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Fairness literacy: The achilles’ heel of applying fair principles

data science Journal 2020年第1期19卷 1-11页

作者： David, Romain Mabile, Laurence Specht, Alison Stryeck, Sarah Thomsen, Mogens Yahia, Mohamed Jonquet, Clement Dollé, Laurent Jacob, Daniel Bailo, Daniele Bravo, Elena Gachet, Sophie Gunderman, Hannah Hollebecq, Jean-Eudes Ioannidis, Vassilios Bras, Yvan Le Lerigoleur, Emilie Cambon-Thomsen, Anne MISTEA INRAE Montpellier SupAgro University of Montpellier Montpellier France AISBL Paris France INSERM-Université Paul Sabatier Toulouse III Toulouse France SEES-TERN the University of Queensland St Lucia Australia Graz University of Technology Institute for Interactive Systems and Data Science Graz Austria INIST-CNRS Nancy France LIRMM University of Montpellier CNRS Montpellier France Erasme Hospital Brussels Belgium INRAE UMR BFP Université de Bordeaux Villenave d’Ornon France EPOS-ERIC/Istituto Nazionale di Geofisica e Vulcanologia Rome Italy Istituto Superiore Sanità Rome Italy Aix Marseille Université CNRS Avignon Université IRD IMBE Marseille France Carnegie Mellon University Pittsburgh Pittsburgh United States MISTEA INRAE Montpellier SupAgro Université de Montpellier Montpellier France SIB Swiss Institute of Bioinformatics Lausanne Switzerland DGD-REVE UMS PatriNat MNHN Paris France CNRS UMR GEODE Université Toulouse 2 Toulouse France

The SHARC Interest Group of the Research data Alliance was established to improve research crediting and rewarding mechanisms for scientists who wish to organise their data (and material resources) for community sharing. This requires that data are findable and accessible on the Web, and comply with shared standards making them interoperable and reusable in alignment with the FAIR principles. It takes considerable time, energy, expertise and motivation. It is imperative to facilitate the processes to encourage scientists to share their data. To that aim, supporting FAIR principles compliance processes and increasing the human understanding of FAIRness criteria – i.e., promoting FAIRness literacy – and not only the machine-readability of the criteria, are critical steps in the data sharing process. Appropriate human-understandable criteria must be the first identified in the FAIRness assessment processes and roadmap. This paper reports on the lessons learned from the RDA SHARC Interest Group on identifying the processes required to prepare FAIR implementation in various communities not specifically data skilled, and on the procedures and training that must be deployed and adapted to each practice and level of understanding. These are essential milestones in developing adapted support and credit back mechanisms not yet in place. © 2020 The Author(s).

关键词： FAIR assessment, Research data sharing FAIR principles FAIRification FAIRness literacy Pre-FAIRification

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scTenifoldXct: A semi-supervised method for predicting cell-cell interactions and mapping cellular communication graphs

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Cell systems 2023年第4期14卷 302-311.e4页

作者： Yongjian Yang Guanxun Li Yan Zhong Qian Xu Yu-Te Lin Cristhian Roman-Vicharra Robert S Chapkin James J Cai Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA. Department of Statistics Texas A&M University College Station TX 77843 USA. Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University 3663 North Zhongshan Road Shanghai 200062 China. Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA. Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan. Department of Nutrition and the Program in Integrative Nutrition & Complex Diseases Texas A&M University College Station TX 77843 USA. Electronic address: r-chapkin@tamu.edu. Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA Interdisciplinary Program of Genetics Texas A&M University College Station TX 77843 USA. Electronic address: jcai@tamu.edu.

We present scTenifoldXct, a semi-supervised computational tool for detecting ligand-receptor (LR)-mediated cell-cell interactions and mapping cellular communication graphs. Our method is based on manifold alignment, using LR pairs as inter-data correspondences to embed ligand and receptor genes expressed in interacting cells into a unified latent space. Neural networks are employed to minimize the distance between corresponding genes while preserving the structure of gene regression networks. We apply scTenifoldXct to real datasets for testing and demonstrate that our method detects interactions with high consistency compared with other methods. More importantly, scTenifoldXct uncovers weak but biologically relevant interactions overlooked by other methods. We also demonstrate how scTenifoldXct can be used to compare different samples, such as healthy vs. diseased and wild type vs. knockout, to identify differential interactions, thereby revealing functional implications associated with changes in cellular communication status.

