检索结果-内蒙古大学图书馆

ieee TRANSACTIONS ON computational biology AND bioinformatics 2025年第2期22卷 867-876页

作者： Li, Yaoyao Yan, Yajie Yuan, Xiguo Xidian Univ Hangzhou Inst Technol Hangzhou 311231 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710126 Peoples R China

Read depth (RD) signals anomaly-based copy number variation (CNV) detection methods using whole genome sequencing data are affected by the measurement scale and parameters, and the breakpoint of CNVs is easily influenced by the sizes of windows in preparing RD signals. In this study, we propose an integrated approach for somatic CNV and breakpoint detection, COPCNVBD, which builds an improved Copula-Based Outlier Detector (COPOD) anomaly detection method to infer approximate locations of CNVs without hyper-parameters. Then, COPCNVBD first regards the precise CNV breakpoint location as the image boundary detection and makes use of the pair-end mapping (PEM) reads information to design a CNV breakpoint identification strategy. We use simulation datasets with different tumor purity and coverage settings and real samples to demonstrate the performance of COPCNVBD and compare it with peer-popular tools. Simulation results show that COPCNVBD has a superior comprehensive performance even on low coverage data with low tumor purity. Results on three real cancer samples show that our proposed COPCNVBD is able to detect moderate CNVs and has a high consistency. The proposed COPCNVBD can be as a tool for the analysis of CNVs in the genome.

关键词： bioinformatics Genomics Tumors Training Sequential analysis Image edge detection computational biology Artificial intelligence Approximation algorithms Noise Copy number variation read depth anomaly detection pair-ended mapping accurate breakpoint

来源：评论

学校读者我要写书评

暂无评论

scCDG: A Method Based on DAE and GCN for scRNA-Seq Data Analysis

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第6期19卷 3685-3694页

作者： Wang, Hai-Yun Zhao, Jian-Ping Su, Yan-Sen Zheng, Chun-Hou Xinjiang Univ Coll Math & Syst Sci Urumqi 830046 Xinjiang Peoples R China Xinjiang Univ Inst Math & Phys Coll Math & Syst Sci Urumqi 830046 Xinjiang Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230039 Anhui Peoples R China

Identifying cell types is one of the main goals of single-cell RNA sequencing (scRNA-seq) analysis, and clustering is a common method for this item. However, the massive amount of data and the excess noise level bring challenge for single cell clustering. To address this challenge, in this paper, we introduced a novel method named single-cell clustering based on denoising autoencoder and graph convolution network (scCDG), which consists of two core models. The first model is a denoising autoencoder (DAE) used to fit the data distribution for data denoising. The second model is a graph autoencoder using graph convolution network (GCN), which projects the data into a low-dimensional space (compressed) preserving topological structure information and feature information in scRNA-seq data simultaneously. Extensive analysis on seven real scRNA-seq datasets demonstrate that scCDG outperforms state-of-the-art methods in some research sub-fields, including single cell clustering, visualization of transcriptome landscape, and trajectory inference.

关键词： Noise reduction Convolution Computer architecture Microprocessors Sequential analysis Encoding Decoding Clustering scRNA-seq denoising autoencoder (DAE) graph convolution network (GCN)

来源：评论

学校读者我要写书评

暂无评论

Utilizing Comprehensive Biological Network to Improve Accuracy of computational Drug Repurposing

Utilizing Comprehensive Biological Network to Improve Accura...

引用

ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Aleksandar Poleksic Department of Computer Science University of Northern Iowa Cedar Falls IA USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Drug repurposing can overcome challenges associated with expensive and time-consuming drug discovery pipelines by quickly finding new uses for existing drugs. computational drug repurposing is founded on the principle that similar drugs treat similar diseases. Thus, the research in the field has traditionally focused on defining and implementing measures of drug and disease similarities that closely resemble similarities in the observed drug pharmacological profiles and disease pathways. In this study, we incorporate comprehensive data on biological relationships and interactions to arrive at a fast and accurate method for completing the existing knowledge on drug-disease associations and, in turn, find new indications for FDA approved drugs.

