检索结果-内蒙古大学图书馆

The Genome Sequence of Taurine Cattle: A Window to Ruminant Biology and Evolution

SCIENCE 2009年第5926期324卷 522-528页

作者： Elsik, Christine G. Tellam, Ross L. Worley, Kim C. Gibbs, Richard A. Abatepaulo, Antonio R. R. Abbey, Colette A. Adelson, David L. Aerts, Jan Ahola, Virpi Alexander, Lee Alioto, Tyler Almeida, Iassudara G. Amadio, Ariel F. Anatriello, Elen Antonarakis, Stylianos E. Anzola, Juan M. Astashyn, Alex Bahadue, Suria M. Baldwin, Cynthia L. Barris, Wes Baxter, Rebecca Bell, Stephanie Nicole Bennett, Anna K. Bennett, Gary L. Biase, Fernando H. Boldt, Clayton R. Bradley, Daniel G. Brinkman, Fiona S. L. Brinkmeyer-Langford, Candice L. Brown, Wendy C. Brownstein, Michael J. Buhay, Christian Caetano, Alexandre R. Camara, Francisco Carroll, Jeffrey A. Carvalho, Wanessa A. Casey, Theresa Cervelatti, Elaine P. Chack, Joseph Chacko, Elsa Chandrabose, Mimi M. Chapin, Jennifer E. Chapple, Charles E. Chen, Hsiu-Chuan Chen, Lin Cheng, Ye Cheng, Ze Childers, Christopher P. Chitko-McKown, Carol G. Chiu, Readman Choi, Jungwoo Chrast, Jacqueline Colley, Adam J. Connelley, Tomothy Cree, Andy Curry, Stacey Dalrymple, Brian Diep Dao, Marvin Davis, Clay de Oliveira, Carlo J. F. de Miranda Santos, Isabel K. F. de Campos, Tatiana A. Deobald, Heather Devinoy, Eve Dickins, C. Michael Yan, Ding Dinh, Huyen H. De Donato, Marcos Donohue, Kaitlin E. Donthu, Ravikiran Dovc, Peter Dugan-Rocha, Shannon Durbin, K. James Eberlein, Annett Edgar, Robert C. Egan, Amy Eggen, Andre Eichler, Evan E. Elhaik, Eran Ellis, Shirley A. Elnitski, Laura Ermolaeva, Olga Eyras, Eduardo Fitzsimmons, Carolyn J. Fowler, Gerald R. Franzin, Alessandra M. Fritz, Krista Gabisi, Ramatu Ayiesha Garcia, Gustavo R. Garcia, Jose Fernando Genini, Sem Gerlach, Daniel German, J. Bruce Gilbert, James G. R. Gill, Clare A. Gladney, Cody J. Glass, Elizabeth J. Goodell, Jason Grant, Jason R. Graur, Dan Greaser, Marion L. Green, Jonathan A. Green, Ronnie D. Guan, Leluo Guigo, Roderic Hadsell, Darryl L. Hagen, Darren E. Hakimov, Hatam A. Halgren, Rob Hamernik, Debora L. Hamilton, Cerissa Harhay, Gregory P. Harrow, Jennifer L. Hart, Elizabeth A. Hastings, Nicola Havlak, Paul Henrichsen, Charl Georgetown Univ Dept Biol 406 Reiss37th & O St NW Washington DC 20057 USA CSIRO Livestock Ind St Lucia Qld 4067 Australia Baylor Coll Med Dept Mol & Human Genet Human Genome Sequencing Ctr Houston TX 77030 USA Washington Univ Sch Med Genome Ctr St Louis MO 63108 USA Texas A&M Univ Dept Anim Sci College Stn TX 77843 USA Univ Adelaide Sch Mol Biomed & Sci Sch Agr Food & Wine Adelaide SA 5005 Australia Univ Washington Dept Genome Sci Seattle WA 98195 USA Howard Hughes Med Inst Seattle WA 98195 USA Natl Human Genome Res Inst Natl Inst Hlth Rockville MD 20878 USA Univ Pompeu Fabra Ctr Genomic Regulation Grp Recerca Informatica Biomed Inst Municipal Invest Med E-08003 Barcelona Spain USDA Cooperative State Res Educ & Extension Serv Washington DC 20250 USA USDA Agr Res Serv Natl Program Staff Beltsville MD 20705 USA Univ Illinois Urban Champaign Dept Anim Sci Urbana IL 61801 USA Univ Illinois Urban Champaign Inst Genomic Biol Urbana IL 61801 USA Simon Fraser Univ Dept Mol Biol Biochem Burnaby BC V5A 1S6 Canada Univ Sydney Fac Vet Sci Sydney NSW 2006 Australia Univ Lausanne Ctr Integrative Genomics CH-1015 Lausanne Switzerland Baylor Coll Med Dept Pediat Nutr USDA Agr Res Serv Childrens Nutr Res Ctr Houston TX 77030 USA Texas A&M Univ Dept Vet Integrative Biosci College Stn TX 77843 USA Univ Geneva Med Sch Dept Genetic Med & Dev CH-1211 Geneva Switzerland Swiss Inst Bioinformatics CH-1211 Geneva Switzerland Imperial Coll S Kensington Campus Div Mol Biosci London SW7 2AZ England Texas A&M Univ Dept Vet Pathobiol College Stn TX 77843 USA Natl Inst Hlth Natl Lib Med Natl Ctr Biotech Information Bethesda MD 20892 USA Wellcome Trust Sanger Inst Hinxton Informatics Dept Cambridge CB10 1HH England Univ London Royal Holloway Egham Dept Comp Sci Surrey TW20 0EX England Univ Houston Dept Biol & Biochem Houston TX 77204 USA Johns Hopkins Univ Sch Med McKusick Nathans Inst Genetic Med Baltimore MD 21205 USA 45 Monterey Dr Tiburon CA 94

