检索结果-内蒙古大学图书馆

Comprehensive and critical evaluation of individualized pathway activity measurement tools on pan-cancer data (vol 21, bby097, 2018)

引用

BRIEFINGS IN bioinformatics 2020年第3期21卷 1118-1118页

作者： Lim, Sangsoo Lee, Sangseon Jung, Inuk Rhee, Sungmin Kim, Sun Interdisciplinary Program in Bioinformatics Seoul National University Seoul Korea Department of Computer Science and Engineering Seoul National University Seoul Korea Bioinformatics Institute Seoul National University Seoul Korea Department of Computer Science and Engineering Seoul National University Seoul Korea Interdisciplinary Program in Bioinformatics Seoul National University Seoul Korea Department of Computer Science and Engineering Seoul National University Seoul Korea Bioinformatics Institute Seoul National University Seoul Korea Corresponding author: Sun Kim Interdisciplinary Program in Bioinformatics Department of Computer Science and Engineering and Bioinformatics Institute Seoul National University Seoul 08826 Korea. Tel.: +82-2-880-7280 Fax: +82-2-888-9623 Email: sunkim.bioinfo@snu.ac.kr

Motivation Biological pathways are extensively used for the analysis of transcriptome data to characterize biological mechanisms underlying various phenotypes. There are a number of computational tools that summarize transcriptome data at the pathway level. However, there is no comparative study on how well these tools produce useful information at the cohort level, enabling comparison of many samples or *** In this study, we systematically compared and evaluated 13 different pathway activity inference tools based on 5 comparison criteria using pan-cancer data set. This study has two major contributions. First, our study provides a comprehensive survey on computational techniques used by existing pathway activity inference tools. The tools use different strategies and assume different requirements on data: input transformation, use of labels, necessity of cohort-level input data, use of gene relations and scoring metric. Second, we performed extensive evaluations on the performance of these tools. Because different tools use different methods to map samples to the pathway dimension, the tools are evaluated at the pathway level using five comparison criteria. Starting from measuring how well a tool maintains the characteristics of original gene expression values, robustness was also investigated by adding noise into gene expression data. Classification tasks on three clinical variables (tumor versus normal, survival and cancer subtypes) were performed to evaluate the utility of tools for their clinical applications. In addition, the inferred activity values were compared between the tools to see how similar they are along with the scoring schemes they use.

关键词： pathway analysis individualized pathway activity pathway activity inference pan-cancer analysis

来源：评论

学校读者我要写书评

暂无评论

Biomedical data and AI

引用

Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Modeling the causal regulatory network by integrating chromatin accessibility and transcriptome data

引用

National Science Review 2016年第2期3卷 240-251页

作者： Yong Wang Rui Jiang Wing Hung Wong Department of Statistics Department of Biomedical Data ScienceBio-X ProgramStanford University Academy of Mathematics and Systems Science National Center for Mathematics and Interdisciplinary SciencesChinese Academy of Sciences MOE Key Laboratory of Bioinformatics Bioinformatics Division and Center for Synthetic and Systems BiologyTNLISTDepartment of AutomationTsinghua University

Cell packs a lot of genetic and regulatory information through a structure known as chromatin,*** is wrapped around histone proteins and is tightly packed in a remarkable *** express a gene in a specific coding region,the chromatin would open up and DNA loop may be formed by interacting enhancers and ***,the mediator and cohesion complexes,sequence-specific transcription factors,and RNA polymerase Ⅱ are recruited and work together to elaborately regulate the expression *** is in pressing need to understand how the information,about when,where,and to what degree genes should be expressed,is embedded into chromatin structure and gene regulatory *** to large consortia such as Encyclopedia of DNA Elements(ENCODE) and Roadmap Epigenomic projects,extensive data on chromatin accessibility and transcript abundance are available across many tissues and cell *** rich data offer an exciting opportunity to model the causal regulatory ***,we will review the current experimental approaches,foundational data,computational problems,interpretive frameworks,and integrative models that will enable the accurate interpretation of regulatory ***,we will discuss the efforts to organize,analyze,model,and integrate the DNA accessibility data,transcriptional data,and functional genomic regions *** believe that these efforts will eventually help us understand the information flow within the cell and will influence research directions across many fields.