关键词： Drugs Accuracy computational modeling Systems biology Pipelines Machine learning Predictive models Biomedical measurement Drug discovery Diseases

来源：评论

学校读者我要写书评

暂无评论

Research on the mechanism of ncRNA in neurogenesis based on deep learning bioinformatics algorithm

Research on the mechanism of ncRNA in neurogenesis based on ...

引用

2024 International conference Optoelectronic Information and Optical Engineering, OIOE 2024

作者： Zhu, Mohan College of Life Sciences Sichuan University Sichuan China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

In the new era of science and technology, the research and application of deep learning algorithms for biological information are bringing revolutionary breakthroughs to the medical field. Recently, researchers have used deep learning techniques by integrating the mechanisms of long non-coding RNAs (lncRNAs) in neurogenesis, ensuring they work together to support normal development and functional maintenance of the nervous system. Non-coding RNA (ncRNA) constitutes a higher proportion of nucleic acids compared to coding RNA. Unlike protein-coding RNA, ncRNA is transcribed from DNA but does not undergo translation into proteins;instead, it functions as a functional RNA molecule. In recent years, research on ncRNA has deepened, and the development of ncRNA recognition algorithms has aided in understanding the functions of ncRNA, gradually revealing its growing importance in regulatory processes. An increasing number of ncRNAs have established their significant roles in the fields of disease, reprogramming, and differentiation. In disease research, researchers have developed databases and computational methods based on these databases to predict the relationships between diseases and related ncRNAs. In the field of reprogramming and differentiation, miRNAs, a well-studied subgroup of ncRNAs, have shown considerable progress. MicroRNAs can act on transcription factors (TFs) and other regulatory factors involved in reprogramming, thereby enhancing reprogramming efficiency. From a developmental biology perspective, certain miRNAs, such as miRNA-124 or let-7a, can interact with their target genes to regulate neural cell development. Recent studies have increasingly focused on the role of long non-coding RNAs (lncRNAs) in the fields of cellular reprogramming and developmental biology. Researchers have discovered that specific lncRNAs promote neuron differentiation, influence the expression of neighboring genes, and serve as key modulators of signaling pathways. Additionally

关键词： Stem cells

来源：评论

学校读者我要写书评

暂无评论

IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATIC...

引用

ieee TRANSACTIONS ON computational biology AND bioinformatics 2025年第1期22卷 203-215页

作者： Li, Ruijie Ning, Qiao Zhao, Yaomiao Guo, Shikai Li, Hui Dalian Maritime Univ Dept Informat Sci & Technol Dalian 116026 Peoples R China DUT Artificial Intelligence Inst Dalian 116024 Peoples R China Dalian Key Lab Artificial Intelligence Dalian 116024 Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China

In recent years, microRNA (miRNA) has been recognized as crucial in the progression of human diseases. However, existing computational methods for identifying miRNA-disease associations often overlook the rich association information contained in specific long-distance pathways and lack effective exploration of potential associations. In this study, we propose a biologically interpretable "similarity-association-similarity" metapath and heterogeneous-hyper network (HeteroHyperNet) learning approach for miRNA-disease association prediction (MHMDA). In MHMDA, a "similarity-association-similarity" multi-hop metapaths learning method based on hierarchical attention perception is proposed to explore specific long-distance associated pathway information connecting potentially associated miRNAs and diseases. In addition, a HeteroHyperNet learning approach integrating heterogeneous network and hyper network is designed to progressively learn direct association information and potential association information between miRNA and disease. The "similarity-association-similarity" metapath with hierarchical attention significantly enhances the learning of long-distance biological associations, while the HeteroHyperNet comprehensively learns the known and potential associations of miRNA-disease, greatly improving the richness and accuracy of information. A large number of experimental results show that MHMDA has demonstrated excellent performance in the prediction of miRNA-disease association. In addition, cross independent dataset experiment and cold start experiment on miRNA and disease prove the effectiveness of MHMDA on sparse association points, and its stability and reliability in predicting potential miRNA-disease association are further confirmed.

