Interdisciplinary Sciences: Computational Life Sciences最新文献_第9页

Construction and Application of Traditional Chinese Medicine Knowledge Graph Based on Large Language Model. 基于大语言模型的中药知识图谱构建与应用

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-06-01 Epub Date: 2025-07-02 DOI: 10.1007/s12539-025-00735-1

Bo Zhang, Ruifang Li, Kedong Yin, Shuo Hua, Shiyu Li, Mengwan Jiang, Haoping An, Peng Li

{"title":"Construction and Application of Traditional Chinese Medicine Knowledge Graph Based on Large Language Model.","authors":"Bo Zhang, Ruifang Li, Kedong Yin, Shuo Hua, Shiyu Li, Mengwan Jiang, Haoping An, Peng Li","doi":"10.1007/s12539-025-00735-1","DOIUrl":"10.1007/s12539-025-00735-1","url":null,"abstract":"Traditional Chinese Medicine (TCM) is a vital component of the Chinese heritage, embodying a wealth of medical knowledge and distinctive therapeutic practices. A critical challenge in TCM modernization lies in extracting essential information from its complex and diverse knowledge system to develop knowledge-based services, which represents a cutting-edge research focus. This study proposes a Large Language Model (LLM)-driven approach for structuring TCM knowledge integrating historical TCM texts with open-source TCM datasets. A Fine-Tuning ChatGLM3-6B (FT-ChatGLM3) model was developed on the AliCloud DSW platform, optimized specifically for Chinese-language processing to enhance semantic understanding and knowledge extraction within TCM contexts. FT-ChatGLM3 powers an intelligent TCM Q&A system, significantly improving the accuracy and efficiency of diagnosis and therapeutic recommendations. Furthermore, a BERT-based TCM Entity Recognition (TCMER) model was developed, and a knowledge graph was constructed using FT-ChatGLM3's outputs. Experimental results demonstrate that FT-ChatGLM3 achieves strong performance in TCM applications, delivering precise diagnosis and treatment suggestions. The TCMER model also exhibits high efficacy, facilitating the systematization and structuring of TCM knowledge, while improving knowledge retrieval and consistency. The integration of FT-ChatGLM3 and TCMER not only accelerates the development of TCM knowledge graphs but also advances TCM modernization and its intelligent application in global healthcare.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"734-746"},"PeriodicalIF":3.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144553459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

m⁶ADP-GCNPUAS: m⁶A-Disease Prediction via Graph Convolutional Network and Positive-Unlabeled Learning with Self-Adaptive Sampling. m6ADP-GCNPUAS：基于图卷积网络和自适应采样的正无标记学习的m6a -疾病预测。

IF 5.2 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-06-01 Epub Date: 2025-08-30 DOI: 10.1007/s12539-025-00760-0

Teng Zhang, Lian Liu

{"title":"m6ADP-GCNPUAS: m6A-Disease Prediction via Graph Convolutional Network and Positive-Unlabeled Learning with Self-Adaptive Sampling.","authors":"Teng Zhang, Lian Liu","doi":"10.1007/s12539-025-00760-0","DOIUrl":"10.1007/s12539-025-00760-0","url":null,"abstract":"Cumulative studies have revealed that RNA N6-methyladenosine (m6A) modification plays a critical role in various human complex diseases including cancers. Exploring the relationship between m6As and diseases can reveal the molecular mechanism of disease pathogenesis. However, the known associations between m6As and diseases are very rare due to the time-consuming and laborious biological experiments. Some computational approaches have been proposed to predict the potential m6A-disease associations. However, these methods are restricted to specific application scenarios. To solve this limitation, we propose a novel computational framework, named m6A-Disease Prediction using Graph Convolutional Networks and Positive-Unlabeled Learning with Self-Adaptive Sampling (m6ADP-GCNPUAS), to accurately predict m6A-disease associations. m6ADP-GCNPUAS effectively captures the embedded features of m6A sites or diseases using GCN. Given the very limited m6A-disease associations, m6ADP-GCNPUAS adopts the PUAS framework to augment the potential positive samples, which improves the predictive power for m6A-disease associations. The experimental results indicate that m6ADP-GCNPUAS achieves over 0.99 across six evaluation metrics and PUAS significantly improves the predicted performance. In addition, case studies demonstrate that the predicted cancer-associated m6A sites participate in the regulation of cancer development and some predicted m6A genes could be the hallmarks for different cancers.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"564-578"},"PeriodicalIF":5.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144953027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AMPred-MFG: Investigating the Mutagenicity of Compounds Using Motif-Based Graph Combined with Molecular Fingerprints and Graph Attention Mechanism. AMPred-MFG：基于基序图结合分子指纹和图注意机制研究化合物的致突变性。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-06-01 Epub Date: 2025-07-16 DOI: 10.1007/s12539-025-00742-2

