Interdisciplinary Sciences: Computational Life Sciences最新文献

{"title":"Adaptive Graph Prompting Meets Contrastive Learning: A Multi-View Framework for Metabolite-Disease Association Prediction.","authors":"Xiaoxin Du, Xue Yang, Bo Wang, Mei Jin, Yiping Wang, Changrong Li, Peilong Wu","doi":"10.1007/s12539-025-00751-1","DOIUrl":"10.1007/s12539-025-00751-1","url":null,"abstract":"<p><p>Metabolite-disease associations (MDAs) are critical for advancing precision medicine, yet existing computational methods face challenges in data sparsity, noise robustness, and feature representation. We propose GPLCL (graph prompt-enhanced contrastive learning), a novel multi-view graph learning framework integrating adaptive graph prompting and contrastive learning. GPLCL introduces enhanced graph prompt features (GPF +) with attention-based node adaptation, enabling dynamic feature recalibration. Through strategic graph augmentation and self-supervised contrastive optimization, it preserves essential topological invariants while aggregating multi-scale neighborhood patterns via HeteroGraphSAGE. In the fivefold cross-validation, GPLCL achieves AUC 0.9761 and AUPR 0.9729 on dataset 1, which is the highest improvement of 0.55 to 6.37 percentage points over the existing methods; GPLCL still maintains AUC 0.9576 and AUPR 0.9499 on the highly noisy Dataset 2, which proves its excellent performance and robustness. Case studies on type 1 diabetes, obesity, and Parkinson's disease highlighted the model's potential in discovering novel MDAs, underscoring its applicability in advancing metabolomics research and translational medicine. The code is publicly available at https://github.com/yxue9/GPLCL .</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"640-655"},"PeriodicalIF":3.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144952922","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":"GapSense: Similarity Estimation-Based Gap Filler with TGS-Reads for Genome Assemblies.","authors":"Yejin Kan, Dongyeon Kim, Jinkyung Yang, Gangman Yi","doi":"10.1007/s12539-025-00770-y","DOIUrl":"10.1007/s12539-025-00770-y","url":null,"abstract":"<p><p>Advances in next-generation sequencing have led to an explosion in sequencing data, accelerating genome assembly research. However, draft genomes generated after scaffolding still contain unresolved gaps, often caused by repetitive regions and sequencing errors. These gaps may contain biologically meaningful sequences and thus require accurate resolution. However, existing gap-filling tools often exhibit limited reliability, especially when applied to large and complex eukaryotic genomes, due to their insufficient capacity to resolve repetitive regions or their heavy dependence on error-prone long reads. To address this challenge, we present GapSense, a robust gap-filling method that leverages similarity estimation using third-generation sequencing (TGS) reads. By quantifying pairwise similarity among candidate sequences, GapSense prioritizes informative regions and reconstructs gap sequences with higher accuracy. The proposed method introduces a novel similarity scoring mechanism that evaluates the geometric overlap of adjacent subregions to capture local structural variations and reduces noise from low-coverage and error-prone long reads. Experimental results on six representative species and three popular assemblers show that GapSense consistently outperforms existing tools in terms of gap-filling accuracy and contiguity, while maintaining low performance variability across different datasets. These findings demonstrate the effectiveness and generalizability of GapSense for accurate and scalable gap-filling.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"501-514"},"PeriodicalIF":3.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145451891","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":"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":"373-391"},"PeriodicalIF":3.9,"publicationDate":"2026-06-01","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":"Joint Low Rank Representation with Symmetric Orthogonal Decomposition for Clustering of scRNA-seq Data.","authors":"Wei Zhang, Yue Yu, Yuanyuan Li, Xiaoying Zheng, Juan Shen","doi":"10.1007/s12539-025-00767-7","DOIUrl":"10.1007/s12539-025-00767-7","url":null,"abstract":"<p><p>Single-cell RNA transcriptome data offer a fantastic chance to investigate biological mechanisms such as cellular heterogeneity. Accurate identification of subtypes is of great importance for revealing the molecular mechanisms underlying complex diseases. Designing computational methods for cell type identification has been a hot topic recently, and various computational algorithms have been designed to estimate cell type composition. However, owing to the high sparseness, noise, and dimensionality of the obtainable scRNA-seq data, boosting prediction performance remains a challenge. In this work, a new cell type identification method is developed by integrating low rank representation (LRR) and symmetric orthogonal decomposition, named LRRS. Different from the spectral embedding algorithm in which the number of clusters is predefined, LRRS introduces a new orthogonal symmetric decomposition strategy and adaptively characterizes the local properties by measuring the weighted distance under the orthogonal space. To optimize the graph model, an efficient iterative approach is proposed to optimize each variable alternatively utilizing the alternating direction method of multipliers (ADMM). Based on the resulting similarity matrix, the spectral algorithm is adopted to group the individual cells. To evaluate the performance of LRRS, we implemented it on the eleven benchmark datasets and compared it with fourteen other cutting-edge methods in terms of prediction accuracy and normalized mutual information. The comparison results show that LRRS is effective in predicting cell type composition.