关键词： Diseases Predictive models Autoencoders Feature extraction Biological system modeling bioinformatics Kernel computational biology Heterogeneous networks Accuracy Potential miRNA-disease association similarity-association-similarity metapaths heterogeneous-hyper network graph convolutional networks

来源：评论

学校读者我要写书评

暂无评论

AI-Driven Drug Discovery: Unravelling the Potential of Generative Adversarial Networks (GANs) in Pharmaceutical Research 3

AI-Driven Drug Discovery: Unravelling the Potential of Gener...

引用

3rd International conference on Technological Advancements in computational Sciences, ICTACS 2023

作者： Burri, Srinivasa Rao Diallo, Mamadou Yero Sharma, Lakshay Dutt, Vishal Western Union DenverCO United States Sharda University Computer Science & Engineering Greater Noida India Indraprastha Institute of Information Technology Computer Science & Applied Mathematics Delhi India Chandigarh University Department of Cse Punjab Mohali India

ISBN: (纸本)9798350342338

This paper examines how AI has revolutionised drug development and medical research using the ChEMBL dataset. The primary study areas are AI-driven therapeutic target identification., computational approaches in drug development., drug repurposing for COVID-19 therapies., and AI methods for natural leather flaw detection. Target selection must balance novelty and confidence., and AI-driven therapeutic target identification is considered. Structure-based virtual screening and profound learning predictions of ligand properties and target activities are considered for application in scaling up to broader chemical spaces. AI is used to discover new links between drugs., targets., and diseases and treat COVID-19. The paper also highlights this field's enforcement challenges and offers solutions. A Generative Adversarial Network (GAN)-based automatic flaw identification system for natural leather is another topic of study. The results show that the suggested strategy is economical and accurate., despite limitations and biases. AI has revolutionised medical diagnostics., medication development., and precision medicine., making this work meaningful. This paper's findings offer a cross-disciplinary perspective on artificial intelligence's potential in healthcare., revealing knowledge gaps and suggesting further research. © 2023 ieee.

关键词： Artificial intelligence Chemical biology Database (ChEMBL) Dataset computational Approaches COVID-19 Treatments Defect Identification Drug Discovery Drug Repurposing Generative Adversarial Network (GAN) Precision Medicine Therapeutic Target Identification

来源：评论

学校读者我要写书评

暂无评论

Uncertainty Quantification Using Subsampling for Assembly-Free Estimates of Genomic Distance and Phylogenetic Relationships 26th

Uncertainty Quantification Using Subsampling for Assembly-F...

引用

26th International conference on Research in computational Molecular biology, RECOMB 2022

作者： Rachtman, Eleonora Sarmashghi, Shahab Bafna, Vineet Mirarab, Siavash Bioinformatics and Systems Biology Graduate Program UC San Diego La JollaCA United States Department of Electrical and Computer Engineering UC San Diego La JollaCA United States Department of Computer Science and Engineering UC San Diego La JollaCA United States

Computing the distance between two genomes, often without using alignments or even access to assembled sequences, is fundamental to many downstream analyses. © 2022, The Author(s), under exclusive license to Spri... 详细信息

ISBN: (纸本)9783031047480

关键词： Uncertainty analysis

来源：评论

学校读者我要写书评

暂无评论

Guest Editorial for Selected Papers From BIOKDD 2019

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第3期18卷 809-810页

作者： Yan, Da Thankachan, Sharma Chen, Jake Y. Univ Alabama Birmingham Dept Comp Sci Birmingham AL 35294 USA Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA Univ Alabama Birmingham UAB Dept Genet UAB Informat Inst Genet Comp Sci & Informat Sect Birmingham AL 35294 USA

The papers in this special section were presented at the 18th International Workshop on Data Mining in bioinformatics (BIOKDD), held in conjunction with the ACM SIGKDD International conference on Knowledge Discovery a... 详细信息