To understand the biology and evolution of ruminants, the cattle genome was sequenced to about sevenfold coverage. The cattle genome contains a minimum of 22,000 genes, with a core set of 14,345 orthologs shared among seven mammalian species of which 1217 are absent or undetected in noneutherian (marsupial or monotreme) genomes. Cattle-specific evolutionary breakpoint regions in chromosomes have a higher density of segmental duplications, enrichment of repetitive elements, and species-specific variations in genes associated with lactation and immune responsiveness. Genes involved in metabolism are generally highly conserved, although five metabolic genes are deleted or extensively diverged from their human orthologs. The cattle genome sequence thus provides a resource for understanding mammalian evolution and accelerating livestock genetic improvement for milk and meat production.

关键词： Alternative Splicing Animals Animals, Domestic Biological Evolution Cattle Evolution, Molecular Evolution, Molecular Female Genetic Variation Genome Humans Male MicroRNAs/genetics MicroRNAs/genetics Molecular Sequence Data Proteins/genetics Sequence Analysis, DNA Species Specificity Synteny

来源：评论

学校读者我要写书评

暂无评论

Calculation of splicing potential from the alternative splicing mutation database

引用

BMC Research Notes 2008年第1期1卷 1-6页

作者： Bechtel, Jason M. Rajesh, Preeti Ilikchyan, Irina Deng, Ying Mishra, Pankaj K. Wang, Qi Wu, Xiaochun Afonin, Kirill A. Grose, William E. Wang, Ye Khuder, Sadik Fedorov, Alexei Program in Bioinformatics and Proteomics/Genomics University of Toledo Health Science Campus Toledo OH 43614 United States Dept. of Medical Microbiology and Immunology University of Toledo Health Science Campus Toledo OH 43614 United States Dept. of Basic Pharmaceutical Sciences South Carolina College of Pharmacy University of South Carolina Columbia SC 29208 United States Department of Biological Sciences Bowling Green State University Bowling Green OH 43403 United States College of Engineering University of Toledo Toledo OH 43606 United States Dept. of Medicine University of Toledo Health Science Campus Toledo OH 43614 United States

Background. The Alternative Splicing Mutation Database (ASMD) presents a collection of all known mutations inside human exons which affect splicing enhancers and silencers and cause changes in the alternative splicing pattern of the corresponding genes. Findings. An algorithm was developed to derive a Splicing Potential (SP) table from the ASMD information. This table characterizes the influence of each oligonucleotide on the splicing effectiveness of the exon containing it. If the SP value for an oligonucleotide is positive, it promotes exon retention, while negative SP values mean the sequence favors exon skipping. The merit of the SP approach is the ability to separate splicing signals from a wide range of sequence motifs enriched in exonic sequences that are attributed to protein-coding properties and/or translation efficiency. Due to its direct derivation from observed splice site selection, SP has an advantage over other computational approaches for predicting alternative splicing. Conclusion. We show that a vast majority of known exonic splicing enhancers have highly positive cumulative SP values, while known splicing silencers have core motifs with strongly negative cumulative SP values. Our approach allows for computation of the cumulative SP value of any sequence segment and, thus, gives researchers the ability to measure the possible contribution of any sequence to the pattern of splicing. © 2008 Bechtel et al;licensee BioMed Central Ltd.