Jinpeng Hu, Xin Yang, Chenggui Yao, Mingxuan Zhang, Shihang Shen, Lijie Na, Qi Zhao

{"title":"AMPred-MFG: Investigating the Mutagenicity of Compounds Using Motif-Based Graph Combined with Molecular Fingerprints and Graph Attention Mechanism.","authors":"Jinpeng Hu, Xin Yang, Chenggui Yao, Mingxuan Zhang, Shihang Shen, Lijie Na, Qi Zhao","doi":"10.1007/s12539-025-00742-2","DOIUrl":"10.1007/s12539-025-00742-2","url":null,"abstract":"Accurate mutagenicity prediction is crucial in drug discovery, but evaluating the mutagenicity of drug molecules is technically challenging, and experimental mutagenicity tests are both time-consuming and costly. In this study, we introduce a new deep learning predictive model called AMPred-MFG, designed to predict the mutagenicity of drug molecules. Our approach first combines multiple molecular fingerprint features with molecular graph features to obtain a comprehensive molecular representation. Next, considering the significance of molecular substructures on mutagenicity, we decompose the molecules into motifs to form motif-based graph. We use a graph transformer to extract features from these motif-based graphs and fuse motif graph features with molecular fingerprint features and molecular graph features to create the final molecular representation. Finally, we use a multilayer perceptron to classify the compounds into mutagens and non-mutagens. We evaluate the performance of AMPred-MFG through ten-fold cross-validation experiments and validate its robustness on external validation datasets. By comparing with several state-of-the-art algorithms, AMPred-MFG achieves the best results in terms of performance, with AUC value of 0.912, ACC of 0.835, SEN of 0.849, NPV of 0.811, PPV of 0.854, MCC of 0.665. In addition, we use attention scores to identify molecular fragments related to mutagenicity, highlighting the interpretability of AMPred-MFG. We believe that AMPred-MFG can act as a dependable tool for predicting mutagenicity, allowing for the assessment of both mutagens and non-mutagens during the early phases of drug development. AMPred-MFG is freely available at https://github.com/zhaoqi106/AMPred-MFG .","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"471-487"},"PeriodicalIF":3.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144649379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IHDFN-DTI: Interpretable Hybrid Deep Feature Fusion Network for Drug-Target Interaction Prediction. 用于药物-靶标相互作用预测的可解释混合深度特征融合网络。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-06-01 Epub Date: 2025-05-12 DOI: 10.1007/s12539-025-00712-8

Yuanyuan Zhang, Qihao Wang, Ci'ao Zhang, Baoming Feng, Junliang Shang, Li Zhang

{"title":"IHDFN-DTI: Interpretable Hybrid Deep Feature Fusion Network for Drug-Target Interaction Prediction.","authors":"Yuanyuan Zhang, Qihao Wang, Ci'ao Zhang, Baoming Feng, Junliang Shang, Li Zhang","doi":"10.1007/s12539-025-00712-8","DOIUrl":"10.1007/s12539-025-00712-8","url":null,"abstract":"Conventional drug discovery is expensive and takes a long period. Drug-target interaction (DTI) prediction through computational methods significantly improves efficiency and reduces costs, holding substantial research value. Despite progress in existing prediction methods, two major challenges remain: first, most methods fail to effectively combine shallow and deep features of protein sequences, overlooking the synergistic effect of both; second, existing feature fusion techniques are relatively simple and struggle to fully capture the complexity and richness of fused features. We suggest an interpretable hybrid deep feature fusion network (IHDFN) as a solution to these problems. In the hybrid deep feature extraction module for protein sequences, shallow and deep features of protein sequences are extracted through two distinct views respectively, which capture multi-level information of proteins comprehensively. To further enhance the feature fusion effect, we introduce the StarNet fusion model in this module, enabling efficient fusion of shallow and deep features and enriching feature representation. To further improve the representation power of drug characteristics and the stability of the model, we use a graph convolutional network (GCN) in the drug feature extraction section in conjunction with residual connections and layer normalization. Furthermore, by integrating multimodal features from drugs and proteins utilizing an attention mechanism in the heterogeneous feature fusion module, we increase the complexity of features and achieve interpretability in predictions by attention focusing. Finally, we experimented on three datasets, and the findings indicate that IHDFN has exceptional performance and robustness compared to other cutting-edge techniques, underscoring its great promise and usefulness in DTI tasks. The code for this study is available on GitHub at https://github.com/wangqhfff/IHDFN.git .","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"687-701"},"PeriodicalIF":3.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144024434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interpretable Multi-task Analysis of Single-Cell RNA-seq Data Through Topological Structure Preservation and Data Denoising. 基于拓扑结构保存和数据去噪的单细胞RNA-seq数据可解释多任务分析。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-06-01 Epub Date: 2025-09-29 DOI: 10.1007/s12539-025-00765-9

