检索结果-内蒙古大学图书馆

ieee International conference on bioinformatics and computational biology (ICBCB)

作者： Miri Weiss Cohen Dept. of Software Engineering Braude College of Engineering Karmiel Israel

ISBN: (数字)9798350375749

ISBN: (纸本)9798350375756

Based on Magnetic Resonance Imaging (MRI) scans and Convolutional Neural Network (CNN) architectures with optimized hyperparameters, this study proposes a machine learning system for classification of brain malignant tumors. Using a two-level approach, the first level identifies the most suitable MRI modalities for classification by evaluating the performance of the modalities under different hyperparameters. The second level consists of the development and comparison of two CNN architectures, Inceptionv3 and DenseNet121, based on their respective training methods. As part of the optimization process, various hyperparameters are tested, such as epochs, batch sizes, learning rates, and dropout rates. Additionally, it was found that learning rate has an important impact on the training phase with a lower learning rate resulting in a smoother, more consistent process. As a result of optimizing the system, it has been demonstrated that it can accurately classify brain malignant tumors, particularly Glioblastomas.

关键词： Training Magnetic resonance imaging Malignant tumors Computer architecture Hyperparameter optimization Convolutional neural networks bioinformatics Optimization computational biology

来源：评论

学校读者我要写书评

暂无评论

Insights into the Evolution of GC Content in Coronavirus Genome Sequences

Insights into the Evolution of GC Content in Coronavirus Gen...

引用

ieee International conference on bioinformatics and computational biology (ICBCB)

作者： Jie Li Wendi Xue Chen Cao Yayue Ling School of Biology and Food Engineering Changshu Institute of Technology Suzhou China Faculty of Science and Engineering University of Nottingham Ningbo China Ningbo China

ISBN: (数字)9798350375749

ISBN: (纸本)9798350375756

GC content is an important feature of genomic nucleic acid sequence composition. The change of GC content is closely related to the origin, evolution, and functional variation of the gene. Coronavirus can cause interspecific transmission and pose a serious threat to human and animal health. At present, it has led to three major epidemics that have caused global panic. This paper attempts to systematically analyze the relationship between coronavirus classification and GC content in the genome. Through the reference sequence of coronavirus provided by NCBI, the GC content characteristics of coronavirus were studied by MEGA, Mafft, and other software. The results showed that the GC content of coronavirus was closely related to the hypotype structure, the content was 30%-50%, and some coronaviruses had a certain affinity. In conclusion, this paper provides new insights for understanding the relevant knowledge of SARS-CoV-2 and is helpful to study the pathogenicity and evolutionary mechanism of novel coronavirus.

关键词： COVID-19 Epidemics Evolution (biology) Animals Genomics Software bioinformatics computational biology

来源：评论

学校读者我要写书评

暂无评论

Percolate: An Exponential Family JIVE Model to Design DNA-Based Predictors of Drug Response 27th

Percolate: An Exponential Family JIVE Model to Design DNA-B...

引用

27th International conference on Research in computational Molecular biology, RECOMB 2023

作者： Mourragui, Soufiane M. C. Loog, Marco van Nee, Mirrelijn de Wiel, Mark A van Reinders, Marcel J. T. Wessels, Lodewyk F. A. Computational Cancer Biology Division of Molecular Carcinogenesis Oncode Institute The Netherlands Cancer Institute Amsterdam Netherlands Faculty of EEMCS Delft University of Technology Delft Netherlands Department of Computer Science University of Copenhagen Copenhagen Denmark Department of Epidemiology and Biostatistics Amsterdam Public Health research institute Amsterdam University medical centers Amsterdam Netherlands MRC Biostatistics Unit University of Cambridge Cambridge United Kingdom Leiden Computational Biology Center Leiden University Medical Center Leiden Netherlands

ISBN: (纸本)9783031291180

Motivation: Anti-cancer drugs may elicit resistance or sensitivity through mechanisms which involve several genomic layers. Nevertheless, we have demonstrated that gene expression contains most of the predictive capacity compared to the remaining omic data types. Unfortunately, this comes at a price: gene expression biomarkers are often hard to interpret and show poor robustness. Results: To capture the best of both worlds, i.e. the accuracy of gene expression and the robustness of other genomic levels, such as mutations, copy-number or methylation, we developed Percolate, a computational approach which extracts the joint signal between gene expression and the other omic data types. We developed an out-of-sample extension of Percolate which allows predictions on unseen samples without the necessity to recompute the joint signal on all data. We employed Percolate to extract the joint signal between gene expression and either mutations, copy-number or methylation, and used the out-of sample extension to perform response prediction on unseen samples. We showed that the joint signal recapitulates, and sometimes exceeds, the predictive performance achieved with each data type individually. Importantly, molecular signatures created by Percolate do not require gene expression to be evaluated, rendering them suitable to clinical applications where only one data type is available. Availability: Percolate is available as a Python 3.7 package and the scripts to reproduce the results are available here. © 2023, The Author(s).

