Interdisciplinary Sciences: Computational Life Sciences最新文献

{"title":"Relevance of 3D Rotationally Equivariant Neural Networks for Predicting Protein-Ligand Binding Affinities.","authors":"Gaili Li, Yongna Yuan, Ruisheng Zhang","doi":"10.1007/s12539-025-00745-z","DOIUrl":"https://doi.org/10.1007/s12539-025-00745-z","url":null,"abstract":"<p><p>Proteins are fundamental to biological processes, mediating critical functions through precise molecular interactions. The rotational dynamics between ligand atoms and protein binding sites can significantly influence interaction efficacy by modifying spatial relationships. In our research, we present the PLAe (three-dimensional (3D) rotationally equivariant neural networks for predicting protein-ligand binding affinities) methodology. This novel model synergizes radial basis functions with e3nn networks to encapsulate the radial and angular dimensions of molecular features. Radial basis functions effectively measure interatomic distances, while e3nn-an advanced neural network utilizing spherical harmonics-maintains invariance under rotational and translational transformations. The Clebsch-Gordan coefficients are employed to integrate angular and atomic properties seamlessly. By merging radial basis and spherical harmonic elements with Clebsch-Gordan representations, our approach adeptly captures molecular rotational symmetries and interatomic interactions. The inclusion of an attention mechanism further refines the affinity predictions, ensuring a high level of precision. This integrative and sophisticated model sets a new benchmark to accurately predict protein-ligand binding affinities, leveraging intricate molecular details to enhance predictive performance.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144855175","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}

{"title":"ABEEM/MM Magnesium Force Field for Proteins and Aqueous Solutions.","authors":"Jing Zhang, Linan Lu, Runqiang Yu, Linlin Liu, Lei Wang, Cui Liu, Lidong Gong, Zhongzhi Yang","doi":"10.1007/s12539-025-00746-y","DOIUrl":"https://doi.org/10.1007/s12539-025-00746-y","url":null,"abstract":"<p><p>Magnesium is an essential element involved in diverse life activities. The strong polarization and significant charge transfer effects pose challenges to the traditional fixed charge force fields. Here we establish the ABEEM/MM magnesium force field for proteins and aqueous solutions. The interaction potentials of magnesium with water and proteins are treated as the ABEEM/MM bonded model (ABEEM-BM) in the Morse potential function form. Based on quantum mechanical (QM) results, the related parameters are optimized and determined. The charge distributions of model molecules from ABEEM-BM and the ABEEM/MM nonbonded model (ABEEM-NBM) agree well with the QM results. The potential energy surfaces (PESs) for bond stretching and angle bending between magnesium and ligands by ABEEM-BM have a good consistency with those from QM. Molecular dynamics (MD) simulations of 40 aqueous magnesium protein segments are carried out using ABEEM-BM, ABEEM-NBM, OPLS-AA, AMBER99, and CHARMM22 force fields. The root mean square deviations (RMSDs) for bond length and angle by ABEEM-BM are 0.088 Å and 5.99°, respectively, which are smaller than those from the others. MD simulations of aqueous magnesium solutions are carried out using ABEEM-BM and ABEEM-NBM. The radial and angular distribution functions from ABEEM-BM reproduce the best structural properties, and the rate constant is 4.7 × 10⁵ s^- 1. Moreover, the dynamic changing picture of charge transfer and the coordination number (CN) during water exchange processes is presented by ABEEM model. The overall performance of ABEEM models is evidently better than those from fixed charge force fields.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144855173","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}

{"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":"https://doi.org/10.1007/s12539-025-00748-w","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-08-14","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}

{"title":"Diffusion Model-Based Multi-Channel EEG Representation and Forecasting for Early Epileptic Seizure Warning.","authors":"Zekun Jiang, Wei Dai, Qu Wei, Ziyuan Qin, Rui Wei, Mianyang Li, Xiaolong Chen, Ying Huo, Jingyun Liu, Kang Li, Le Zhang","doi":"10.1007/s12539-025-00750-2","DOIUrl":"https://doi.org/10.1007/s12539-025-00750-2","url":null,"abstract":"","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144821314","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}

