Interdisciplinary Sciences: Computational Life Sciences最新文献_第2页

AI Prediction of Structural Stability of Nanoproteins Based on Structures and Residue Properties by Mean Pooled Dual Graph Convolutional Network. 基于结构和残基性质的纳米蛋白质结构稳定性人工智能预测--基于平均汇集双图卷积网络

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-03-01 Epub Date: 2024-10-05 DOI: 10.1007/s12539-024-00662-7

Daixi Li, Yuqi Zhu, Wujie Zhang, Jing Liu, Xiaochen Yang, Zhihong Liu, Dongqing Wei

{"title":"AI Prediction of Structural Stability of Nanoproteins Based on Structures and Residue Properties by Mean Pooled Dual Graph Convolutional Network.","authors":"Daixi Li, Yuqi Zhu, Wujie Zhang, Jing Liu, Xiaochen Yang, Zhihong Liu, Dongqing Wei","doi":"10.1007/s12539-024-00662-7","DOIUrl":"10.1007/s12539-024-00662-7","url":null,"abstract":"The structural stability of proteins is an important topic in various fields such as biotechnology, pharmaceuticals, and enzymology. Specifically, understanding the structural stability of protein is crucial for protein design. Artificial design, while pursuing high thermodynamic stability and rigidity of proteins, inevitably sacrifices biological functions closely related to protein flexibility. The thermodynamic stability of proteins is not always optimal when they are highest to perfectly perform their biological functions. Extensive theoretical and experimental screening is often required to obtain stable protein structures. Thus, it becomes critically important to develop a stability prediction model based on the balance between protein stability and bioactivity. To design protein drugs with better functionality in a broader structural space, a novel protein structural stability predictor called PSSP has been developed in this study. PSSP is a mean pooled dual graph convolutional network (GCN) model based on sequence characteristics and secondary structure, distance matrix, graph, and residue properties of a nanoprotein to provide rapid prediction and judgment. This model exhibits excellent robustness in predicting the structural stability of nanoproteins. Comparing with previous artificial intelligence algorithms, the results indicate this model can provide a rapid and accurate assessment of the structural stability of artificially designed proteins, which shows the great promises for promoting the robust development of protein design.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"101-113"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377868","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 Integrated TCN-CrossMHA Model for Predicting circRNA-RBP Binding Sites. 用于预测 circRNA-RBP 结合位点的 TCN-CrossMHA 集成模型。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-03-01 Epub Date: 2024-11-06 DOI: 10.1007/s12539-024-00660-9

Yajing Guo, Xiujuan Lei, Shuyu Li

{"title":"An Integrated TCN-CrossMHA Model for Predicting circRNA-RBP Binding Sites.","authors":"Yajing Guo, Xiujuan Lei, Shuyu Li","doi":"10.1007/s12539-024-00660-9","DOIUrl":"10.1007/s12539-024-00660-9","url":null,"abstract":"Circular RNA (circRNA) has the capacity to bind with RNA binding protein (RBP), thereby exerting a substantial impact on diseases. Predicting binding sites aids in comprehending the interaction mechanism, thereby offering insights for disease treatment strategies. Here, we propose a novel approach based on temporal convolutional network (TCN) and cross multi-head attention mechanism to predict circRNA-RBP binding sites (circTCA). First, we employ two distinct encoding methodologies to obtain two raw matrices of circRNA sequences. Then, two parallel TCN blocks extract shallow and abstract features of the two matrices separately. The fusion of the two is achieved through cross multi-head attention mechanism and after this, global expectation pooling assigns weights to the concatenated feature. Finally, the task of classifying the input sequence is entrusted to a fully connected (FC) layer. We compare circTCA with other five methods and conduct ablation experiments to demonstrate its effectiveness. We also conduct feature visualization and assess the motifs extracted by circTCA with existing motifs. All in all, circTCA is effective for binding sites prediction of circRNA and RBP.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"86-100"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142581680","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

CSEL-BGC: A Bioinformatics Framework Integrating Machine Learning for Defining the Biosynthetic Evolutionary Landscape of Uncharacterized Antibacterial Natural Products. CSEL-BGC：整合机器学习的生物信息学框架，用于定义未表征抗菌天然产品的生物合成进化图谱。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-03-01 Epub Date: 2024-09-30 DOI: 10.1007/s12539-024-00656-5

Minghui Du, Yuxiang Ren, Yang Zhang, Wenwen Li, Hongtao Yang, Huiying Chu, Yongshan Zhao