关键词： Gene expression

来源：评论

学校读者我要写书评

暂无评论

Characterizing The Landscape Of Viral Expression In Cancer By Deep Learning

Characterizing The Landscape Of Viral Expression In Cancer B...

引用

2022 ieee International conference on bioinformatics and Biomedicine, BIBM 2022

作者： Elbasir, Abdurrahman Ye, Ying Schaffer, Daniel E. Wickramasinghe, Jayamanna Hao, Xue Lieberman, Paul M. Morris, Quaid Zhang, Rugang Schaffer, Alejandro A. Auslander, Noam The Wistar Institute PhiladelphiaPA19104 United States Carnegie Melon University Computational Biology Department PittsburghPA15213 United States Solan Kettering Institute Computational and Systems Biology New York CityNY10065 United States National Institute of Health Cancer Data Science Laboratory BethesdaMD20892 United States

ISBN: (纸本)9781665468190

About 15% of human cancer cases are attributed to viral infections. To date, virus expression in tumor tissues has been mostly studied by aligning tumor RNA sequencing reads to databases of known viruses. To allow identification of divergent viruses and rapid characterization of the tumor virome, we developed viRNAtrap, an alignment-free pipeline to identify viral reads and assemble viral contigs. We apply viRNAtrap, which is based on a deep learning model trained to discriminate viral RNAseq reads, to 14 cancer types from The Cancer Genome Atlas (TCGA). We find that expression of exogenous cancer viruses is associated with better overall survival. In contrast, expression of human endogenous viruses is associated with worse overall survival. Using viRNAtrap, we uncover expression of unexpected and divergent viruses that have not previously been implicated in cancer. The viRNAtrap pipeline provides a way forward to study viral infections associated with different clinical conditions. © 2022 ieee.

关键词： Viruses

来源：评论

学校读者我要写书评

暂无评论

Multi-Scale Double-Layer Multiple Instance Learning Algorithm for Whole Slide Image Classification of Primary Gastric Lymphoma

Multi-Scale Double-Layer Multiple Instance Learning Algorith...

引用

ieee International conference on bioinformatics and computational biology (ICBCB)

作者： Jiawei Quan Jingxuan Ye Xinlin Zhang Ziwei Hu Yongxin Mao Yuntao Liu Tong Tong Gang Chen College of Physics and Information Engineering Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Translational Cancer Medicine Fujian Medical University Fuzhou China

ISBN: (数字)9798350375749

ISBN: (纸本)9798350375756

Primary Gastric Lymphoma (PGL) is a type of malignant lymphoma that originates in the stomach. Timely and precise diagnosis and classification are crucial for effective treatment and predicting outcomes. Advances in artificial intelligence, especially deep learning, have ushered in a new era for medical diagnostics. Weakly supervised learning, in particular, is gaining traction for classifying histopathological whole slide images (WSIs). Nonetheless, there remain challenges, such as the classification of small sample cohorts. To address these issues, we propose a novel multi-scale double-layer multiple instance learning classification algorithm for accurate classification of PGL. This algorithm leverages pseudo-bags-a technique to effectively augment the sample size-and employs multiple instance learning to distill essential features. It then constructs an enhanced classification model utilizing a multi-scale, double-layer approach. Validation using clinical pathological data indicates that our method outperforms other latest methods in classifying primary gastric lymphoma, as well as obtains superior results on the CAMELYON-16 dataset. Our framework holds promise for broader adoption across various medical applications.

关键词： Deep learning Stomach Pathology Accuracy Weak supervision Learning (artificial intelligence) Feature extraction Classification algorithms Medical diagnosis Cancer

来源：评论

学校读者我要写书评

暂无评论

Sorting Signed Permutations by Inverse Tandem Duplication Random Losses

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第6期18卷 2177-2188页

作者： Hartmann, Tom Bannach, Max Middendorf, Martin Univ Leipzig Fac Math & Comp Sci Swarm Intelligence & Complex Syst Grp Augustuspl 10 D-04109 Leipzig Germany Univ Lubeck Inst Theoret Comp Sci D-23562 Lubeck Germany