关键词： Special Issues And Sections bioinformatics Biological System Modeling Data Mining Big Data Meetings

来源：评论

学校读者我要写书评

暂无评论

COMBING: Clustering in Oncology for Mathematical and Biological Identification of Novel Gene Signatures

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第6期19卷 3317-3331页

作者： Battistella, Enzo Vakalopoulou, Maria Sun, Roger Estienne, Theo Lerousseau, Marvin Nikolaev, Sergey Andres, Emilie Alvarez Carre, Alexandre Niyoteka, Stephane Robert, Charlotte Paragios, Nikos Deutsch, Eric Univ Paris Saclay Cent Supelec Math & Informat Complexite & Syst F-91190 Gif Sur Yvette France Univ Paris Saclay Cent Supelec Inria F-91190 Gif Sur Yvette France Gustave Roussy Canc Campus Noesia Ctr Artificial Intelligence Radiat Therapy Gustave Roussy Cent Supelec TheraPanacea F-94805 Villejuif France Univ Paris Saclay Inst Gustave Roussy INSERM U1030Mol Radiotherapy & Innovat Therapeut F-94800 Villejuif France Univ Paris 07 St Louis Hosp F-75010 Paris France

Precisionmedicine is a paradigm shift in healthcare relying heavily on genomics data. However, the complexity of biological interactions, the large number of genes aswell as the lack of comparisons on the analysis of data, remain a tremendous bottleneck regarding clinical adoption. In this paper, we introduce a novel, automatic and unsupervised framework to discover low-dimensional gene biomarkers. Our method is based on the LP-Stability algorithm, a high dimensional center-based unsupervised clustering algorithm. It offers modularity as concerns metric functions and scalability, while being able to automatically determine the best number of clusters. Our evaluation includes both mathematical and biological criteria to define a quantitativemetric. The recovered signature is applied to a variety of biological tasks, including screening of biological pathways and functions, and characterization relevance on tumor types and subtypes. Quantitative comparisons among different distance metrics, commonly used clusteringmethods and a referential gene signature used in the literature, confirmstate of the art performance of our approach. In particular, our signature, based on 27 genes, reports at least 30 times bettermathematical significance (average Dunn's Index) and 25% better biological significance (average Enrichment in Protein-Protein Interaction) than those produced by other referential clustering methods. Finally, our signature reports promising results on distinguishing immune inflammatory and immune desert tumors, while reporting a high balanced accuracy of 92% on tumor types classification and averaged balanced accuracy of 68% on tumor subtypes classification, which represents, respectively 7% and 9% higher performance compared to the referential signature.

关键词： Clustering algorithms biology Tumors Measurement Genomics bioinformatics Indexes Clustering predictive signature biomarkers genomics multi-tumor association

来源：评论

学校读者我要写书评

暂无评论

On an Approach to the Study of Distances Between DNA Sequences

On an Approach to the Study of Distances Between DNA Sequenc...

引用

ieee International conference on bioinformatics and computational biology (ICBCB)

作者： Boris Melnikov Faculty of Computational Mathematics and Cybernetics Shenzhen MSU-BIT University Shenzhen China

ISBN: (数字)9798350375749

ISBN: (纸本)9798350375756

We are considering an approach to the study of the proximity of genomes. The approach is based on the definition and consideration of a triangular norm (triangular badness) for the distances between genomes and, based on it, the norm for the corresponding distance matrices. We used mtDNA in our research, and of the algorithms for comparing genomes, first of all, the Needleman - Wunsch algorithm algorithm (metric). However, the approach can be applied to any relevant genome regions and any described algorithms for calculating the proximity of strings. As results, we obtained, among other things, a comparison of variants of different metrics between genomes, the results of reconstruction of partially filled distance matrices, the description of the application of the pair correlation method for the sequences of badness of the triangles of distance matrices, as well as descriptions of possible generalizations for various similar problems.

关键词： Measurement Inverse problems Databases Heuristic algorithms Data preprocessing Genomics Traveling salesman problems Approximation algorithms Filling Optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：