关键词： gene regulatory network open chromatin DNA accessibility transcription factor colocalization statistical model data integration

来源：评论

学校读者我要写书评

暂无评论

Uncertainty Quantification and Statistical Inference for Biologically Informed Neural Networks

Uncertainty Quantification and Statistical Inference for Bio...

引用

2024 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2024

作者： Goo, Taewan Lee, Chanhee Shin, Sangyeon Kim, Haeyoung Park, Taesung Seoul National University Interdisciplinary Program in Bioinformatics Seoul Korea Republic of Seoul National University Department of Statistics Seoul Korea Republic of

ISBN: (纸本)9798350386226

In precision medicine, deep learning models play an important role in identifying relevant biomarkers and making predictions for the early diagnosis and prognosis of diseases. While successful in developing accurate prediction models for prognosis and diagnosis, the 'black box' nature of deep learning poses significant challenges, which makes it difficult to have a meaningful biological interpretation. Recently, several biologically informed neural networks (BINNs), such as the Visible Neural Network and P-NET, have been proposed to incorporate biological knowledge for improved interpretation. These models assess node importance using quantitative measures such as SHAP and DeepLIFT. However, these approaches have limitations because they typically provide a single estimate of node importance without any information on the uncertainty of these estimates. This study aims to assess uncertainty of node importance through a novel statistical framework. Bootstrapping is used to calculate confidence interval of node importance. Our findings validate that the proposed statistical framework quantifies the uncertainty of nodes in BINNs. By providing robust confidence intervals of pathways and genes, this framework enhances the interpretability and reliability of BINNs. © 2024 IEEE.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

Confidence Interval Estimation for Machine Learning Models in Forecasting Infectious Diseases

Confidence Interval Estimation for Machine Learning Models i...

引用

2024 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2024

作者： Goo, Taewan Han, Kyulhee Song, Hanbyul Park, Jiwon Liu, Zhe Oh, Jooha Jose, Sayooj Aby Park, Taesung Seoul National University Interdisciplinary Program in Bioinformatics Seoul Korea Republic of Seoul National University Department of Statistics Seoul Korea Republic of

ISBN: (纸本)9798350386226

Forecasting models have been instrumental in managing the COVID-19 pandemic by facilitating efficient resource distribution and proper interventions. Although various forecasting models - such as mathematical models, statistical models, and machine learning models - are available, a few models account for the uncertainty in their predictions. The lack of uncertainty information constrains the reliability of forecasting results, making them less applicable for use in decision-making. Confidence Intervals (CIs) are widely used in statistical inference to provide uncertainty information. In this study, we introduce a framework that provides bootstrap-based CIs readily applicable to various forecasting models. The key concept of this framework is repeated training on bootstrap data based on resampled residuals from the forecasting model. After fitting the forecasting model, bootstrap datasets are generated considering time series characteristics to retrain the forecasting models. Then, CIs are calculated based on the distribution of prediction values. This framework was applied to forecasting COVID-19 outcomes - such as daily confirmed COVID-19 cases, deaths, and ICU patients - in South Korea. Our results demonstrate that bootstrap-based CIs can be successfully applied to provide additional information on the reliability of prediction. In conclusion, our framework can provide uncertainty information without requiring complex assumptions, regardless of the forecasting model or response variable type. Therefore, this approach can support public health policymakers in gaining a deeper understanding of epidemic trends, thereby offering valuable insights for decision-making. © 2024 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Discovering peptides and computational investigations of a multiepitope vaccine target Mycobacterium tuberculosis

引用

Synthetic and Systems Biotechnology 2024年第3期9卷 391-405页

作者： Truc Ly Nguyen Heebal Kim Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Sciences Seoul National UniversitySeoul08826Republic of Korea Interdisciplinary Program in Bioinformatics Seoul National UniversitySeoul08826Republic of Korea eGnome Inc.Seoul05836Republic of Korea