Gene order evolution of unichromosomal genomes, for example mitochondrial genomes, has been modelled mostly by four major types of genome rearrangements: inversions, transpositions, inverse transpositions, and tandem duplication random losses. Generalizing models that include all those rearrangements while admitting computational tractability are rare. In this paper, we study such a rearrangement model, namely the inverse tandem duplication random loss (iTDRL) model, where an iTDRL duplicates and inverts a continuous segment of a gene order followed by the random loss of one of the redundant copies of each gene. The iTDRL rearrangement has currently been proposed by several authors suggesting it to be a possible mechanisms of mitochondrial gene order evolution. We initiate the algorithmic study of this new model of genome rearrangement by proving that a shortest rearrangement scenario that transforms one given gene order into another given gene order can be obtained in quasilinear time. Furthermore, we show that the length of such a scenario, i.e., the minimum number of iTDRLs in the transformation, can be computed in linear time.

关键词： Sorting Genomics computational modeling Approximation algorithms Biological cells Evolution (biology) Biological system modeling Inverse tandem duplication random loss mitochondria genome rearrangements sorting problem distance problem

来源：评论

学校读者我要写书评

暂无评论

EEG-DIF: Early Warning of Epileptic Seizures through Generative Diffusion Model-based Multi-channel EEG Signals Forecasting 24

EEG-DIF: Early Warning of Epileptic Seizures through Generat...

引用

15th conference on bioinformatics computational biology and Health Informatics

作者： Jiang, Zekun Dai, Wei Wei, Qu Qin, Ziyuan Li, Kang Zhang, Le Sichuan Univ Coll Comp Sci Chengdu Sichuan Peoples R China Sichuan Univ West China Hosp Chengdu Sichuan Peoples R China Case Western Reserve Univ Sch Engn Cleveland OH 44106 USA

ISBN: (纸本)9798400713026

Multi-channel EEG signals are commonly used for diagnosis and assessment of diseases such as epilepsy. Currently, various EEG diagnostic algorithms based on deep learning have been developed. However, most research efforts focus solely on diagnosing and classifying current signal data, but not consider the prediction of future trends for early ***, since multi-channel EEG can be essentially regarded as the spatio-temporal signal data received by detectors at different locations in the brain, how to construct spatio-temporal information representations of EEG signals to facilitate future trend prediction for multi-channel EEG becomes an important problem. This study proposes a multi-signal prediction algorithm based on generative diffusion models (EEG-DIF), which transforms the multi-signal forecasting task into an image completion task, allowing for comprehensive representation and learning of the spatio-temporal correlations and future developmental patterns of multi-channel EEG signals. Based on our method, we can achieve multi-signal forecasting by using one diffusion model to simultaneously predict future trends for multichannel EEG signals. We also integrate a CNN-LSTM classifier into the backend of EEG-DIF and develop an early warning diagnostic model for epileptic seizures based on the generated EEG signals, providing accurate early seizure ***, we employ a publicly available epilepsy EEG dataset to construct and validate the EEG-DIF. The results demonstrate that our method can accurately predict future trends for multi-channel EEG signals simultaneously. Furthermore, the early warning accuracy for epilepsy seizures based on the generated EEG data reaches 0.89. In general, EEG-DIF provides a novel approach for characterizing multi-channel EEG signals and an innovative early warning algorithm for epilepsy seizures, aiding in optimizing and enhancing the clinical diagnosis process.

关键词： Diffusion model multi-signal forecasting EEG epileptic seizures Artificial intelligence spatio-temporal representation

来源：评论

学校读者我要写书评

暂无评论

Assessing the Impact of Vulnerabilities on Confidentiality, Integrity, and Availability in Smart Systems 2

Assessing the Impact of Vulnerabilities on Confidentiality, ...

引用

2nd International conference on Informatics, ICI 2023

作者： Minhass, Shubham Chauhan, Ritu Kaur, Harleen Amity University Amity Institute of Information Technology UP India Amity University IoT and Artificial Intelligence Lab Center for Computational Biology and Bioinformatics UP India Jamia Hamdard Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350343830

As Internet of Things (IoT) technologies proliferate and smart devices become more and more integrated into our daily lives and workplaces, it is becoming more and more important to address the complex issues of data security and privacy within smart homes and buildings. Even though the Internet of Things (IoT) has many benefits, such as remote control, real-time monitoring, and sophisticated security features, it also raises serious concerns about how sensitive data is protected. By examining the particular problems associated with protecting sensitive data in the IoT era and guaranteeing the continuous provision of dependable services within smart environments, this research paper seeks to close a significant knowledge gap. In order to do this, the research focuses on the complex data-centric features of smart ecosystems, including data processing, generation, and storage. This paper thoroughly examines the range of current IoT technologies, data management practices, and privacy challenges by using a qualitative research approach. It also offers a thorough examination of the privacy protections and security measures that are now in place in smart buildings and homes. This research focuses primarily on the interface between technology, security, and privacy of data. This work effectively fills in the significant research vacuum by clarifying the specific issues it seeks to solve. It provides useful advice and insights that can help individuals and organizations navigate the complex world of smart technologies, securely store their data, and realize the full potential of these game-changing advancements. © 2023 ieee.

