检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Andersen, Jakob Lykke Flamm, Christoph Merkle, Daniel Stadler, Peter F. Department of Mathematics and Computer Science University of Southern Denmark Campusvej 55 Odense MDK-5230 Denmark Institute for Theoretical Chemistry University of Vienna Währingerstraße 17 WienA-1090 Austria Bioinformatics Group Department of Computer Science University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Interdisciplinary Center for Bioinformatics University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Halle-Jena-Leipzig University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Competence Center for Scalable Data Services and Solutions Dresden-Leipzig University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Leipzig Research Center for Civilization Diseases University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Centre for Biotechnology and Biomedicine University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Facultad de Ciencias Universidad Nacional de Colombia Sede Bogotá Colombia Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States

Autocatalysis is a deceptively simple concept, referring to the situation that a chemical species X catalyzes its own formation. From the perspective of chemical kinetics, autocatalysts show a regime of superlinear growth. Given a chemical reaction network, however, it is not at all straightforward to identify species that are autocatalytic in the sense that there is a sub-network that takes X as input and produces more than one copy of X as output. The difficulty arises from the need to distinguish autocatalysis e.g. from the superposition of a cycle that consumes and produces equal amounts of X and a pathway that produces X. To deal with this issue, a number of competing notions, such as exclusive autocatalysis and autocatalytic cycles, have been introduced. A closer inspection of concepts and their usage by different authors shows, however, that subtle differences in the definitions often makes conceptually matching ideas difficult to bring together formally. In this contribution we make some of the available approaches comparable by translating them into a common formal framework that uses integer hyperflows as a basis to study autocatalysis in large chemical reaction networks. As an application we investigate the prevalence of autocatalysis in metabolic networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Growth kinetics

来源：评论

学校读者我要写书评

暂无评论

COVID-19 Testing Pipeline: Lesson Learned

引用

IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Dian Amirulloh Digdo Sudigyo Arif Budiarto Ika Nurlaila Erlin Listiyaningsih Andrew Simon Bens Pardamean Genetics Indonesia Jakarta Indonesia 12940 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Information System Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

The transmission of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) in Indonesia is seen to be uncontrollably increasing that urges the government to leverage the capacity for the disease detections. Real-time polymerase chain reaction (RT-PCR), rapid test and computed tomography (CT) scan are the most common methods to determine if one has been infected regardless of whether or not the common symptoms of such Corona Virus Disease 2019 (COVID-19) surface. Among these three, RT-PCR is considered the gold standard for qualitative and quantitative assessment of SARS CoV-2 detection. The present paper aims at elaborating the framework of Roche's RT-PCR machine employed specifically for SARS CoV-2 detection performed by Genetics Indonesia which is deemed to be efficient and relatively quicker than other detection kits. RT-PCR machine detected SARS Cov-2 with RNA amplification curve equals to 10 copies RNA below the cut off value of Crossing point (Cp) positive control. Also elucidated in the paper is the implementations of EAV RNA and LightCycler® 96 RT-PCR System through which analysis time, amounts of individual required sample, as well as the reagents, can be accordingly reduced.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Evaluation of Deep Neural Network Performance on Limited Protein Phosphorylation Site Prediction data

引用

Procedia Computer science 2019年 157卷 25-30页

作者： Favorisen Rosyking Lumbanraja Bharuno Mahesworo Tjeng Wawan Cenggoro Arif Budiarto Bens Pardamean Department of Computer Science Faculty of Mathematics and Natural Science University of Lampung Jalan Prof. Dr. Sumantri Brojonegoro No.17 35145 Bandar Lampung Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

One of the common and important post-translational modification (PTM) types is phosphorylation. Protein phosphorylation is used to regulate various enzyme and receptor activations which include signal pathways. There have been many significant studies conducted to predict phosphorylation sites using various machine learning methods. Recently, several researchers claimed deep learning based methods as the best methods for phosphorylation sited prediction. However, the performance of these methods were backed up with the massive training data used in the researches. In this paper, we study the performance of simple deep neural network on the limited data generally used prior to deep learning employment. The result shows that a deep neural network can still achieve comparable performance in the limited data settings.