Mycobacterium tuberculosis(MTB)is the causative agent of tuberculosis(TB),a prevalent airborne infectious *** the availability of the Bacille Calmette-Guerin vaccine,its global efficacy remains modest,and tuberculosis persists as a significant global public health *** this challenge and advancing towards the End MTB Strategy,we developed a multiepitope vaccine(MEV)based on immunoinformatics and compu-tational *** screening of MBT protein identified immune-dominant epitopes based on Major Histocompatibility Complex(MHC)allele binding,immunogenicity,antigenicity,allergenicity,toxicity,and cytokine *** epitopes were integrated into an MEV construct with adjuvant and linkers,forming a fully immunogenic vaccine *** analyses encompassed the evaluation of immunological and physicochemical properties,determination of tertiary structure,molecular docking with Toll-Like Receptors(TLR),molecular dynamics(MD)simulations for all atoms,and immune *** MEV comprises 534 amino acids,featuring 6 cytotoxic T lymphocyte,8 helper T lymphocyte,and 7 linear B lymphocyte epitopes,demonstrating high antigenicity and ***,molecular docking studies and triplicate MD simulations revealed enhanced interactions and stability of MEV with the TLR4 complex compared to *** addition,the immune simulation indicated the capacity to effectively induce elevated levels of an-tibodies and cytokines,emphasizing the vaccine’s robust immunogenic *** study presents a promising MEV against TB,exhibiting favorable immunological and physicochemical *** findings provide theoretical support for TB vaccine *** study aligns with the global initiative of the End MTB Strategy,emphasizing its potential impact on addressing persistent challenges in TB control.

关键词： Mycobacterium tuberculosis Tuberculosis Multiepitope vaccine Docking molecular Molecular dynamics simulation Immune simulation

来源：评论

学校读者我要写书评

暂无评论

LOCOS: A cosine based local gene expression pattern finding algorithm on time-series data

LOCOS: A cosine based local gene expression pattern finding ...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Suk, Youjeong Jeon, Jaemin Jung, Inuk Kyungpook National University School of Computer Science and Engineering Daegu Korea Republic of Seoul National University Interdisciplinary Program in Bioinformatics Seoul Korea Republic of

ISBN: (纸本)9798350362480

In gene expression analysis, understanding a biological event that is observed at some time instance often requires capturing genes whose expression levels modulate before and after the event. Such genes are expected to be the responders to the event and will exhibit similar expression patterns during the event. However, after the effect of the event fades, their expression levels may loose their correlation. Hence, it is a non-trivial task to identify genes that share highly similar local expression patterns nearby the event and also allowing some level of divergent expression patterns further away from the event's time point. Here, we propose LOCOS (LOcal COSine), a novel time-course clustering algorithm tailored to cluster genes exhibiting similar expression patterns within a specified time interval. LOCOS is an extension of the traditional Non-negative Matrix Factorization (NMF), which incorporates the cosine similarity and Euclidean distances in its update procedure. LOCOS maximizes the cosine similarity within an interval of interest, while allowing a relaxed minimization of Euclidean distance outside it. Using synthetic and non-biological time-course data, we showed that LOCOS was able to correctly detect the known local patterns within a specified interval. Furthermore, we used longitudinal single-cell RNA-seq samples from four patients showing deteriorating and recovering health conditions to identify genes related to each of the phenotype. As a result, LOCOS was able to capture gene clusters with distinct expression patterns that aligned with the intervals embedding the clinical deterioration and recovery events. © 2024 IEEE.

关键词： Non-negative matrix factorization

来源：评论

学校读者我要写书评

暂无评论

Learn2Evade: Learning-based generative model for evading PDF malware classifiers

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2021年第4期2卷 299-313页

作者： Bae, Ho Lee, Younghan Kim, Yohan Hwang, Uiwon Yoon, Sungroh Paek, Yunheung Department of Cyber Security EwhaWomans University Seoul03760 Korea Republic of Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Interdisciplinary Program in Bioinformatics Interdisciplinary Program in Artificial Intelligence Automation and Systems Research Institute Institute of NewMedia and Communications Institute of Engineering Research Seoul National University Seoul08826 Korea Republic of