Shengpeng Yu, Zihan Yang, Tianyu Liu, Cheng Liang, Hong Wang

{"title":"Interpretable Multi-task Analysis of Single-Cell RNA-seq Data Through Topological Structure Preservation and Data Denoising.","authors":"Shengpeng Yu, Zihan Yang, Tianyu Liu, Cheng Liang, Hong Wang","doi":"10.1007/s12539-025-00765-9","DOIUrl":"10.1007/s12539-025-00765-9","url":null,"abstract":"The advent of single-cell transcriptome sequencing (scRNA-seq) has revolutionized our ability to analyze gene expression at the individual cell level, overcoming the limitations of bulk RNA sequencing. However, the explosive growth of scRNA-seq data and the prevalence of dropout events pose significant challenges for downstream analysis. Existing methodologies often focus on isolated tasks, such as identifying cell communities, processing dropout events, and mitigating batch effects, neglecting collaborative multi-task analysis, and introducing new noise during dropout event handling. In response to these challenges, we propose scIMTA (interpretable multi-task analysis of single-cell), an advanced framework designed to enhance interpretability and effectively address the issues of topological structure preservation and dropout events. The key innovations of scIMTA are that scIMTA enables collaborative multi-task analysis of sparse, high-noise gene expression data, enhances interpretability through biological grounding, robustly handles dropout events by preserving data integrity, and demonstrates efficacy and generalizability through rigorous validation on breast cancer scRNA-seq datasets. scIMTA establishes a new framework for collaborative multi-task analysis, interpretability, and robust dropout handling in single-cell transcriptome studies. This work significantly advances the field and allows a more nuanced exploration of cellular heterogeneity and gene expression dynamics. The source code of scIMTA is available for download at https://github.com/ShengPengYu/scIMTA .","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"434-450"},"PeriodicalIF":3.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145191500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dear-OMG: An Omics-General Compression Method for Genomics, Proteomics and Metabolomics Data. 用于基因组学、蛋白质组学和代谢组学数据的组学通用压缩方法。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-06-01 Epub Date: 2025-12-04 DOI: 10.1007/s12539-025-00786-4

Qingzu He, Xiang Li, Huan Guo, Yulin Li, Jianwei Shuai

{"title":"Dear-OMG: An Omics-General Compression Method for Genomics, Proteomics and Metabolomics Data.","authors":"Qingzu He, Xiang Li, Huan Guo, Yulin Li, Jianwei Shuai","doi":"10.1007/s12539-025-00786-4","DOIUrl":"10.1007/s12539-025-00786-4","url":null,"abstract":"As high-throughput omics technologies continue to advance, researchers are facing the challenge of a rapid surge in proteomics, metabolomics, and genomics data. This growth not only necessitates more disk space and network bandwidth, but also complicates data sharing and subsequent analysis. To address this challenge and enhance the analytical efficiency of downstream software, we propose Dear-OMG, a unified, compact, flexible, and high-performance metadata storage solution. Dear-OMG introduces a novel file storage structure and utilizes the Elias-Fano encoding algorithm to compress and store proteomics, genomics, and metabolomics metadata into the unified OMG format. The OMG format not only demonstrates remarkably high compression and decompression speeds, but also enables parallel random access to any data block. Test results reveal that, compared to the commonly used proteomics formats of mzXML and mzML, the OMG format achieves an 80% reduction in storage space, a 90% decrease in conversion time, and approximately a tenfold speed improvement with support for parallel random access. Dear-OMG is freely available at https://github.com/jianweishuai/Dear-OMG .","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"551-563"},"PeriodicalIF":3.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145677573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GraphTransDTA: Drug-Target Affinity Prediction with Graph Transformer for Multimodal Data Fusion. GraphTransDTA：基于多模态数据融合的图转换器药物-靶标亲和力预测。