关键词： Phosphorylation Site Prediction Protein Phosphorylation Deep Learning Deep Neural Network

来源：评论

学校读者我要写书评

暂无评论

Author Correction: Characterization of the genetic architecture of infant and early childhood body mass index

引用

Nature metabolism 2024年第2期6卷 378页

作者： Øyvind Helgeland Marc Vaudel Pol Sole-Navais Christopher Flatley Julius Juodakis Jonas Bacelis Ingvild L Koløen Gun Peggy Knudsen Bente B Johansson Per Magnus Ted Reichborn Kjennerud Petur B Juliusson Camilla Stoltenberg Oddgeir L Holmen Ole A Andreassen Bo Jacobsson Pål R Njølstad Stefan Johansson Center for Diabetes Research Department of Clinical Science University of Bergen Bergen Norway. Department of Genetics and Bioinformatics Health Data and Digitalization Norwegian Institute of Public Health Oslo Norway. Department of Obstetrics and Gynecology Institute of Clinical Sciences Sahlgrenska Academy University of Gothenburg Gothenburg Sweden. Department of Medical Genetics Haukeland University Hospital Bergen Norway. Norwegian Institute of Public Health Oslo Norway. Centre for Fertility and Health Norwegian Institute of Public Health Oslo Norway. Department of Mental Disorders Norwegian Institute of Public Health Oslo Norway. Institute of Clinical Medicine University of Oslo Oslo Norway. Department of Health Registry Research and Development National Institute of Public Health Bergen Norway. Department of Clinical Science University of Bergen Bergen Norway. Children and Youth Clinic Haukeland University Hospital Bergen Norway. HUNT Research Centre Department of Public Health and Nursing Faculty of Medicine and Health Sciences Norwegian University of Science and Technology Trondheim Norway. NORMENT Centre Institute of Clinical Medicine University of Oslo Oslo Norway. Division of Mental Health and Addiction Oslo University Hospital Oslo Norway. Center for Diabetes Research Department of Clinical Science University of Bergen Bergen Norway. pal.njolstad@uib.no. Children and Youth Clinic Haukeland University Hospital Bergen Norway. pal.njolstad@uib.no. Center for Diabetes Research Department of Clinical Science University of Bergen Bergen Norway. stefan.johansson@uib.no. Department of Medical Genetics Haukeland University Hospital Bergen Norway. stefan.johansson@uib.no.

来源：评论

学校读者我要写书评

暂无评论

A phase I/II trial of WT1-specific TCR gene therapy for patients with acute myeloid leukemia and active disease post-allogeneic hematopoietic cell transplantation: skewing towards NK-like phenotype impairs T cell function and persistence

引用

Nature Communications 2025年第1期16卷 1-20页

作者： Francesco Mazziotta Lauren E. Martin Sinéad Kinsella Miranda C. Lahman Daniel Hunter Thomas M. Schmitt Natalie Duerkopp Tzu-Hao Tang Yuta Asano Elise C. Wilcox Bo Lee Tianzi Zhang Philip D. Greenberg Aude G. Chapuis Daniel N. Egan Merav Bar Kelly G. Paulson Cecilia C. S. Yeung Filippo Milano Valentin Voillet Raphael Gottardo Paolo Lopedote Livius Penter Catherine J. Wu Program in Immunology Fred Hutchinson Cancer Center Seattle WA USA Translational Sciences and Therapeutics Division Fred Hutchinson Cancer Center Seattle WA USA Immunotherapy Integrated Research Center Fred Hutch Cancer Center Seattle WA USA Departments of Immunology and Medicine University of Washington Seattle WA USA Division of Medical Oncology University of Washington Seattle WA USA Providence-Swedish Cancer Institute Seattle WA USA Bristol Myers Squibb Boudry Switzerland University of Washington Dept. of Laboratory Medicine and Pathology Seattle WA USA Vaccine and Infectious Disease Division Fred Hutchinson Cancer Center Seattle WA USA Cape Town HVTN Immunology Laboratory Hutchinson Centre Research Institute of South Africa Cape Town South Africa Biomedical Data Science Center Lausanne University Hospital Lausanne Switzerland University of Lausanne Lausanne Switzerland Agora Translational Research Center Lausanne Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland Department of Medicine St. Elizabeth’s Medical Center Boston University Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Hematology Oncology and Tumorimmunology Campus Virchow Klinikum Charité - Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin Berlin Germany Berlin Institute of Health at Charité-Universitätsmedizin Berlin BIH Biomedical Innovation Academy BIH Charité Digital Clinician Scientist Program Berlin Germany