{"title":"BEST: Basic Embedding Search Tool Enhancing Discovery of Novel Enzyme.","authors":"Yuxuan Wu, Xiao Yi, Yang Tan, Huiqun Yu, Guisheng Fan, Gaowei Zheng","doi":"10.1007/s12539-025-00753-z","DOIUrl":"https://doi.org/10.1007/s12539-025-00753-z","url":null,"abstract":"<p><p>The identification of protein homologs in large databases is critical for biological advancements. Traditional methods, such as protein sequence alignment, often miss remote homologs. To address this limitation, we present the Basic Embedding Search Tool (BEST), a fast and sensitive approach that employs protein language models to create sequence embeddings enriched with evolutionary and structural information. Besides, we introduce a segmented distillation pruning technique to accelerate sequence encoding and develop a multi-layer acceleration structure to achieve a 4290.86-fold speedup in swift access and retrieval of dense vectors. Extensive experiments on real datasets demonstrate that BEST increases sensitivity by over 20% compared to prior methods while maintaining precision and recall. It operates 23.41 times faster than traditional tools like PSI-BLAST and 3.92 times faster than Foldseek, while also detecting homologous sequences that conventional methods miss. BEST and its open-access web server ( http://pm2s.cpolar.top/best1/ ) are poised to significantly aid enzyme mining and advance biological research. The code is publicly available at https://github.com/SkyTai-W/ProteinMiningEvaluator .</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144821313","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}

{"title":"SANNO: A Graph-Transformer Enhanced Optimal Transport Tool for Spatial Transcriptomic Annotation.","authors":"Yuansong Zeng, Yuanze Chen, Ningyuan Shangguan, Wenbing Li, Xiaoming Cai, Hongyu Zhang, Zheng Wang, Huiying Zhao","doi":"10.1007/s12539-025-00752-0","DOIUrl":"https://doi.org/10.1007/s12539-025-00752-0","url":null,"abstract":"<p><p>The latest progress in spatial transcriptomics has empowered scientists to investigate spatial heterogeneity with single-cell precision. A pivotal yet demanding aspect of spatial transcriptomics data analysis is cell type annotation. However, current methods exhibit limited performance as they are primarily designed for scRNA-seq data. Especially, these approaches often neglect spatial coordinate information and encounter challenges in identifying novel cell types. Here, we introduce SANNO, a novel approach that employs Optimal Transport (OT) to concurrently identify both known and novel cell types in spatially resolved single-cell data. Specifically, SANNO leverages a graph-Transformer module to model spatial coordinates and gene expression. This produces unified representations for both reference and query data. Building on this, SANNO employs a dual-strategy classifier. The first is an Unbalanced Optimal Transport (UOT) module that aligns query data with reference prototypes. The second is a self-supervised OT-based module that enhances global cluster separation and local cellular consistency, effectively eliminating batch effects. To further improve prediction accuracy, SANNO integrates an entropy-based re-weighted loss function. This significantly boosts the confidence of query cell predictions. Comprehensive experiments reveal that SANNO surpasses state-of-the-art techniques across both intra- and cross-spatial datasets, particularly in the identification of novel cell types. Additionally, SANNO demonstrates commendable performance in annotating cells within single-cell data, underscoring its potential as a versatile tool for cell annotation across single-cell and spatial transcriptomics datasets.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144821315","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}