关键词： Splice Enhancer Exonic Splice Enhancer Code Potential Constitutive Exon 14th Exon

来源：评论

学校读者我要写书评

暂无评论

The Alternative Splicing Mutation Database: A hub for investigations of alternative splicing using mutational evidence

引用

BMC Research Notes 2008年第1期1卷 1-7页

Background. Some mutations in the internal regions of exons occur within splicing enhancers and silencers, influencing the pattern of alternative splicing in the corresponding genes. To understand how these sequence changes affect splicing, we created a database of these mutations. Findings. The Alternative Splicing Mutation Database (ASMD) serves as a repository for all exonic mutations not associated with splicing junctions that measurably change the pattern of alternative splicing. In this initial published release (version 1.2), only human sequences are present, but the ASMD will grow to include other organisms, (see Availability and requirements section for the ASMD web address). This relational database allows users to investigate connections between mutations and features of the surrounding sequences, including flanking sequences, RNA secondary structures and strengths of splice junctions. Splicing effects of the mutations are quantified by the relative presence of alternative mRNA isoforms with and without a given mutation. This measure is further categorized by the accuracy of the experimental methods employed. The database currently contains 170 mutations in 66 exons, yet these numbers increase regularly. We developed an algorithm to derive a table of oligonucleotide Splicing Potential (SP) values from the ASMD dataset. We present the SP concept and tools in detail in our corresponding article. Conclusion. The current data set demonstrates that mutations affecting splicing are located throughout exons and might be enriched within local RNA secondary structures. Exons from the ASMD have below average splicing junction strength scores, but the difference is small and is judged not to be significant. © 2008 Bechtel et al;licensee BioMed Central Ltd.

关键词： Alternative Splice Splice Effect Splice Junction Alternative Splice Effect mRNA Isoforms

来源：评论

学校读者我要写书评

暂无评论

Correcting base-assignment errors in repeat regions of shotgun assembly

Correcting base-assignment errors in repeat regions of shotg...

引用

作者： Zhi, Degui Keich, Uri Pevzner, Pavel Heber, Steffen Tang, Haixu Bioinformatics Program University of California San Diego San Diego CA 92093 Department of Computer Science 4130 Upson Hall Cornell University Ithaca NY 14853 Department of Computer Science and Engineering University of California San Diego San Diego CA 92093 North Carolina State University Raleigh NC 27695-7566 School of Informatics Center for Genomics and Bioinformatics Indiana University Bloomington IN 47408

Accurate base-assignment in repeat regions of a whole genome shotgun assembly is an unsolved problem. Since reads in repeat regions cannot be easily attributed to a unique location in the genome, current assemblers may place these reads arbitrarily. As a result, the base-assignment error rate in repeats is likely to be much higher than that in the rest of the genome. We developed an iterative algorithm, EULER-AIR, that is able to correct base-assignment errors in finished genome sequences in public databases. The Wolbachia genome is among the best finished genomes. Using this genome project as an example, we demonstrated that EULER-AIR can 1) discover and correct base-assignment errors, 2) provide accurate read assignments, 3) utilize finishing reads for accurate base-assignment and 4) provide guidance for designing finishing experiments. In the genome of Wolbachia, EULER-AIR found 16 positions with ambiguous base-assignment and two positions with erroneous bases. Besides Wolbachia, many other genome sequencing projects have significantly fewer finishing reads and, hence, are likely to contain more base-assignment errors in repeats. We demonstrate that EULER-AIR is a software tool that can be used to find and correct base-assignment errors in a genome assembly project. © 2007 IEEE.

关键词： Error analysis

来源：评论

学校读者我要写书评

暂无评论

The unique probe selector: A comprehensive web service for probe design and oligonucleotide arrays

The unique probe selector: A comprehensive web service for p...