Relapsed and/or refractory acute myeloid leukemia (AML) post-allogeneic hematopoietic cell transplantation (HCT) is usually fatal. We previously reported that post-HCT immunotherapy with Epstein-Barr virus (EBV)-specific donor CD8+T cells engineered to express a Wilms Tumor Antigen 1-specific T-cell receptor (TTCR-C4) appeared to prevent relapse in high-risk patients. In this phase I/II clinical trial (NCT01640301), we evaluated safety (primary endpoint), persistence and efficacy (secondary endpoints) of EBV- or Cytomegalovirus (CMV)-specific TTCR-C4in fifteen patients with active AML post-HCT. Infusions were well tolerated, with no dose-limiting toxicities or serious adverse events related to the product. However, TTCR-C4cells did not clearly improve outcomes despite EBV-specific TTCR-C4cells showing enhanced potential for prolonged persistence compared to CMV-specific TTCR-C4. Investigating the fate of persisting TTCR-C4, we identified a shift towards natural killer-like (NKL) terminal differentiation, distinct from solid tumor-associated canonical exhaustion programs. In one patient, treatment with azacitidine appeared to mitigate this NKL skewing, promoting TTCR-C4persistence. These findings suggest that AML drives a distinct form of T-cell dysfunction, highlight the need for targeted approaches that preserve T-cell fitness, ultimately improving the efficacy of cellular therapies for AML.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Cross-platform transcriptomic profiling of the response to recombinant human erythropoietin

Research Square

引用

research Square 2021年

作者： Wang, Guan Kitaoka, Traci Crawford, Ali Mao, Qian Hesketh, Andrew Guppy, Fergus M. Ash, Garrett I. Liu, Jason Gerstein, Mark B. Pitsiladis, Yannis P. Sport and Exercise Science and Sports Medicine Research and Enterprise Group University of Brighton Brighton United Kingdom Centre for Regenerative Medicine and Devices University of Brighton Brighton United Kingdom Illumina San DiegoCA United States BGI Shenzhen China School of Pharmacy and Biomolecular Sciences University of Brighton Brighton United Kingdom Centre for Stress and Age-related Disease University of Brighton Brighton United Kingdom Veterans Affairs Connecticut Healthcare System West HavenCT United States Center for Medical Informatics Yale University New HavenCT United States Program in Computational Biology & Bioinformatics Yale University New HavenCT United States Department of Molecular Biophysics & Biochemistry Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States Department of Statistics and Data Science Yale University New HavenCT United States

RNA-seq has matured and become an important tool for studying RNA biology. Here we compared two RNA-seq (Illumina sequencing by synthesis and MGI DNBSEQTM) and two microarray platforms (Illumina Expression BeadChip and GeneChipTM Human Transcriptome Array 2.0) in healthy individuals administered recombinant human erythropoietin for transcriptome-wide quantification of differential gene expression. The results show that total RNA sequencing combined with DNB-seq produced a multitude of genes of biological relevance and significance in response to recombinant human erythropoietin, in contrast to other platforms. Through data triangulation linking genes to functions, genes representing the processes of erythropoiesis as well as non-erythropoietic functions of erythropoietin were unveiled. This study provides a knowledge base of genes characterising the responses to recombinant human erythropoietin through cross-platform comparison and validation. © 2021, CC BY.

关键词： RNA

来源：评论

学校读者我要写书评

暂无评论

The 2022 Report on the Human Proteome from the HUPO Human Proteome Project

引用

Journal of proteome research 2023年第4期22卷 1024-1042页

作者： Gilbert S Omenn Lydie Lane Christopher M Overall Charles Pineau Nicolle H Packer Ileana M Cristea Cecilia Lindskog Susan T Weintraub Sandra Orchard Michael H A Roehrl Edouard Nice Siqi Liu Nuno Bandeira Yu-Ju Chen Tiannan Guo Ruedi Aebersold Robert L Moritz Eric W Deutsch University of Michigan Ann Arbor Michigan48109 United States. Institute for Systems Biology Seattle Washington98109 United States. CALIPHO Group SIB Swiss Institute of Bioinformatics and University of Geneva 1015Lausanne Switzerland. University of British Columbia Vancouver British ColumbiaV6T 1Z4 Canada. French Institute of Health and Medical Research 35042RENNESCedexFrance. Macquarie University Sydney New South Wales2109 Australia. Griffith University's Institute for Glycomics Sydney New South Wales2109 Australia. Princeton University Princeton New Jersey08544 United States. Uppsala University 752 36Uppsala Sweden. University of Texas Health Science Center-San Antonio San Antonio Texas78229-3900 United States. EMBL-EBI Hinxton CambridgeshireCB10 1SD United Kingdom. Memorial Sloan Kettering Cancer Center New York New York10065 United States. Monash University ClaytonVictoria3800 Australia. BGI Group Shenzhen518083 P. R. China. University of California San Diego La Jolla California92093 United States. National Taiwan University Academia Sinica Nankang Taipei11529 Taiwan. Westlake University Guomics Laboratory of Big Proteomic Data Hangzhou310024 ZhejiangProvinceP. R. China. Institute of Molecular Systems Biology in ETH Zurich 8092Zurich Switzerland.