关键词： Sensitive data

来源：评论

学校读者我要写书评

暂无评论

Resting State Functional Connectivity Analysis During General Anesthesia: A High-Density EEG Study

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第1期19卷 3-13页

作者： Bi, Hui Cao, Shumei Yan, Hanying Jiang, Zhongyi Zhang, Jun Zou, Ling Changzhou Univ Sch Informat Sci & Engn Changzhou 213164 Jiangsu Peoples R China Fudan Univ Huashan Hosp Dept Anesthesiol Shanghai 200040 Peoples R China Fudan Univ Shanghai Canc Ctr Dept Anesthesiol Shanghai 200032 Peoples R China Key Lab Brain Machine Collaborat Intelligence Fdn Hangzhou 310018 Zhejiang Peoples R China

The depth of anesthesia monitoring is helpful to guide administrations of general anesthetics during surgical procedures, however, the conventional 2-4 channels electroencephalogram (EEG) derived monitors have their limitations in monitoring conscious states due to low spatial resolution and suboptimal algorithm. In this study, 256-channel high-density EEG signals in 24 subjects receiving three types of general anesthetics (propofol, sevoflurane and ketamine) respectively were recorded both before and after anesthesia. The raw EEG signals were preprocessed by EEGLAB 14.0. Functional connectivity (FC) analysis by traditional coherence analysis (CA) method and a novel sparse representation (SR) method. And the network parameters, average clustering coefficient (ACC) and average shortest path length (ASPL) before and after anesthesia were calculated. The results show SR method find more significant FC differences in frontal and occipital cortices, and whole brain network (p<0.05). In contrast, CA can hardly obtain consistent ASPL in the whole brain network (p>0.05). Further, ASPL calculated by SR for whole brain connections in all of three anesthesia groups increased, which can be a unified EEG biomarker of general anesthetics-induced loss of consciousness (LOC). Therefore FC analysis based on SR analysis has better performance in distinguishing anesthetic-induced LOC from awake state.

关键词： Anesthesia electroencephalogram (EEG) functional connectivity sparse representation

来源：评论

学校读者我要写书评

暂无评论

Prediction of Transposon Insertion Sites in the Human Genome Using Deep Learning Techniques 2

Prediction of Transposon Insertion Sites in the Human Genome...

引用

2nd ieee International conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Preeti, P. Nakul, Mihir Singh, Abhishek Dudeja, Chhavi Kanishka Rawal, Kamal Amity University Noida Centre for Computational Biology and Bioinformatics AIB Noida India

ISBN: (纸本)9798350368109

Transposable elements (TEs) account for approximately 44% of the human genome but less than.05% are known to be active. In contrast, these TEs are highly mobile in the genome which can result genomic rearrangement and several associated human diseases. It is notoriously difficult to predict the properties of DNA sequences at prospective insertion sites that make them good elements. This study proposes a deep learning model that leverages DNA properties to predict potential transposon insertion sites in nucleotide sequences, focusing on the active L1 and Alu elements identified in human chromosome 3 using RepeatMasker. Positive datasets comprise 30 bp upstream sequences of detected L1 and Alu elements, while negative datasets are randomly selected 30 bp sequences without these elements. Employing DNASCANNER v2, we computed 159 DNA properties encompassing nucleotide composition, structural traits, physicochemical attributes, thermodynamics, and mechanics. The data was partitioned into training, validation, and testing sets, scaled, and normalized. Fully Connected Layers were utilized to construct the deep learning models, which underwent hyperparameter tuning and training on the training subset. Evaluation metrics like sensitivity, precision, accuracy, recall, specificity, and AUC assessed model performance. The proposed Alu-L1-TIS DL model aims to provide a versatile predictive tool for identifying potential Alu or L1 TIS in the human genome. The web server for TIS predictions can be accessed at https://***/. This research contributes to a better understanding of the role of transposable elements in human diseases and genomic instability. © 2024 ieee.

关键词： Nucleotides

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：