引用

6th International Conference on bioinformatics, InCoB 2007

作者： Chen, Shu-Hwa Lo, Chen-Zen Tsai, Ming-Chi Hsiung, Chao A. Lin, Chung-Yen Stem Cell/Regenerative Medicine Program Genomics Research Center Academia Sinica. No. 128 Academia Rd. Taipei 115 Taiwan Institute of Information Science Academia Sinica No. 128 Academia Rd. Taipei 115 Taiwan Division of Biostatistics and Bioinformatics National Health Research Institutes No. 35 Keyan Rd. Zhunan Miaoli County 350 Taiwan Institute of Ishery Science College of Life Science National Taiwan University Roosevelt Rd. Taipei Taiwan

Background: Nucleic acid hybridization, a fundamental technique in molecular biology, can be modified into very effective and sensitive methods for detecting particular targets mixed with millions of non-target sequences. Therefore, avoiding cross-hybridization is the most crucial issue for developing diagnostic methods based on hybridization. Results: To develop a probe with a high discriminating power, this study constructed a web service, the Unique Probe Selector (UPS), for customized probe design. The UPS service integrates a probe design mechanism and a scoring system for evaluating the performance of probe annealing and the uniqueness of a probe in a user-defined genetic background. Starting from an intuitive web interface, the UPS accepts a query with single or multiple sequences in fasta format. The best probe(s) for each sequence can be downloaded from result pages in a fasta or .csv format with a summary of probe characteristics. The option "Unique probe within group" selects the most unique probe for each target sequence with low probability to hybridize to the other sequences in the same submitted query. The option "Unique probe in the specific organism" devises probes for each submitted sequence to identify its target among selected genetic backgrounds based on Unigene. Conclusion: The UPS evaluates probe-to-target hybridization under a user-defined condition in silico to ensure high-performance hybridization and minimizes the possibility of non-specific reactions. UPS has been applied to design human arrays for gene expression studies and to develop several small arrays of gene families that were inferred as molecular signatures of cancer typing/staging or pathogen signatures. Notably, UPS is freely accessible at http://***/ups/. © 2006 Brahmachary et al;licensee BioMed Central Ltd.

关键词： Probes

来源：评论

学校读者我要写书评

暂无评论

The inflammatory and normal transcriptome of mouse bladder detrusor and mucosa

引用

BMC Physiology 2006年第1期6卷 1-20页

作者： Saban, Marcia R. Hellmich, Helen L. Turner, Mary Nguyen, Ngoc-Bich Vadigepalli, Rajanikanth Dyer, David W. Hurst, Robert E. Centola, Michael Saban, Ricardo Department of Physiology University Oklahoma Health Sciences Center Oklahoma City OK United States Department of Anesthesiology University of Texas Medical Branch Galveston TX United States Oklahoma Medical Research Foundation (OMRF) Arthritis and Immunology Research Program Microarray Core Facility Oklahoma City OK United States Daniel Baugh Institute for Functional Genomics and Computational Biology Department of Pathology Anatomy and Cell Biology Thomas Jefferson University Philadelphia United States Department of Microbiology and Immunology Laboratory for Genomics and Bioinformatics Oklahoma University Health Sciences Center Oklahoma City OK United States Department of Urology University Oklahoma Health Sciences Center Oklahoma City OK United States Cellular and Structural Biology University of Texas Health Science Center at San Antonio San Antonio TX United States

Background: An organ such as the bladder consists of complex, interacting set of tissues and cells. Inflammation has been implicated in every major disease of the bladder, including cancer, interstitial cystitis, and infection. However, scanty is the information about individual detrusor and urothelium transcriptomes in response to inflammation. Here, we used suppression subtractive hybridizations (SSH) to determine bladder tissue- and disease-specific genes and transcriptional regulatory elements (TRE)s. Unique TREs and genes were assembled into putative networks. Results: It was found that the control bladder mucosa presented regulatory elements driving genes such as myosin light chain phosphatase and calponin I that influence the smooth muscle phenotype. In the control detrusor network the Pax-3 TRE was significantly over-represented. During development, the Pax-3 transcription factor (TF) maintains progenitor cells in an undifferentiated state whereas, during inflammation, Pax-3 was suppressed and genes involved in neuronal development (synapsin I) were up-regulated. Therefore, during inflammation, an increased maturation of neural progenitor cells in the muscle may underlie detrusor instability. NF-κB was specifically over-represented in the inflamed mucosa regulatory network. When the inflamed detrusor was compared to control, two major pathways were found, one encoding synapsin I, a neuron-specific phosphoprotein, and the other an important apoptotic protein, siva. In response to LPS-induced inflammation, the liver X receptor was over-represented in both mucosa and detrusor regulatory networks confirming a role for this nuclear receptor in LPS-induced gene expression. Conclusion: A new approach for understanding bladder muscle-urothelium interaction was developed by assembling SSH, real time PCR, and TRE analysis results into regulatory networks. Interestingly, some of the TREs and their downstream transcripts originally involved in organogenesis and oncogene

关键词：

来源：评论

学校读者我要写书评

暂无评论

MyLibrary at LANL: proximity and semi-metric networks for a collaborative and recommender Web service

MyLibrary at LANL: proximity and semi-metric networks for a ...