{"title":"CSEL-BGC: A Bioinformatics Framework Integrating Machine Learning for Defining the Biosynthetic Evolutionary Landscape of Uncharacterized Antibacterial Natural Products.","authors":"Minghui Du, Yuxiang Ren, Yang Zhang, Wenwen Li, Hongtao Yang, Huiying Chu, Yongshan Zhao","doi":"10.1007/s12539-024-00656-5","DOIUrl":"10.1007/s12539-024-00656-5","url":null,"abstract":"The sluggish pace of new antibacterial drug development reflects a vulnerability in the face of the current severe threat posed by bacterial resistance. Microbial natural products (NPs), as a reservoir of immense chemical potential, have emerged as the most promising avenue for the discovery of next generation antibacterial agent. Directly accessing the antibacterial activity of potential products derived from biosynthetic gene clusters (BGCs) would significantly expedite the process. To tackle this issue, we propose a CSEL-BGC framework that integrates machine learning (ML) techniques. This framework involves the development of a novel cascade-stacking ensemble learning (CSEL) model and the establishment of a groundbreaking model evaluation system. Based on this framework, we predict 6,666 BGCs with antibacterial activity from 3,468 complete bacterial genomes and elucidate a biosynthetic evolutionary landscape to reveal their antibacterial potential. This provides crucial insights for interpretating the synthesis and secretion mechanisms of unknown NPs.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"27-41"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142346017","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

RAEPI: Predicting Enhancer-Promoter Interactions Based on Restricted Attention Mechanism. RAEPI：RAEPI：基于受限注意力机制预测促进剂与增强剂之间的相互作用

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-03-01 Epub Date: 2024-11-15 DOI: 10.1007/s12539-024-00669-0

Wanjing Zhang, Mingyang Zhang, Min Zhu

引用次数: 0

BES-Designer: A Web Tool to Design Guide RNAs for Base Editing to Simplify Library. BES-Designer：设计用于碱基编辑的引导 RNA 以简化文库的网络工具。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-03-01 Epub Date: 2024-10-28 DOI: 10.1007/s12539-024-00663-6

Qian Zhou, Qian Gao, Yujia Gao, Youhua Zhang, Yanjun Chen, Min Li, Pengcheng Wei, Zhenyu Yue

{"title":"BES-Designer: A Web Tool to Design Guide RNAs for Base Editing to Simplify Library.","authors":"Qian Zhou, Qian Gao, Yujia Gao, Youhua Zhang, Yanjun Chen, Min Li, Pengcheng Wei, Zhenyu Yue","doi":"10.1007/s12539-024-00663-6","DOIUrl":"10.1007/s12539-024-00663-6","url":null,"abstract":"CRISPR/Cas base editors offer precise conversion of single nucleotides without inducing double-strand breaks. This technology finds extensive applications in gene therapy, gene function analysis, and other domains. However, a crucial challenge lies in selecting the appropriate guide RNAs (gRNAs) for base editing. Although various gRNAs design tools exist, creating a simplified base-editing library with diverse protospacer adjacent motifs (PAM) sequences for gRNAs screening remains a challenge. We present a user-friendly web tool, BES-Designer ( https://bes-designer.aielab.net ), for gRNAs design based on base editors, aimed at streamlining the creation of a base-editing library. BES-Designer incorporates our proposed rules for target sequence simplification, helping researchers narrow down the scope of biological experiments in the lab. It allows users to design target sequences with various PAMs and editing types simultaneously, and prioritize them in the simplified base-editing library. This tool has been experimentally proven to achieve a 30% simplification efficiency on the base-editing-library.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"134-139"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142521781","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 Molecular Fragment Representation Learning Framework for Drug-Drug Interaction Prediction. 用于药物相互作用预测的分子片段表征学习框架。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-03-01 Epub Date: 2024-10-09 DOI: 10.1007/s12539-024-00658-3

