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

ABTrans: A Transformer-based Model for Predicting Interaction between Anti-Aβ Antibodies and Peptides. ABTrans：基于变压器的抗 Aβ 抗体与多肽相互作用预测模型

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/s12539-024-00664-5

Yuhong Su, Xincheng Zeng, Lingfeng Zhang, Yanlin Bian, Yangjing Wang, Buyong Ma

引用次数: 0

cascAGS: Comparative Analysis of SNP Calling Methods for Human Genome Data in the Absence of Gold Standard. cascAGS：缺乏黄金标准时人类基因组数据 SNP 调用方法的比较分析

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-03-01 Epub Date: 2024-10-23 DOI: 10.1007/s12539-024-00653-8

Qianqian Song, Taobo Hu, Baosheng Liang, Shihai Li, Yang Li, Jinbo Wu, Shu Wang, Xiaohua Zhou

{"title":"cascAGS: Comparative Analysis of SNP Calling Methods for Human Genome Data in the Absence of Gold Standard.","authors":"Qianqian Song, Taobo Hu, Baosheng Liang, Shihai Li, Yang Li, Jinbo Wu, Shu Wang, Xiaohua Zhou","doi":"10.1007/s12539-024-00653-8","DOIUrl":"10.1007/s12539-024-00653-8","url":null,"abstract":"The development of third-generation sequencing has accelerated the boom of single nucleotide polymorphism (SNP) calling methods, but evaluating accuracy remains challenging owing to the absence of the SNP gold standard. The definitions for without-gold-standard and performance metrics and their estimation are urgently needed. Additionally, the possible correlations between different SNP loci should also be further explored. To address these challenges, we first introduced the concept of a gold standard and imperfect gold standard under the consistency framework and gave the corresponding definitions of sensitivity and specificity. A latent class model (LCM) was established to estimate the sensitivity and specificity of callers. Furthermore, we incorporated different dependency structures into LCM to investigate their impact on sensitivity and specificity. The performance of LCM was illustrated by comparing the accuracy of BCFtools, DeepVariant, FreeBayes, and GATK on various datasets. Through estimations across multiple datasets, the results indicate that LCM is well-suitable for evaluating callers without the SNP gold standard, and accurate inclusion of the dependency between variations is crucial for better performance ranking. DeepVariant has a higher sum of sensitivity and specificity than other callers, followed by GATK and BCFtools. FreeBayes has low sensitivity but high specificity. Notably, appropriate sequencing coverage is another important factor for precise callers' evaluation. Most importantly, a web interface for assessing and comparing different callers was developed to simplify the evaluation process.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"1-11"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142499766","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

EfficientNet-resDDSC: A Hybrid Deep Learning Model Integrating Residual Blocks and Dilated Convolutions for Inferring Gene Causality in Single-Cell Data. EfficientNet-resDDSC：整合残差块和稀释卷积的混合深度学习模型，用于推断单细胞数据中的基因因果关系。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-03-01 Epub Date: 2024-11-22 DOI: 10.1007/s12539-024-00667-2

Aimin Li, Mingyue Li, Rong Fei, Saurav Mallik, Bo Hu, Yue Yu

{"title":"EfficientNet-resDDSC: A Hybrid Deep Learning Model Integrating Residual Blocks and Dilated Convolutions for Inferring Gene Causality in Single-Cell Data.","authors":"Aimin Li, Mingyue Li, Rong Fei, Saurav Mallik, Bo Hu, Yue Yu","doi":"10.1007/s12539-024-00667-2","DOIUrl":"10.1007/s12539-024-00667-2","url":null,"abstract":"Gene Regulatory Networks (GRNs) reveal complex interactions between genes in organisms, crucial for understanding the life system's operation. The rapid development of biotechnology, especially single-cell RNA sequencing (scRNA-seq), has generated a large amount of scRNA-seq data, which can be analyzed to explore the regulatory relationships between genes at the single-cell level. Previous models used to construct GRNs mainly aim at constructing associative relationships between genes, but usually fail to accurately reveal the causality between genes. Therefore, we present a hybrid deep learning model called EfficientNet-resDDSC (the EfficientNet with Residual Blocks and Depthwise Separable Dilated Convolutions) to infer causality between genes. The model inherits the basic structure of EfficientNet-B0 and incorporates residual blocks as well as dilated convolutions. The model's ability to extract low-level features at the primary stage is enhanced by introducing residual blocks. The model combines Depthwise Separable Convolution (DSC) in the inverted linear bottleneck layers with the dilated convolutions to expand the model's receptive fields without increasing the computational effort. This design enables the model to comprehensively reveal potential relationships among different genes in high-dimensional and high-noise single-cell data. In comparison with the five existing deep learning network models, EfficientNet-resDDSC's overall performance is significantly better than others on four datasets. In this study, EfficientNet-resDDSC was further applied to construct GRNs for breast cancer patients, focusing on the related regulatory genes of the key gene BRCA1, which contributes to the advancement of breast cancer research and treatment strategies.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"166-184"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692978","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

DeepPD: A Deep Learning Method for Predicting Peptide Detectability Based on Multi-feature Representation and Information Bottleneck. 基于多特征表示和信息瓶颈的深度学习多肽可检测性预测方法。

IF 3.9 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2025-03-01 Epub Date: 2024-12-11 DOI: 10.1007/s12539-024-00665-4

Fenglin Li, Yannan Bin, Jianping Zhao, Chunhou Zheng

{"title":"DeepPD: A Deep Learning Method for Predicting Peptide Detectability Based on Multi-feature Representation and Information Bottleneck.","authors":"Fenglin Li, Yannan Bin, Jianping Zhao, Chunhou Zheng","doi":"10.1007/s12539-024-00665-4","DOIUrl":"10.1007/s12539-024-00665-4","url":null,"abstract":"Peptide detectability measures the relationship between the protein composition and abundance in the sample and the peptides identified during the analytical procedure. This relationship has significant implications for the fundamental tasks of proteomics. Existing methods primarily rely on a single type of feature representation, which limits their ability to capture the intricate and diverse characteristics of peptides. In response to this limitation, we introduce DeepPD, an innovative deep learning framework incorporating multi-feature representation and the information bottleneck principle (IBP) to predict peptide detectability. DeepPD extracts semantic information from peptides using evolutionary scale modeling 2 (ESM-2) and integrates sequence and evolutionary information to construct the feature space collaboratively. The IBP effectively guides the feature learning process, minimizing redundancy in the feature space. Experimental results across various datasets demonstrate that DeepPD outperforms state-of-the-art methods. Furthermore, we demonstrate that DeepPD exhibits competitive generalization and transfer learning capabilities across diverse datasets and species. In conclusion, DeepPD emerges as the most effective method for predicting peptide detectability, showcasing its potential applicability to other protein sequence prediction tasks.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"200-214"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142806788","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

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