引用

IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： L.M. Rocha T. Simas A. Rechtsteiner M. Di Giacomo R. Luce School of Informatics and Cognitive Science Program Indiana University Bloomington IN USA Center for Genomics and Bioinformatics Indiana University Bloomington IN USA Research Library Los Alamos National Laboratory Los Alamos NM USA

We describe a network approach to building recommendation systems for a Web service. We employ two different types of weighted graphs in our analysis and development: proximity graphs, a type of fuzzy graphs based on a co-occurrence probability, and semi-metric distance graphs, which do not observe the triangle inequality of Euclidean distances. Both types of graphs are used to develop intelligent recommendation and collaboration systems for the MyLibrary@LANL Web service, a user-centered front-end to the Los Alamos National Laboratory's digital library collections and Web resources.

关键词： Collaboration Web services Software libraries Databases Intelligent networks bioinformatics Laboratories Information resources Physics Computer science

来源：评论

学校读者我要写书评

暂无评论

Remarkable sequence signatures in archaeal genomes

引用

Archaea 2003年第3期1卷 185-190页

作者： Fadiel, Ahmed Lithwick, Stuart Ganji, Gopi Scherer, Stephen W. Center for Applied Genomics Hospital for Sick Children Toronto Ont. M5G 1Z8 Canada Bioinformatics Supercomputing Centre Genom. and Genetics Biology Program Hospital for Sick Children Toronto Ont. M5G 1Z8 Canada

Complete archaeal genomes were probed for the presence of long (≥ 25 bp) oligonucleotide repeats (words). We detected the presence of many words distributed in tandem with narrow ranges of periodicity (i.e., spacer length between repeats). Similar words were not identified in genomes of non-archaeal species, namely Escherichia coli, Bacillus subtilis, Haemophilus influenzae, Mycoplasma genitalium and Mycoplasma pneumoniae. BLAST similarity searches against the GenBank nucleotide sequence database revealed that these words were archaeal species-specific, indicating that they are of a signature character. Sequence analysis and genome viewing tools showed these repeats to be restricted to non-coding regions. Thus, archaea appear to possess a non-coding genomic signature that is absent in bacterial species. The identification of a species-specific genomic signature would be of great value to archaeal genome mapping, evolutionary studies and analyses of genome complexity.

关键词： Archaea bioinformatics Comparative genomics Genome signature Oligonucleotide frequencies