Jiaxi He, Yuping Sun, Jie Ling

{"title":"A Molecular Fragment Representation Learning Framework for Drug-Drug Interaction Prediction.","authors":"Jiaxi He, Yuping Sun, Jie Ling","doi":"10.1007/s12539-024-00658-3","DOIUrl":"10.1007/s12539-024-00658-3","url":null,"abstract":"The concurrent use of multiple drugs may result in drug-drug interactions, increasing the risk of adverse reactions. Hence, it is particularly crucial to propose computational methods for precisely identifying unknown drug-drug interactions, which is of great significance for drug development and health. However, most recent studies have limited the drug-drug interaction prediction task to identifying interactions between substructures, overlooking molecular hierarchical information. Moreover, the extracted substructures in these methods are always restricted to have the same number of atoms as contained in the molecular graph, which does not align with real-world facts. In this study, a molecular fragment representation learning framework for drug-drug interaction prediction is introduced. Initially, a fragment extraction module is designed to acquire a series of molecular fragments. Subsequently, to capture more comprehensive features, molecular hierarchical information is effectively integrated, enabling drug-drug interaction prediction by identifying pairwise interactions between molecular fragments of each drug. Comprehensive evaluations demonstrate that the proposed method achieved state-of-the-art performance in both DrugBank and Twosides datasets, particularly achieving an improved accuracy of over 20% for unseen drugs in both two datasets. Furthermore, case studies and visual analysis confirm that the proposed method can accurately identify crucial substructures influencing the interactions, which are basically consistent with functional group structures in reality. In conclusion, this method not only enhances the performance of drug-drug interaction prediction but also offers high interpretability. Source code is freely available at https://github.com/kennysyp/MFR-DDI .","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"42-58"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390339","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

Repurposing Drugs for Infectious Diseases by Graph Convolutional Network with Sensitivity-Based Graph Reduction. 基于灵敏度的图约简的图卷积网络对传染病药物的再利用。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-03-01 Epub Date: 2024-12-04 DOI: 10.1007/s12539-024-00672-5

Rongting Yue, Abhishek Dutta

{"title":"Repurposing Drugs for Infectious Diseases by Graph Convolutional Network with Sensitivity-Based Graph Reduction.","authors":"Rongting Yue, Abhishek Dutta","doi":"10.1007/s12539-024-00672-5","DOIUrl":"10.1007/s12539-024-00672-5","url":null,"abstract":"Computational systems biology employs computational algorithms and integrates diverse data sources, such as gene expression profiles, molecular interactions, and network modeling, to identify promising drug candidates through repurposing existing compounds in response to urgent healthcare needs. This study tackles the urgent need for rapid therapeutic development against emerging infectious diseases. We introduce a novel analytic expression for sensitivity analysis based on the Kronecker product and enhance model prediction performance using Graph Convolutional Networks (GCNs) with sensitivity-based graph reduction. Our algorithm refines prediction performance by leveraging sensitivity-based graph reduction. By integrating RNA-seq data, molecular interactions, and GCNs, we identify disease-related genes and pathways, construct heterogeneous graph models, and predict potential drugs. This approach involves novel analytical expressions that assess sensitivity to model loss, employing the Kronecker product approach. Subgraph analysis identifies nodes for removal, leading to a refined graph used for model retraining. This cost-effective pipeline focuses on computational methods for drug repurposing, targeting infectious diseases such as Zika virus and COVID-19 infection. Applied to these infections, our methodology integrates 659 proteins and 703 drugs for Zika virus, and 495 proteins and 468 drugs for COVID-19, along with their interactions derived from gene expression profiles. Top candidate drugs, such as Betamethasone phosphate and Bizelesin for Zika virus, and Chloroquine, Heparin Disaccharide, and Resveratrol for COVID-19, were validated through literature review or docking analysis. This scalable approach demonstrates promise in repurposing drugs for urgent healthcare challenges.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"185-199"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142768515","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

scCrab: A Reference-Guided Cancer Cell Identification Method based on Bayesian Neural Networks. scCrab：基于贝叶斯神经网络的参考引导癌细胞识别方法

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-03-01 Epub Date: 2024-09-30 DOI: 10.1007/s12539-024-00655-6

Heyang Hua, Wenxin Long, Yan Pan, Siyu Li, Jianyu Zhou, Haixin Wang, Shengquan Chen

