Interdisciplinary Sciences: Computational Life Sciences最新文献

筛选
英文 中文
ResNeXt-Based Rescoring Model for Proteoform Characterization in Top-Down Mass Spectra. 基于resnext的自顶向下质谱中蛋白质形态表征的评分模型。
IF 3.9 2区 生物学
Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-09-01 Epub Date: 2025-05-17 DOI: 10.1007/s12539-025-00701-x
Jiancheng Zhong, Yicheng Luo, Chen Yang, Maoqi Yuan, Shaokai Wang
{"title":"ResNeXt-Based Rescoring Model for Proteoform Characterization in Top-Down Mass Spectra.","authors":"Jiancheng Zhong, Yicheng Luo, Chen Yang, Maoqi Yuan, Shaokai Wang","doi":"10.1007/s12539-025-00701-x","DOIUrl":"10.1007/s12539-025-00701-x","url":null,"abstract":"<p><p>In top-down proteomics, the accurate identification and characterization of proteoform through mass spectrometry represents a critical objective. As a result, achieving accuracy in identification results is essential. Multiple primary structure alterations in proteins generate a diverse range of proteoforms, resulting in an exponential increase in potential proteoform. Moreover, the absence of a definitive reference set complicates the standardization of results. Therefore, enhancing the accuracy of proteoform characterization continues to be a significant challenge. We introduced a ResNeXt-based deep learning model, PrSMBooster, for rescoring proteoform spectrum matches (PrSM) during proteoform characterization. As an ensemble method, PrSMBooster integrates four machine learning models, logistic regression, XGBoost, decision tree, and support vector machine, as weak learners to obtain PrSM features. The basic and latent features of PrSM are subsequently input into the ResNeXt model for final rescoring. To verify the effect and accuracy of the PrSMBooster model in rescoring proteoform characterization, it was compared with the characterization algorithm TopPIC across 47 independent mass spectrometry datasets from various species. The experimental results indicate that in most mass spectrometry datasets, the number of PrSMs obtained after rescoring with PrSMBooster increases at a false discovery rate (FDR) of 1%. Further analysis of the experimental results confirmed that PrSMBooster improves the accuracy of PrSM scoring, generates more mass spectrometry characterization results, and demonstrates strong generalization ability.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"634-648"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12289818/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144086199","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}
引用次数: 0
ScAGCN: Graph Convolutional Network with Adaptive Aggregation Mechanism for scRNA-seq Data Dimensionality Reduction. ScAGCN:基于自适应聚合机制的scRNA-seq数据降维图卷积网络。
IF 3.9 2区 生物学
Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-09-01 Epub Date: 2025-04-25 DOI: 10.1007/s12539-025-00702-w
Xiaoshu Zhu, Liquan Zhao, Fei Teng, Shuang Meng, Miao Xie
{"title":"ScAGCN: Graph Convolutional Network with Adaptive Aggregation Mechanism for scRNA-seq Data Dimensionality Reduction.","authors":"Xiaoshu Zhu, Liquan Zhao, Fei Teng, Shuang Meng, Miao Xie","doi":"10.1007/s12539-025-00702-w","DOIUrl":"10.1007/s12539-025-00702-w","url":null,"abstract":"<p><p>With the development of single-cell RNA-sequencing (scRNA-seq) technology, scRNA-seq data analysis suffers huge challenges due to large scale, high dimensionality, high noise, and high sparsity. To achieve accurately embedded representation in the large-scale scRNA-seq data, we try to design a novel graph convolutional network with an adaptive aggregation mechanism. Based on the assumption that the aggregation order of different cells would be different, a graph convolutional network with an adaptive aggregation-based dimensionality reduction algorithm for scRNA-seq data is developed, named scAGCN. In scAGCN, a preprocessing consisting of quality control and feature selection is implemented. Then, an approximate nearest neighbor graph is rapidly constructed. Finally, a graph convolutional network with an adaptive aggregation mechanism is constructed, in which the neighborhood selection strategy based on node distribution and similarity boxplots is designed, and the aggregation function is optimized by defining a similarity measurement between neighborhood nodes and the central node. The results show that scAGCN outperforms existing dimensionality reduction methods on 15 real scRNA-seq datasets, especially in 10 large-scale scRNA-seq datasets.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"576-585"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143995198","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
AttResAMD: An Attention-Driven Deep Learning Framework for Expert-Level Automated Classification of Age-Related Macular Degeneration from Fundus Photography. 一个关注驱动的深度学习框架,用于眼底摄影中年龄相关性黄斑变性的专家级自动分类。
IF 3.9 2区 生物学
Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-08-30 DOI: 10.1007/s12539-025-00763-x
Siqi Bao, Zijian Yang, Zicheng Zhang, Jia Qu, Jie Sun
{"title":"AttResAMD: An Attention-Driven Deep Learning Framework for Expert-Level Automated Classification of Age-Related Macular Degeneration from Fundus Photography.","authors":"Siqi Bao, Zijian Yang, Zicheng Zhang, Jia Qu, Jie Sun","doi":"10.1007/s12539-025-00763-x","DOIUrl":"https://doi.org/10.1007/s12539-025-00763-x","url":null,"abstract":"","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144952944","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 3.9 2区 生物学
Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-08-30 DOI: 10.1007/s12539-025-00760-0
Teng Zhang, Lian Liu
{"title":"m<sup>6</sup>ADP-GCNPUAS: m<sup>6</sup>A-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":"https://doi.org/10.1007/s12539-025-00760-0","url":null,"abstract":"","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-08-30","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
IQSPred-PLM: An Interpretable Quorum Sensing Peptides Prediction Model Based on Protein Language Model. 基于蛋白质语言模型的可解释群体感应多肽预测模型。
IF 3.9 2区 生物学
Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-08-26 DOI: 10.1007/s12539-025-00766-8
Yusen Su, Qingyang Guo, Taigang Liu
{"title":"IQSPred-PLM: An Interpretable Quorum Sensing Peptides Prediction Model Based on Protein Language Model.","authors":"Yusen Su, Qingyang Guo, Taigang Liu","doi":"10.1007/s12539-025-00766-8","DOIUrl":"https://doi.org/10.1007/s12539-025-00766-8","url":null,"abstract":"<p><p>Quorum sensing regulates cooperative behaviors in bacteria through the accumulation and detection of signaling molecules. This process plays a crucial role in various biological functions, including biofilm formation, antibiotic production, regulation of virulence factors, and immune modulation. Quorum sensing peptides (QSPs), primarily produced by Gram-positive bacteria, are key components of the quorum sensing mechanism, and their identification is crucial for understanding bacterial regulation. Despite the availability of several QSP prediction tools based on handcrafted features and machine learning techniques, there is still potential for improving their performance and interpretability. In this study, we present IQSPred-PLM, a novel model for predicting QSPs that integrates protein language models (PLMs) with a convolutional neural network (CNN). First, we utilize the pre-trained PLM ESM-2 to encode peptide sequences. Then, feature extraction is performed using a multi-scale residual CNN (MSRes-CNN), with dynamic feature integration through an adaptive weight modulation (AWM) module. Finally, a fully connected network is designed to conduct the classification of QSPs. Evaluated on the benchmark dataset, IQSPred-PLM demonstrated the outstanding predictive performance with accuracy (ACC), Matthews correlation coefficient (MCC), and the area under the receiver operating characteristic (ROC) curve (AUC) of 97.50%, 0.951, and 0.990, respectively. Furthermore, case studies and interpretability analyses confirmed the effectiveness of IQSPred-PLM for the QSP prediction task.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144953063","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
Adaptive Graph Prompting Meets Contrastive Learning: A Multi-View Framework for Metabolite-Disease Association Prediction. 自适应图形提示与对比学习:代谢物-疾病关联预测的多视图框架。
IF 3.9 2区 生物学
Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-08-22 DOI: 10.1007/s12539-025-00751-1
Xiaoxin Du, Xue Yang, Bo Wang, Mei Jin, Yiping Wang, Changrong Li, Peilong Wu
{"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":"https://doi.org/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":""},"PeriodicalIF":3.9,"publicationDate":"2025-08-22","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}
引用次数: 0
AlzhiNet: Traversing from 2D-CNN to 3D-CNN, Towards Early Detection and Diagnosis of Alzheimer's Disease. 阿尔茨海默病:从2D-CNN到3D-CNN,迈向阿尔茨海默病的早期检测和诊断。
IF 3.9 2区 生物学
Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-08-22 DOI: 10.1007/s12539-025-00764-w
Romoke Grace Akindele, Samuel Adebayo, Ming Yu, Paul Shekonya Kanda
{"title":"AlzhiNet: Traversing from 2D-CNN to 3D-CNN, Towards Early Detection and Diagnosis of Alzheimer's Disease.","authors":"Romoke Grace Akindele, Samuel Adebayo, Ming Yu, Paul Shekonya Kanda","doi":"10.1007/s12539-025-00764-w","DOIUrl":"https://doi.org/10.1007/s12539-025-00764-w","url":null,"abstract":"<p><p>Alzheimer's disease (AD) is a progressive neurodegenerative disorder with increasing prevalence among the ageing population, necessitating early and accurate diagnosis for effective disease management. In this study, we present a novel hybrid deep learning framework, AlzhiNet, that integrates both 2D convolutional neural networks (2D-CNNs) and 3D convolutional neural networks (3D-CNNs), along with a custom loss function and volumetric data augmentation, to enhance feature extraction and improve classification performance in AD diagnosis. According to extensive experiments, AlzhiNet outperforms standalone 2D and 3D models, highlighting the importance of combining these complementary representations of data. The depth and quality of 3D volumes derived from the augmented 2D slices also significantly influence the model's performance. The results indicate that carefully selecting weighting factors in hybrid predictions is imperative for achieving optimal results. Our framework has been validated on the magnetic resonance imaging (MRI) from Kaggle and MIRIAD datasets, obtaining accuracies of 98.9% and 99.99%, respectively, with an AUC of 100%. Furthermore, AlzhiNet was studied under a variety of perturbation scenarios on the Alzheimer's Kaggle dataset, including Gaussian noise, brightness, contrast, salt and pepper noise, color jitter, and occlusion. The results obtained show that AlzhiNet is more robust to perturbations than ResNet-18, making it an excellent choice for real-world applications. This approach represents a promising advancement in the early diagnosis and treatment planning for AD.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144952961","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