IF 5.2 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-06-01 Epub Date: 2026-04-29 DOI: 10.1007/s12539-026-00823-w

Zihang Yuan, Fang Zheng, Yujin Ji, Juanjuan Zhao, Yan Qiang, Long Wang, Qianqian Yang

{"title":"GraphTransDTA: Drug-Target Affinity Prediction with Graph Transformer for Multimodal Data Fusion.","authors":"Zihang Yuan, Fang Zheng, Yujin Ji, Juanjuan Zhao, Yan Qiang, Long Wang, Qianqian Yang","doi":"10.1007/s12539-026-00823-w","DOIUrl":"10.1007/s12539-026-00823-w","url":null,"abstract":"Deep learning technology has become a pivotal tool in the domain of computer-aided drug design and shows promising prospects in pharmaceutical research. Among the numerous tasks involved in drug development, drug–target affinity (DTA) prediction is of particular importance, since it expedites drug discovery and reduces resource consumption. With the fast progress of deep learning techniques, applying these techniques to enhance DTA prediction accuracy has emerged as a prominent research focus. Drugs and protein targets can be described using various representations, such as approaches grounded in structure, sequences, and graphs. We leverage the structural features of both compound molecules and proteins to generate graph representations that encode drug molecules and proteins, respectively. Building upon recent developments in graph neural networks, we employ a graph Transformer framework to extract graph representation information. In addition, we integrate SMILES based compound descriptions with textual protein features to propose a model named GraphTransDTA for effective DTA prediction. This approach fully utilizes the structural features of compound molecules and proteins, adeptly handles multi-feature nodes in heterogeneous graphs through the graph Transformer. Node level information aggregation is achieved via the multi-head attention mechanism. Moreover, the integration of multimodal data including textual features from both SMILES and protein sequences further improves the predictive accuracy and robustness of DTA models. By retaining the expressive power of deep neural networks and exploiting the integrative nature of various data modalities, the proposed method offers a novel perspective for accurate drug discovery. ","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"579-594"},"PeriodicalIF":5.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147770651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Adaptive Multi-Stage and Adjacent-Level Feature Integration Network for Brain Tumor Image Segmentation. 一种用于脑肿瘤图像分割的自适应多阶段邻接层特征集成网络。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-06-01 Epub Date: 2025-08-14 DOI: 10.1007/s12539-025-00748-w

Jiwen Zhou, Yulun Wu, Yue Xu, Wanyu Liu

{"title":"An Adaptive Multi-Stage and Adjacent-Level Feature Integration Network for Brain Tumor Image Segmentation.","authors":"Jiwen Zhou, Yulun Wu, Yue Xu, Wanyu Liu","doi":"10.1007/s12539-025-00748-w","DOIUrl":"10.1007/s12539-025-00748-w","url":null,"abstract":"The segmentation of brain tumor magnetic resonance imaging (MRI) plays a crucial role in assisting diagnosis, treatment planning, and disease progression evaluation. Convolutional neural networks (CNNs) and transformer-based methods have achieved significant progress due to their local and global feature extraction capabilities. However, similar to other medical image segmentation tasks, challenges remain in addressing issues such as blurred boundaries, small lesion volumes, and interwoven regions. General CNN and transformer approaches struggle to effectively resolve these issues. Therefore, a new multi-stage and adjacent-level feature integration network (MAI-Net) is introduced to overcome these challenges, thereby improving the overall segmentation accuracy. MAI-Net consists of dual-branch, multi-level structures and three innovative modules. The stage-level multi-scale feature extraction (SMFE) module focuses on capturing feature details from fine to coarse scales, improving detection of blurred edges and small lesions. The adjacent-level feature fusion (AFF) module facilitates information exchange across different levels, enhancing segmentation accuracy in complex regions as well as small volume lesions. Finally, the multi-stage feature fusion (MFF) module further integrates features from various levels to improve segmentation performance in complex regions. Extensive experiments on BraTS2020 and BraTS2021 datasets demonstrate that MAI-Net significantly outperforms existing methods in Dice and HD95 metrics. Furthermore, generalization experiments on a public ischemic stroke dataset confirm its robustness across different segmentation tasks. These results highlight the significant advantages of MAI-Net in addressing domain-specific challenges while maintaining strong generalization capabilities.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"595-613"},"PeriodicalIF":3.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144855174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HPCSMN: A Classification Method of Chemotherapy Sensitivity of Hypopharyngeal Cancer Based on Multimodal Network. HPCSMN：一种基于多模式网络的下咽癌化疗敏感性分类方法。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-06-01 Epub Date: 2025-11-18 DOI: 10.1007/s12539-025-00783-7

