multiple evaluation metrics confirm the superior performance of DeepKbhb, highlighting its potential as a valuable tool for advancing Kbhb-related functional and mechanistic studies. This capability can further support disease-oriented research, particularly in cancer, metabolic disorders, and immune regulation. Further sequence analyses revealed distinct local amino acid preferences, supporting the biological relevance of our model. The web interface is accessible at https://awi.cuhk.edu.cn/~DeepKbhb/.
14 July 2026
Read appraisal →Briefings in bioinformatics
EssTFNet: integration of adaptive time-frequency and DNA language models for interpretable human essential gene prediction
Essential genes are defined as indispensable for an organism's survival. The loss of function of these genes results in cell death or an inability to complete the normal life cycle. Research on essential genes is pivotal in elucidating the origin and evolution of life, as well as in identifying potential therapeutic targets. Therefore, predicting essential genes is of great scientific importance and has many applications in basic research and the biomedical field. In this study, we propose EssTFNet, a novel, interpretable deep learning framework that combines adaptive time-frequency analysis with a DNA language model to achieve accurate prediction of human essential genes while enabling mechanistic biological interpretation. EssTFNet leverages the architecture of ATFNet, which maps DNA and protein sequences into equivalent time-series signals to extract periodic and nonstationary features, enhancing the model's capacity to capture complex sequence patterns. Through feature selection and architectural optimization, EssTFNet achieves a favorable balance among prediction accuracy, model interpretability, and cross-tissue generalization. On the S1 benchmark task, EssTFNet outperformed mainstream sequence-based deep learning methods, achieving an area under the curve of 0.9679 and an area under the precision-recall curve of 0.8491. Additionally, the DeepLIFT attribution method was employed to identify functional motifs associated with gene essentiality, offering valuable insights for experimental validation. For the convenience of researchers, we have developed an easy-to-use web server and made it along with the source code in a GitHub repository: https://github.com/QIANJINYDX/EssTFNet. Overall, this study presents a potentially useful methodological framework for human essential genes prediction, which could provide valuable insights for future research and applications in this field.
5 July 2026
Read appraisal →ACS nano
The Use of Deep Learning in RNA Therapeutic Development
Ribonucleic acid (RNA)-based therapeutics have emerged as promising methods of disease treatment due to their ability to target the human genome and influence protein production, their versatility, and their relative lack of toxicity compared to other gene therapies. However, the RNA therapeutic design space is extremely large, encompassing multiple variables, including codon identities, secondary structure, and design of specific regions. RNA therapeutic optimization is difficult due to the impracticality of exploring such a vast design space experimentally. To address this limitation, deep learning methods have been employed to optimize RNA therapeutic development. In this review, we examine the application of deep learning models across three key aspects of RNA therapeutic development (RNA structure prediction, CRISPR activity, and RNA delivery), highlighting major contributions in these fields and analyzing how deep learning model architectures could affect model performance. We then discuss challenges associated with using deep learning for RNA therapeutics, such as computational and data limitations. Finally, we offer perspectives on areas for future exploration, such as emerging model architectures and methods of integration with more advanced high-throughput screening techniques. Ultimately, this review provides an overview of how deep learning is used in RNA therapeutic development and how it can evolve in the future.
24 June 2026
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