The 2022 Metrics of the Human Proteome from the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 407 (93.2%) of the 19 750 predicted proteins coded in the human genome, a net gain of 50 since 2021 from data sets generated around the world and reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 78 from 1421 to 1343. This represents continuing experimental progress on the human proteome parts list across all the chromosomes, as well as significant reclassifications. Meanwhile, applying proteomics in a vast array of biological and clinical studies continues to yield significant findings and growing integration with other omics platforms. We present highlights from the Chromosome-Centric HPP, Biology and Disease-driven HPP, and HPP Resource Pillars, compare features of mass spectrometry and Olink and Somalogic platforms, note the emergence of translation products from ribosome profiling of small open reading frames, and discuss the launch of the initial HPP Grand Challenge Project, "A Function for Each Protein".

关键词： Biology and Disease-HPP (B/D-HPP) Grand Challenge Project Human Protein Atlas Human Proteome Project (HPP) Mass Spectrometry Interactive Virtual Environment (MassIVE) PeptideAtlas Ribo-Seq chromosome-centric HPP (C-HPP) missing proteins (MP) neXtProt protein existence (PE metrics) non-MS PE1 proteins small open reading frames (smORFs) uncharacterized protein existence 1 (uPE1)

来源：评论

学校读者我要写书评

暂无评论

Author Correction: A systematic genome-wide mapping of oncogenic mutation selection during CRISPR-Cas9 genome editing

引用

Nature communications 2022年第1期13卷 2828页

作者： Sanju Sinha Karina Barbosa Kuoyuan Cheng Mark D M Leiserson Prashant Jain Anagha Deshpande David M Wilson Bríd M Ryan Ji Luo Ze'ev A Ronai Joo Sang Lee Aniruddha J Deshpande Eytan Ruppin Cancer Data Science Laboratory Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD 20892 USA. Laboratory of Human Carcinogenesis Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD 20850 USA. Center for Bioinformatics and Computational Biology University of Maryland College Park MD 20742 USA. Tumor Initiation Program Cancer Center Sanford Burnham Prebys Medical Discovery Institute La Jolla CA 92037 USA. Laboratory of Molecular Gerontology National Institute on Aging Intramural Research Program National Institutes of Health Baltimore MD 21224 USA. Laboratory of Cancer Biology and Genetics Center for Cancer Research National Cancer Institute National Institute of Health Bethesda MD 20850 USA. Samsung Medical Center Sungkyunkwan University School of Medicine Suwon 16419 Republic of Korea. Department of Precision Medicine School of Medicine and Department of Artificial Intelligence Sungkyunkwan University Suwon 16419 Republic of Korea. Tumor Initiation Program Cancer Center Sanford Burnham Prebys Medical Discovery Institute La Jolla CA 92037 USA. adeshpande@***. Cancer Data Science Laboratory Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda MD 20892 USA. eytan.ruppin@nih.gov.

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for Imbalance data Classification using Class Expert Generative Adversarial Network

引用

Procedia Computer science 2018年 135卷 60-67页

作者： Fanny Tjeng Wawan Cenggoro Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

Without any specific way for imbalance data classification, artificial intelligence algorithm cannot recognize data from minority classes easily. In general, modifying the existing algorithm by assuming that the training data is imbalanced, is the only way to handle imbalance data. However, for a normal data handling, this way mostly produces a deficient result. In this research, we propose a class expert generative adversarial network (CE-GAN) as the solution for imbalance data classification. CE-GAN is a modification in deep learning algorithm architecture that does not have an assumption that the training data is imbalance data. Moreover, CE-GAN is designed to identify more detail about the character of each class before classification step. CE-GAN has been proved in this research to give a good performance for imbalance data classification.

关键词： imbalance data classification deep learning generative adversarial network

来源：评论

学校读者我要写书评

暂无评论

Analyzing important factors in e-commerce for sustainable industry

引用

IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Yulius Lie Kartika Purwandari Bharuno Mahesworo Reza Rahutomo Anzaludin Samsinga Perbangsa Hery Harjono Muljo Bens Pardamean Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Accounting Information Systems Program Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The rapid growth of e-commerce for sustainable industry in Indonesia leads to competition for customer acquisition. Instead of gaining loyalty, customers are trapped in a price war and take advantage to compare and choose the one with the best offer. This study aims to analyze important factors of an online retailer to determine customer loyalty in the productive age group. This study contributes to design a questionnaire that can be utilized in analyzing customer loyalty to a retailer in e-commerce (popularly known as the term e-tailer). The questionnaire can be distributed through the Internet to gather demography, population, and e-loyalty factors. Questionnaire design is proposed containing three parts: Part A – Demography, Part B – Targeted Population, and Part C - e-Loyalty Factors. While Part A records basic information and Part B validates, Part C records respondent's assessment to an e - tailer. Therefore, the outcome delivers a data engineering method to help e-tailers determine important factors to gain customer loyalty.

关键词：

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：