Recent research has shown that a small perturbation to an input may forcibly change the prediction of a machine learning (ML) model. Such variants are commonly referred to as adversarial examples. Early studies have focused mostly on ML models for image processing and expanded to other applications, including those for malware classification. In this article, we focus on the problem of finding adversarial examples against ML-based portable document format (PDF) malware *** deem that our problem is more challenging than those againstMLmodels for image processing because of the highly complex data structure of PDF and of an additional constraint that the generated PDF should exhibit malicious behavior. To resolve our problem, we propose a variant of generative adversarial networks that generate evasive variant PDF malware (without any crash), which can be classified as benign by various existing classifiers yetmaintaining the original malicious behavior. Our model exploits the target classifier as the second discriminator to rapidly generate an evasive variant PDF with our new feature selection process that includes unique features extracted from malicious PDF files. We evaluate our technique against three representative PDF malware classifiers (Hidost 13, Hidost 16, and PDFrate-v2) and further examine its effectiveness with AntiVirus engines from VirusTotal. To the best of our knowledge, our work is the first to analyze the performance against the commercial AntiVirus engines. Our model finds, with great speed, evasive variants for all selected seeds against state-of-The-Art PDF malware classifiers and raises a serious security concern in the presence of adversaries. © 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Genome-wide association analysis with matched samples discloses additional novel risk loci

Genome-wide association analysis with matched samples disclo...

引用

IEEE International Conference on bioinformatics and Biomedicine

作者： Jungsoo Gim Sungkyoung Choi Jongho Im Jae-Kwang Kim Taesung Park Interdisciplinary program for Bioinformatics and Department of Statistics Seoul National University Interdisciplinary program for Bioinformatics Seoul National University Department of Statistics Iowa State University

ISBN: (纸本)9781479956708

Genome-wide association studies have identified many candidate causal variants associated with common complex diseases and traits, but most of them have been drawn from nonrandomized case/control designs. In nonrandomized experiments, the results drawn from two different groups can be misleading because the units exposed to one group generally differ systematically from the units exposed to the other group. Propensity score is widely used to group case and control units for a more direct and significant comparison even with nonrandomized experiments. This propensity score matching can help with prioritizing additional uncovered variants on disease risk via sub-group analysis in genome-wide association studies. The aim of this work is to propose a post-hoc association test based on the subsets of samples. For that purpose, this paper presents a new paradigm for a post-hoc genome-wide association test when the sample size of controls are larger than that of cases: selecting control samples by equating the distribution of covariates in the case and control groups and re-performing association analysis upon these matched samples. We demonstrated the feasibility of this approach by applying it to 2752 type II diabetes patients in 8842 Korean population. Genome-wide association approach with matched samples is able to disclose 9 additional novel variants and 7 out of 9 have not identified from the association test of whole control samples. The process described here can successfully be combined with other types of case/control studies with large covariate information. This indicates that there a possibility of obtaining additional candidate causal variants responsible for common diseases through genome-wide association analysis with matched samples.

关键词： Propensity score Matching Genome-wide association study

来源：评论

学校读者我要写书评

暂无评论

Author Correction: AIVariant: a deep learning-based somatic variant detector for highly contaminated tumor samples

引用

Experimental & molecular medicine 2025年第1期57卷 284页

作者： Hyeonseong Jeon Junhak Ahn Byunggook Na Soona Hong Lee Sael Sun Kim Sungroh Yoon Daehyun Baek Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea. Genome4me Inc. Seoul Republic of Korea. School of Biological Sciences Seoul National University Seoul Republic of Korea. Department of Electrical and Computer Engineering Seoul National University Seoul Republic of Korea. AIGENDRUG Co. Ltd. Seoul Republic of Korea. Department of Software and Computer Engineering Ajou University Suwon Republic of Korea. Department of Computer Science and Engineering Seoul National University Seoul Republic of Korea. Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Republic of Korea. Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea. baek@snu.ac.kr. Genome4me Inc. Seoul Republic of Korea. baek@snu.ac.kr. School of Biological Sciences Seoul National University Seoul Republic of Korea. baek@snu.ac.kr. Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Republic of Korea. baek@snu.ac.kr.

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：