VHGAE: Drug-Target Interaction Prediction Model Based on Heterogeneous Graph Variational Autoencoder. 基于异构图变分自编码器的药物-靶标相互作用预测模型。
IF 3.9 2区 生物学
Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-08-21 DOI: 10.1007/s12539-025-00758-8
Chen Zhang, Jiaqi Sun, Linlin Xing, Longbo Zhang, Hongzhen Cai, Kai Che
{"title":"VHGAE: Drug-Target Interaction Prediction Model Based on Heterogeneous Graph Variational Autoencoder.","authors":"Chen Zhang, Jiaqi Sun, Linlin Xing, Longbo Zhang, Hongzhen Cai, Kai Che","doi":"10.1007/s12539-025-00758-8","DOIUrl":"https://doi.org/10.1007/s12539-025-00758-8","url":null,"abstract":"<p><p>Identifying drug-target interaction (DTI) is crucial for drug discovery and repositioning. Biological identification of DTI is costly and time-consuming, so heterogeneous network methods for DTI prediction, which can speed up drug development, have drawn much attention. However, the scarcity of known drug-target pairs leads to an overwhelmingly sparse network structure. This sparsity makes it difficult for graph convolutional networks to fully capture the potential relationships between nodes during information transmission and feature extraction, thus challenging the prediction performance of DTI. Therefore, we propose a method, called VHGAE, based on a heterogeneous graph variational autoencoder to predict drug-target interactions. The VHGAE model integrates a wealth of prior knowledge from various sources related to drugs and targets to construct a heterogeneous network. To address the sparsity issue within the constructed heterogeneous network, first, the weighted k-nearest neighbor algorithm is used to densify the DTI network to increase the connectivity of the sparse network. Second, the weighted graph convolutional network within a variational graph autoencoder framework is employed to bolster the edge weights with limited connections. Third, a variational expectation maximization algorithm is introduced between the encoder and the decoder to recover potential relationships in the sparse network. The experimental results on two datasets show that the performance of VHGAE outperforms nine state-of-the-art DTI prediction methods, which suggests that the design of VHGAE in multi-source data fusion and sparse network processing plays an important role in improving prediction performance.</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144953073","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
A Novel Dual-Level Momentum Distillation Method with Extreme Thresholding for Imputing Single-Cell RNA Sequencing Data. 一种新的双能级动量精馏极值法用于单细胞RNA测序数据的输入。
IF 3.9 2区 生物学
Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-08-21 DOI: 10.1007/s12539-025-00754-y
Binhua Tang, Xinyu Gao, Guowei Cheng
{"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":"https://doi.org/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":""},"PeriodicalIF":3.9,"publicationDate":"2025-08-21","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}
引用次数: 0
AIP-TranLAC: A Transformer-Based Method Integrating LSTM and Attention Mechanism for Predicting Anti-inflammatory Peptides. AIP-TranLAC:一种整合LSTM和注意机制的基于转换器的抗炎肽预测方法。
IF 3.9 2区 生物学
Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-08-19 DOI: 10.1007/s12539-025-00761-z
Shengli Zhang, Jingyi Ren
{"title":"AIP-TranLAC: A Transformer-Based Method Integrating LSTM and Attention Mechanism for Predicting Anti-inflammatory Peptides.","authors":"Shengli Zhang, Jingyi Ren","doi":"10.1007/s12539-025-00761-z","DOIUrl":"10.1007/s12539-025-00761-z","url":null,"abstract":"<p><p>Anti-inflammatory peptides (AIPs) have emerged as potential therapeutic candidates for managing various inflammatory disorders, but their computational identification remains challenging. We propose AIP-TranLAC, a novel deep learning framework that integrates Transformer-based embedding, bidirectional long short-term memory (Bi-LSTM), multi-head attention, and convolutional neural network (CNN) to classify AIPs accurately. Our model achieves superior performance on benchmark and independent test datasets, demonstrating significant improvements over existing methods. The hybrid architecture effectively captures local and global sequence patterns, while interpretability analyses reveal critical amino acid residues. With robust performance on imbalanced data and open-source availability, AIP-TranLAC provides a powerful tool for accelerating therapeutic peptide discovery and inflammation research. For reproducibility purposes, we have released the codebase, trained models, and all supporting data on GitHub ( https://github.com/Renjingyi123/AIP-TranLAC ).</p>","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144882828","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
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信