{"title":"Parameter Estimation in Cellular Radiation Effects Using PSO-SQP and GA-SQP Hybrid Methods.","authors":"Dalal Y Alzahrani, F M Siam, F A Abdullah","doi":"10.1007/s12539-025-00736-0","DOIUrl":"https://doi.org/10.1007/s12539-025-00736-0","url":null,"abstract":"<p><p>Despite the current developments in mathematical modelling of biological process, some phenomena such as those encountered with the aspects of cell populations remain poorly understood. Fractional differential equations (FDEs) recently have received a significant amount of attention and demonstrated its rigor in representing real-world problems as opposed to traditional differential equations. In the present work, a systematic investigation using a mathematical approach dealing with the effects of ionizing radiation and using FDEs is proposed to illuminate some biological properties of the cell populations. For this purpose, the theoretical revelation of the cells population memory was treated within the context of FDEs, where the Mittag-Leffler function and Caputo derivatives are used to consider genetic potentials and memory traces. The model verification based on the parameter estimation algorithms is then accomplished by the implementation of two evolutionary hybrid optimization methods, namely the genetic algorithm-sequential quadratic programming (GA-SQP) and the particle swarm optimization-sequential quadratic programming (PSO-SQP). These algorithms have recently gained prominence as they present a practical approach to managing cell populations as well as their ability to effectively estimate the quality of the proposed solution by achieving the optimal solution. Insights and knowledge derived from the optimization of the objective function used in these two algorithms, whether through maximization or minimization, significantly contribute to the enhancement of evolutionary computation within the same cell population. The performance of these two algorithms is illustrated by determining the difference between the optimal results determined from GA-SQP and PSO-SQP algorithms. Both Control data and Bismuth Oxide Nanoparticles (BIONPS) survival experimental data are used. The reliability of the algorithms is elucidated based on the number of iterations, the computational time as well as the sum of squared error values. The linear quadratic method is used for treating the evolutionary computation of the cell population. By contrasting the theoretical findings with experimental results, it turns out that both PSO-SQP and GA-SQP optimization methods provide a correlation value close to experimental data and the estimated survival data. This emerging methodology reliably demonstrates the capability of the model to accurately fit the experimental data. Interestingly, a greater efficiency and effectiveness of the proposed PSO-SQP algorithm than the GA-SQP algorithm is observed suggesting hence the superiority of the PSO-SQP algorithm for determining the most realistic estimates of all the six model parameters studied herein.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144768621","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}

{"title":"Integrating Time and Frequency Domain Features of fMRI Time Series for Alzheimer's Disease Classification Using Graph Neural Networks.","authors":"Wei Peng, Chunshan Li, Yanhan Ma, Wei Dai, Dongxiao Fu, Li Liu, Lijun Liu, Ning Yu, Jin Liu","doi":"10.1007/s12539-025-00759-7","DOIUrl":"https://doi.org/10.1007/s12539-025-00759-7","url":null,"abstract":"<p><p>Accurate and early diagnosis of Alzheimer's Disease (AD) is crucial for timely interventions and treatment advancement. Functional Magnetic Resonance Imaging (fMRI), measuring brain blood-oxygen level changes over time, is a powerful AD-diagnosis tool. However, current fMRI-based AD diagnosis methods rely on noise-susceptible time-domain features and focus only on synchronous brain-region interactions in the same time phase, neglecting asynchronous ones. To overcome these issues, we propose Frequency-Time Fusion Graph Neural Network (FTF-GNN). It integrates frequency- and time-domain features for robust AD classification, considering both asynchronous and synchronous brain-region interactions. First, we construct a fully connected hypervariate graph, where nodes represent brain regions and their Blood Oxygen Level-Dependent (BOLD) values at a time series point. A Discrete Fourier Transform (DFT) transforms these BOLD values from the spatial to the frequency domain for frequency-component analysis. Second, a Fourier-based Graph Neural Network (FourierGNN) processes the frequency features to capture asynchronous brain region connectivity patterns. Third, these features are converted back to the time domain and reshaped into a matrix where rows represent brain regions and columns represent their frequency-domain features at each time point. Each brain region then fuses its frequency-domain features with position encoding along the time series, preserving temporal and spatial information. Next, we build a brain-region network based on synchronous BOLD value associations and input the brain-region network and the fused features into a Graph Convolutional Network (GCN) to capture synchronous brain region connectivity patterns. Finally, a fully connected network classifies the brain-region features. Experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate the method's effectiveness: Our model achieves 91.26% accuracy and 96.79% AUC in AD versus Normal Control (NC) classification, showing promising performance. For early-stage detection, it attains state-of-the-art performance in distinguishing NC from Late Mild Cognitive Impairment (LMCI) with 87.16% accuracy and 93.22% AUC. Notably, in the challenging task of differentiating LMCI from AD, FTF-GNN achieves optimal performance (85.30% accuracy, 94.56% AUC), while also delivering competitive results (77.40% accuracy, 91.17% AUC) in distinguishing Early MCI (EMCI) from LMCI-the most clinically complex subtype classification. These results indicate that leveraging complementary frequency- and time-domain information, along with considering asynchronous and synchronous brain-region interactions, can address existing approach limitations, offering a robust neuroimaging-based diagnostic solution.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144768620","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}