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"488-500"},"PeriodicalIF":3.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145354526","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":"Causal Transformer for Learning Embeddings from Structured Medical History Records and Multi-Source Data Integration for Complex Disease Risk Prediction.","authors":"Zeming Li, Yu Xu, Debajyoti Chowdhury, Hip Fung Yip, Chonghao Wang, Lu Zhang","doi":"10.1007/s12539-025-00749-9","DOIUrl":"10.1007/s12539-025-00749-9","url":null,"abstract":"<p><p>Traditional disease risk prediction models predominantly rely on statistical algorithms and often focus on genetic factors or a limited set of lifestyle factors to estimate the risk of disease onset. Recently, more comprehensive approaches have emerged that integrate genetic factors with additional lifestyle factors (e.g., alcohol intake) and physical features (e.g., body mass index, age) to increase predictive accuracy. Since the onset of complex diseases is often accompanied by the occurrence of comorbidities, incorporating medical history records is a critical yet underexplored avenue for improving risk prediction. In this study, we propose a novel framework, MIDRP (Multi-source Integration for Disease Risk Prediction), which incorporates genetic variants, lifestyle factors, physical attributes, and medical history records to achieve more robust and accurate predictions. At the heart of our approach lies a causal Transformer architecture, specifically designed to extract and interpret nuanced patterns from medical history records. In the experiments, we compared MIDRP with several baselines, including LDPred2, random forest, multilayer perception, logistic regression, AdaBoost, DiseaseCapsule, EIR, and Med-Bert, on three complex diseases Coronary Artery Disease, Type 2 Diabetes, and Breast Cancer using data from the UK Biobank. Our method achieved state-of-the-art performance, AUROC scores of 0.783, 0.841, and 0.784, respectively, demonstrating its potential in the field of complex disease risk prediction.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"614-627"},"PeriodicalIF":5.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13219203/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145080571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Semi-supervised Reinforcement Learning Framework Incorporating the Multi-scale IncepMambaNet Network for Glaucoma Progression Prediction.","authors":"Xue Pan, Ze Xiong, Dehui Qiu, Liguo Deng, Boxuan Zhao, Xiaojie Cao, Xiaohua Wan, Fa Zhang","doi":"10.1007/s12539-025-00739-x","DOIUrl":"10.1007/s12539-025-00739-x","url":null,"abstract":"<p><p>Glaucoma is the leading cause of irreversible blindness worldwide. Currently, artificial intelligence (AI) technology combined with optical coherence tomography (OCT) and visual field testing is widely used for glaucoma progression prediction. However, the lack of labeled patient data and the ambiguity of structural differences in glaucoma remain major research bottlenecks. To address this, this paper proposes a glaucoma progression prediction method based on a semi-supervised reinforcement learning (SSRL) framework. In the SSRL framework, we design a key experience filtering strategy (KEFS) that prioritizes high-value training samples to enhance the model's generalization ability and optimize the quality of pseudo-labels. Additionally, an entropy regularization technique is introduced to encourage high-entropy distributions, preventing the model from prematurely converging to specific decisions and improving its ability to explore unlabeled data. To adapt the classifier and the pseudo-label generator within the SSRL framework, we innovatively propose the IncepMambaNet multi-scale network. This network integrates the visual state space (VSS) module from the VMamba model with traditional CNN structures, designing a four-branch IncepMamba module that leverages the strengths of both approaches. Experimental results demonstrate that compared to traditional supervised learning methods, our SSRL model effectively improves the performance of various state-of-the-art (SOTA) supervised classification networks. Furthermore, compared to various SOTA classification networks, IncepMambaNet achieves leading performance in three key metrics-macro-F1, F2-score, and AUC-by 0.8%, 8.8%, and 0.9%, respectively, showcasing outstanding feature capture capability and generalization performance. Ablation studies further confirm that the multi-branch structure and channel reordering operations play a crucial role in enhancing model performance.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"451-470"},"PeriodicalIF":3.