来源：评论

学校读者我要写书评

暂无评论

Contributory presentations/posters

引用

Journal of Biosciences 1999年第1期24卷 33-198页

作者： N. Manoj V. R. Srinivas A. Surolia M. Vijayan K. Suguna R. Ravishankar R. Schwarzenbacher K. Zeth Diederichs G. M. Kostner A. Gries P. Laggner R. Prassl Madhusudan Pearl Akamine Nguyen-huu Xuong Susan S. Taylor M. Bidva Sagar K. Saikrishnan S. Roy K. Purnapatre P. Handa U. Varshney B. K. Biswal N. Sukumar J. K. Mohana Rao A. Johnson Vasantha Pattabhi S. Sri Krishna Mira Sastri H. S. Savithri M. R. N. Murthy Bindu Pillai Kannan M. V. Hosur Mukesh Kumar Swati Patwardhan K. K. Kannan B. Padmanabhaa S. Sasaki-Sugio M. Nukaga T. Matsuzaki S. Karthikevan S. Sharma A. K. Sharma M. Paramasivam P. Kumar J. A. Khan S. Yadav A. Srinivasan T. P. Singh S. Gourinath Neelima Alam A. Srintvasan Vikas Chandra Punit Kaur Ch. Betzel S. Ghosh A. K. Bera S. Bhattacharya S. Chakraborty A. K. Pal B. P. Mukhopadhyay I. Dey U. Haldar Asok Baneriee Jozef Sevcik Adriana Solovicova K. Sekar M. Sundaralingam N. Genov Dong-cai Liang Tao Jiang Ji-ping Zhang Wen-rui Chang Wolfgang Jahnke Marcel Blommers S. C. Panchal R. V. Hosur Bindu Pillay Puniti Mathur S. Srivatsun Ratan Mani Joshi N. R. Jaganathan V. S. Chauhan H. S. Atreya S. C. Sahu K. V. R. Chary Girjesh Govil Elisabeth Adjadj Éric Quinjou Nadia Izadi-Pruneyre Yves Blouquit Joël Mispelter Bernadette Heyd Guilhem Lerat Philippe Milnard Michel Desmadreil Y. Lin B. D. Nageswara Rao Vidva Raghunathan Mei H. Chau Prashant Pesais Sudha Srivastava Evans Coutinho Anil Saran Leizl F. Sapico Jayson Gesme Herbert Lijima Raymond Paxton Thamarapu Srikrishnan C. R. Grace G. Nagenagowda A. M. Lynn Sudha M. Cowsik Sarata C. Sahu S. Chauhan A. Bhattacharya G. Govil Anil Kumar Maurizio Pellecchia Erik R. P. Zuiderweg Keiichi Kawano Tomoyasu Aizawa Naoki Fujitani Yoichi Hayakawa Atsushi Ohnishi Tadayasu Ohkubo Yasuhiro Kumaki Kunio Hikichi Katsutoshi Nitta V. Rani Parvathy R. M. Kini Takumi Koshiba Yoshihiro Kobashigawa Min Yao Makoto Demura Astushi Nakagawa Isao Tanaka Kunihiro Kuwajima Jens Linge Seán O. Donoghue Michael Nilges G. Chakshusmathi Girish S. Ratnaparkhi P. K. Madhu R. Varadarajan C. Tetreau Molecular Biophysics Unit Indian Institute of Science Bangalore India Austrian Academy of Sciences Institute of Biophysics and X-ray Structure Research Graz Austria Faculty of Biology University of Konstanz Germany Institute of Medical Biochemistry University of Graz Austria Howard Huges Medical Institute Department of Chemistry & Biochemistry University of California La Jolla USA Department of Chemistry & Biochemistry University of California San Diego La Jolla USA Molecular Biophysics Unit India Department of Microbiology &Cell Biology Indian Institute of Science Bangalore India Macromolecular Structure Laboratory NCI - Frederick Cancer Research and Development Center ABL - Basic Research Program Frederick USA Department of Crystallography and Biophysics University of Madras Chennai Biochemistry Department Indian Institute of Science Bangalore India Condensed Matter Physics Division B.A.R.C. Trombay Mumbai Condensed Matter Physics Division Bhabha Atomic Research Centre Trombay Mumbai IMCB The University of Tokyo Tokyo Japan Mitsubishi Chemical Corporation Yokohama Research Center Yokohama Japan Faculty of Pharmaceutical Sciences Chiba University Chiba Japan Department of Biophysics All India Institute of Medical Sciences New Delhi India Department of Biophysics All India Institute of Medical sciences New Delhi India Institüt Physiologische Chemie Hamburg Germany Biophysics Department Bose Institute Calcutta India Slovak Academy of Sciences Institute of Molecular Biology Bratislava Slovakia Indian Institute of Science Bioinformatics Centre Bangalore Departments of Chemistry and Biochemistry Biological Macromolecular Structure Center USA Bulgarian Academy of Sciences Institute of Organic Chemistry Sofia Bulgaria Chinese Academy of Sciences Institute of Biophysics Beijing China Novartis Pharma AG Preclinical Research Basel Switzerland Department of Chemical Sciences Tata Institute of Fundamental Research Mumbai India Solid State Physics Div

来源：评论

学校读者我要写书评

暂无评论

Graph Artificial Intelligence in Medicine

引用

Biomedical Data Science 1000年第1期7卷 345-368页

作者： Ruth Johnson Michelle M. Li Ayush Noori Owen Queen Marinka Zitnik 1Department of Biomedical Informatics Harvard Medical School Boston Massachusetts USA email: marinka@hms.harvard.edu 2Berkowitz Family Living Laboratory Harvard Medical School Boston Massachusetts USA 3Bioinformatics and Integrative Genomics Program Harvard Medical School Boston Massachusetts USA 4Department of Computer Science Harvard John A. Paulson School of Engineering and Applied Sciences Allston Massachusetts USA 5Broad Institute of MIT and Harvard Cambridge Massachusetts USA 7Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Allston Massachusetts USA 6Harvard Data Science Initiative Cambridge Massachusetts USA

In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data—from patient records to imaging—graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pretraining, facilitate interactive feedback loops, and foster human–AI collaboration, paving the way toward clinically meaningful predictions.

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：