{"title":"scCrab: A Reference-Guided Cancer Cell Identification Method based on Bayesian Neural Networks.","authors":"Heyang Hua, Wenxin Long, Yan Pan, Siyu Li, Jianyu Zhou, Haixin Wang, Shengquan Chen","doi":"10.1007/s12539-024-00655-6","DOIUrl":"10.1007/s12539-024-00655-6","url":null,"abstract":"Cancer is a significant global public health concern, where early detection can greatly enhance curative outcomes. Therefore, the identification of cancer cells holds significant importance as the primary method for cancer diagnosis. The advancement of single-cell RNA sequencing (scRNA-seq) technology has made it possible to address the problem of cancer cell identification at the single-cell level more efficiently with computational methods, as opposed to the time-consuming and less reproducible manual identification methods. However, existing computational methods have shown suboptimal identification performance and a lack of capability to incorporate external reference data as prior information. Here, we propose scCrab, a reference-guided automatic cancer cell identification method, which performs ensemble learning based on a Bayesian neural network (BNN) with multi-head self-attention mechanisms and a linear regression model. Through a series of experiments on various datasets, we systematically validated the superior performance of scCrab in both intra- and inter-dataset predictions. Besides, we demonstrated the robustness of scCrab to dropout rate and sample size, and conducted ablation experiments to investigate the contributions of each component in scCrab. Furthermore, as a dedicated model for cancer cell identification, scCrab effectively captures cancer-related biological significance during the identification process.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"12-26"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142346020","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

CEL: A Continual Learning Model for Disease Outbreak Prediction by Leveraging Domain Adaptation via Elastic Weight Consolidation.

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-02-28 DOI: 10.1007/s12539-024-00675-2

Saba Aslam, Abdur Rasool, Xiaoli Li, Hongyan Wu

{"title":"CEL: A Continual Learning Model for Disease Outbreak Prediction by Leveraging Domain Adaptation via Elastic Weight Consolidation.","authors":"Saba Aslam, Abdur Rasool, Xiaoli Li, Hongyan Wu","doi":"10.1007/s12539-024-00675-2","DOIUrl":"https://doi.org/10.1007/s12539-024-00675-2","url":null,"abstract":"Continual learning is the ability of a model to learn over time without forgetting previous knowledge. Therefore, adapting new data in dynamic fields like disease outbreak prediction is paramount. Deep neural networks are prone to error due to catastrophic forgetting. This study introduces a novel CEL model for Continual Learning by leveraging domain adaptation via Elastic weight consolidation (EWC). This model aims to mitigate the catastrophic forgetting phenomenon in a domain incremental setting. The Fisher information matrix (FIM) is constructed with EWC to develop a regularization term that penalizes changes to essential parameters. We conducted experiments on three distinct diseases, influenza, mpox, and measles, with customized metrics. The high R-squared values during evaluation and reevaluation outperform the other state-of-the-art models in several contexts. The results indicate that CEL adapts well to incremental data. CEL's robustness emphasizes its minimal 65% forgetting rate and 18% higher memory stability compared to existing benchmark studies. This study highlights CEL's versatility in disease outbreak prediction by addressing evolving data with temporal patterns. It offers a valuable model for proactive disease control with accurate and timely predictions.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143523276","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

MultiKD-DTA: Enhancing Drug-Target Affinity Prediction Through Multiscale Feature Extraction.

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-02-28 DOI: 10.1007/s12539-025-00697-4

Riqian Hu, Ruiquan Ge, Guojian Deng, Jin Fan, Bowen Tang, Changmiao Wang

{"title":"MultiKD-DTA: Enhancing Drug-Target Affinity Prediction Through Multiscale Feature Extraction.","authors":"Riqian Hu, Ruiquan Ge, Guojian Deng, Jin Fan, Bowen Tang, Changmiao Wang","doi":"10.1007/s12539-025-00697-4","DOIUrl":"https://doi.org/10.1007/s12539-025-00697-4","url":null,"abstract":"The discovery and development of novel pharmaceutical agents is characterized by high costs, lengthy timelines, and significant safety concerns. Traditional drug discovery involves pharmacologists manually screening drug molecules against protein targets, focusing on binding within protein cavities. However, this manual process is slow and inherently limited. Given these constraints, the use of deep learning techniques to predict drug-target interaction (DTI) affinities is both significant and promising for future applications. This paper introduces an innovative deep learning architecture designed to enhance the prediction of DTI affinities. The model ingeniously combines graph neural networks, pre-trained large-scale protein models, and attention mechanisms to improve performance. In this framework, molecular structures are represented as graphs and processed through graph neural networks and multiscale convolutional networks to facilitate feature extraction. Simultaneously, protein sequences are encoded using pre-trained ESM-2 large models and processed with bidirectional long short-term memory networks. Subsequently, the molecular and protein embeddings derived from these processes are integrated within a fusion module to compute affinity scores. Experimental results demonstrate that our proposed model outperforms existing methods on two publicly available datasets.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143523301","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