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144698464","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":"Attention-Guided Multi-View Contrastive Learning for Predicting Sparse Drug-Gene Associations.","authors":"Qingyong Wang, Yudong Liu, Shangping Zhao","doi":"10.1007/s12539-026-00843-6","DOIUrl":"https://doi.org/10.1007/s12539-026-00843-6","url":null,"abstract":"<p><p>Employing deep learning techniques for drug discovery and repurposing necessitates the acceleration of predictions regarding drug-gene interactions. However, the scarcity of experimental support data often constrains the performance and generalization capabilities of existing predictive models. To address this issue, we propose an attention-guided multi-view contrastive learning method (named AMCL) for predicting unidentified drug-gene correlations. Specifically, AMCL integrates multi-scale feature learning and employs a graph convolutional network to extract local topological information. Additionally, it utilizes a kernel function to capture global structural patterns. High-order dependencies are dynamically modeled through the dynamic hypergraph learning module. The model is guided to prioritize the information of densely linked regions in the interactive network and aids in the prediction job by the attention bias mechanism based on the LCA-biased attention. The discriminating capacity of learned embeddings is improved by the cross-view contrastive learning technique, particularly when sparse data is present. The experimental results show that AMCL outperforms other state-of-the-art techniques on the three datasets of DGIdb 5.0, ChEMBL and Guide to Pharmacology. The contributions of each component have been validated through ablation studies, and case studies show AMCL's adaptability in discovering new medications and repositioning existing ones.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148137979","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":"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":"670-686"},"PeriodicalIF":3.9,"publicationDate":"2026-06-01","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":"Clustering Single-Cell RNA-Seq Data with Low-Rank Matrix Factorization and Local Graph Regularization.","authors":"Yue Yu, Wei Zhang, Xiaoying Zheng, Juan Shen, Yuanyuan Li","doi":"10.1007/s12539-025-00762-y","DOIUrl":"10.1007/s12539-025-00762-y","url":null,"abstract":"<p><p>Single-cell RNA sequencing (scRNA-seq) offers significant opportunities to reveal cellular heterogeneity and diversity. Accurate cell type identification is critical for downstream analyses and understanding the mechanisms of heterogeneity. However, challenges arise from the high dimensionality, sparsity, and noise of scRNA-seq data. While various low-rank representation (LRR)-based clustering methods have been developed, many existing approaches may inaccurately capture relationships or conflate true patterns with noise. To address these limitations, we introduce a novel clustering algorithm that integrates low-rank matrix decomposition with local graph regularization (LRMGC). This approach applies a tri-decomposition strategy to the representation matrix to derive an aligned core matrix, and characterizes the \"distance\" between cells in a lower-dimensional space through a local manifold regularization term. Rather than relying on the kernel norm of the representation matrix, the Schatten p-norm is applied to the core matrix to robustly learn the similarity matrix against noise and outliers, while maintaining the high-dimensional noisy data's underlying subspace structure for accurate and robust clustering. Additionally, the final similarity matrix is obtained by applying the angular alignment strategy on the similarity matrix. Comprehensive experiments and comparisons with advanced methods on scRNA-seq datasets demonstrate LRMGC's superior performance and reliability in uncovering cell type composition. Furthermore, a variety of downstream analyses, such as marker gene identification, functional enrichment analysis, rare cell recognition, and cell-cell communication, also demonstrate the effectiveness of LRMGC.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"419-433"},"PeriodicalIF":3.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144952966","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":"A Novel Dual-Level Momentum Distillation Method with Extreme Thresholding for Imputing Single-Cell RNA Sequencing Data.","authors":"Binhua Tang, Xinyu Gao, Guowei Cheng","doi":"10.1007/s12539-025-00754-y","DOIUrl":"10.1007/s12539-025-00754-y","url":null,"abstract":"<p><p>Single-cell RNA sequencing (scRNA-seq) plays a vital role in studying cellular heterogeneity and gene expression patterns. However, the sequencing dropout phenomena still pose a significant challenge. Genes with low expression levels may be misidentified as exhibiting zero expression owing to limitations in sequencing depth and technical noise. This results in increased data sparsity and compromises the accuracy of subsequent analyses. Thus, a novel method, MoDET (Dual-level Momentum Distillation Method with Extreme Thresholding), has been proposed. MoDET employs a label-guided model and an extreme threshold mechanism to enhance cellular representation learning. Experiments demonstrate that MoDET significantly improves clustering performance of the gene expression matrix, with enhancements ranging from 3% to 20% across seven real-world datasets. Cross-batch training and evaluation experiments demonstrated that MoDET effectively mitigates batch effects, achieving an average performance improvement of 5%-7%. Concurrently, it exhibits superior accuracy in identifying rare cell types, outperforming other methods by 3%-20%. Ablation studies confirm that the dual-level momentum distillation boosts performance by 4%-20%, and the extreme threshold mechanism adds an additional 2%-15% improvement. Interpretability analysis shows that the extreme threshold makes the model's decision-making process more transparent. Moreover, MoDET surpasses methods incorporating advanced modules, thereby demonstrating its efficacy in addressing the sparsity challenges inherent in scRNA-seq datasets. The compiled source codes are accessible at https://github.com/gladex/MoDET.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"656-669"},"PeriodicalIF":3.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144952932","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}