{"title":"Classification of Brain Tumors in MRI Images with Brain-CNXSAMNet: Integrating Hybrid ConvNeXt and Spatial Attention Module Networks.","authors":"Hüseyin Fırat, Hüseyin Üzen","doi":"10.1007/s12539-025-00743-1","DOIUrl":"https://doi.org/10.1007/s12539-025-00743-1","url":null,"abstract":"<p><p>Brain tumors (BT) can cause fatal outcomes by affecting body functions, making precise early detection via magnetic resonance imaging (MRI) examinations critical. The complex variations found in cells of BT may pose challenges in identifying the type of tumor and selecting the most suitable treatment strategy, potentially resulting in different assessments by doctors. As a result, in recent years, AI-powered diagnostic systems have been created to accurately and efficiently identify different types of BT using MRI images. Notably, state-of-the-art deep learning architectures, which have demonstrated efficacy in diverse domains, are now being employed effectively for classifying of brain MRI images. This research presents a hybrid model that integrates spatial attention mechanism (SAM) with ConvNeXt to classify three types of BT: meningioma, pituitary, and glioma. The hybrid model integrates ConvNeXt to enhance the receptive field, capturing information from a broader spatial context, crucial for recognizing tumor patterns spanning multiple pixels. SAM is applied after ConvNeXt, enabling the network to selectively focus on informative regions, thereby improving the model's ability to distinguish BT types and capture complex spatial relationships. Tested on BSF and Figshare datasets, the proposed model achieves a remarkable accuracy of 99.39% and 98.86%, respectively, outperforming the results of recent studies by achieving these results in fewer training periods. This hybrid model marks a major step forward in the automatic classification of BT, demonstrating superior performance in accuracy with efficient training.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144753272","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}

{"title":"HAFMMDA: HIN2vec-Based Attentional Factorization Machines for Predicting Microbe-Drug Associations.","authors":"Bo Wang, Junqi Wang, Xiaoxin Du, Jianfei Zhang, Yang He, Fangjian Ma","doi":"10.1007/s12539-025-00710-w","DOIUrl":"https://doi.org/10.1007/s12539-025-00710-w","url":null,"abstract":"<p><p>Emerging research continues to reveal the fundamental contributions of microbial communities to maintaining human physiological balance and advancing drug discovery. However, established wet-lab investigation techniques require significant time and resources. Contemporary research efforts have predominantly concentrated on establishing robust computational architectures to predict microbe-drug associations. Our research establishes a neural network architecture that synthesizes heterogeneous biological relationships with attentional factorization machines (HAFMMDA) to predict undiscovered microbe-drug linkages. The initial step involves assembling a heterogeneous network architecture integrating three key components: microbe similarity networks, drug similarity networks, and established microbe-drug interaction networks. HAFMMDA utilizes HIN2vec to extract feature representations of microbe-drug pairs. Finally, it combines second-order feature interactions and attention mechanism to perform comprehensive prediction. Five-fold cross-validation results confirmed excellent predictive performance with an AUC score of 0.9805, demonstrating statistically significant improvements over five contemporary baseline approaches. These findings corroborate HAFMMDA's effectiveness in uncovering verified drug-microorganism associations while simultaneously predicting innovative therapeutic-microbe relationships.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144753273","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}