Weiqi Fu, Haiyan Li, Xiongwen Quan, Xudong Wang, Wanwan Huang, Han Zhang

{"title":"HPCSMN: A Classification Method of Chemotherapy Sensitivity of Hypopharyngeal Cancer Based on Multimodal Network.","authors":"Weiqi Fu, Haiyan Li, Xiongwen Quan, Xudong Wang, Wanwan Huang, Han Zhang","doi":"10.1007/s12539-025-00783-7","DOIUrl":"10.1007/s12539-025-00783-7","url":null,"abstract":"The treatment of hypopharyngeal cancer faces complex challenges, and accurate prediction of chemotherapy sensitivity is crucial for personalized treatment. In this study, a multimodal fusion network based on deep learning was used to classify the chemotherapy sensitivity of hypopharyngeal cancer, and the prediction accuracy was improved by integrating 3D CT images and radiomic features. The preprocessed and enhanced 3D CT images were analyzed by 3D ResNet branches to extract spatial features; the radiomic features screened by LASSO regression were processed by three layers of fully connected branches to analyze the tabular data. The extracted vectors were fused by fully connected layers, using complementary advantages to capture complex spatial dependencies and detailed radiomic features. Experiments on the manually segmented NKU-TMU-hphc dataset (containing 102 hypopharyngeal cancer CT images) showed that the multimodal fusion network had high accuracy and outperformed single-modality methods and other models in multiple evaluation indicators. Statistical analysis was performed on the extracted features and clinical characteristics. The model effectively integrates image and clinical data, provides a new method for chemotherapy sensitivity classification, and is expected to improve personalized medicine.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"515-533"},"PeriodicalIF":3.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145549229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unveiling Putative Functions of Burkholderia pseudomallei K96243 Hypothetical Proteins Via High-Throughput Characterization of Structural Similarities. 通过结构相似性的高通量表征揭示伪伯克霍尔德菌K96243假设蛋白的假定功能。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-06-01 Epub Date: 2025-12-12 DOI: 10.1007/s12539-025-00792-6

Syed Abuthakir Mohamed Husain, Su Datt Lam, Mohd Firdaus-Raih, Sheila Nathan, Nor Azlan Nor Muhammad, Chyan Leong Ng

{"title":"Unveiling Putative Functions of Burkholderia pseudomallei K96243 Hypothetical Proteins Via High-Throughput Characterization of Structural Similarities.","authors":"Syed Abuthakir Mohamed Husain, Su Datt Lam, Mohd Firdaus-Raih, Sheila Nathan, Nor Azlan Nor Muhammad, Chyan Leong Ng","doi":"10.1007/s12539-025-00792-6","DOIUrl":"10.1007/s12539-025-00792-6","url":null,"abstract":"Burkholderia pseudomallei (BP) infections claims tens of thousands of lives worldwide every year. The bacterium's distinctive characteristics include antibiotic resistance, virulence and ability to survive in stressful environments. The B. pseudomallei genome sequencing and annotation reveal that about 25% of the genes encode hypothetical proteins (HPs). As such, characterising the HPs could shed light on the mechanisms that contribute to the above characteristics. Over the last decade, genome sequencing and annotation technologies have advanced drastically. Furthermore, artificial intelligence programs such as AlphaFold2 (AF2), RoseTTAFold2 (RF2), which can predict 3D protein structures with high accuracy, are also available. Taking advantage of the available tools, this study aimed to re-annotate HPs that are encoded within the BP genome. To achieve this, we retrieved 1869 HPs from the Burkholderia Genome Database, then cross-referenced with UniProt. After filtering, 419 remain hypothetical. These were analysed using BLASTp for sequence homologs and antibiotic resistance proteins, followed by 3D structure prediction using AF2 and RF2, and structural homolog search using Foldseek. This study successfully annotated 209 HPs with only 210 proteins (3.7% of BP coding sequences) still classified as 'hypothetical'. The functions of the predicted HPs were further analysed using structure comparison and active site analysis. The annotated protein list includes fifteen antibiotic resistance proteins, five haem oxygenase-like fold proteins involved in biofilm formation, host pathogenesis, and antibacterial activity, along with five essential proteins. These proteins represent promising drug targets for developing new antibiotics against melioidosis. Nonetheless, experimental validation will be necessary to characterize the predicted protein functions.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"534-550"},"PeriodicalIF":3.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145742336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0