关键词： cell-cell interaction cellular communication gene regression network machine learning manifold alignment neural networks scRNA-seq single-cell RNA sequencing

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Author Correction: A molecular timescale for eukaryote evolution with implications for the origin of red algal-derived plastids

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Nature communications 2021年第1期12卷 3574页

作者： Jürgen F H Strassert Iker Irisarri Tom A Williams Fabien Burki Department of Organismal Biology Program in Systematic Biology Uppsala University Uppsala Sweden. Department of Ecosystem Research Leibniz Institute of Freshwater Ecology and Inland Fisheries Berlin Germany. Department of Biodiversity and Evolutionary Biology Museo Nacional de Ciencias Naturales (MNCN-CSIC) Madrid Spain. Department of Applied Bioinformatics Institute for Microbiology and Genetics University of Göttingen and Campus Institute Data Science (CIDAS) Göttingen Germany. School of Biological Sciences University of Bristol Life Sciences Building Bristol UK. Department of Organismal Biology Program in Systematic Biology Uppsala University Uppsala Sweden. fabien.burki@ebc.uu.se. Science for Life Laboratory Uppsala University Uppsala Sweden. fabien.burki@ebc.uu.se.

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Author Correction: Genomic footprints of activated telomere maintenance mechanisms in cancer (Dec, 10.1038/s41467-019-13824-9, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-1页

作者： Sieverling, Lina Hong, Chen Koser, Sandra D. Ginsbach, Philip Kleinheinz, Kortine Hutter, Barbara Braun, Delia M. Cortes-Ciriano, Isidro Xi, Ruibin Kabbe, Rolf Park, Peter J. Eils, Roland Schlesner, Matthias Brors, Benedikt Rippe, Karsten Jones, David T. W. Feuerbach, Lars Division of Applied Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Faculty of Biosciences Heidelberg University 69120 Heidelberg Germany Division of Applied Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Department for Bioinformatics and Functional Genomics Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Institute of Pharmacy and Molecular Biotechnology and BioQuant Heidelberg University Heidelberg Germany German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany Heidelberg Center for Personalized Oncology (DKFZ-HIPO) German Cancer Research Center (DKFZ) Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant 69120 Heidelberg Germany Department of Biomedical Informatics Harvard Medical School Boston Massachusetts 02115 USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge CB2 1EW UK Ludwig Center at Harvard Medical School Boston Massachusetts 02115 USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge UK Ludwig Center at Harvard Medical School Boston MA USA School of Mathematical Sciences and Center for Statistical Science Peking University Beijing 100871 China Heidelberg University Heidelberg Germany New BIH Digital Health Center Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin Berlin Germany Bioi

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Sustaining public plant breeding programs across generations

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Crop science 2025年第3期65卷

作者： Iago Hale Jenny Koebernick Jenna Hershberger Trevor Rife Juan-David Arbelaez Neil Anderson Anton Bekkerman Martin Bohn Fred Bourland Timothy Burke Peng Chee Kate Evans Nathan Fumia Mitchell Feldmann Ksenija Gasic Steve Hague Ana María Heilman-Morales Amanda Hulse Kemp Carlos Iglesias Lukas Mueller Jim Luby Richard Pratt Addie Thompson Richard Vierling Margaret Worthington Margaret Smith Gayle Volk Marnin Wolfe Michael Kantar Department of Agriculture Nutrition and Food Systems University of New Hampshire Durham New Hampshire USA Department of Crop Soil and Environmental Sciences Auburn University Auburn Alabama USA Plant and Environmental Sciences Department Clemson University Clemson South Carolina USA Department of Crop Sciences University of Illinois Urbana Illinois USA Department of Horticultural Science University of Minnesota Saint Paul Minnesota USA Northeast Research and Extension Center (NEREC) University of Arkansas Keiser Arkansas USA Bayer CropScience LLC St. Louis Missouri USA Department of Soil and Crop Sciences University of Georgia Tifton Georgia USA Horticulture Tree Fruit Research and Extension Center Washington State University Wenatchee Washington USA Hawaii Agriculture Research Center Kunia Hawaiʻi USA Department of Plant Sciences University of California Davis Davis California USA Big Data Pipeline Unit North Dakota State University AES Fargo North Dakota USA USDA-ARS Genomics and Bioinformatics Research Unit Raleigh North Carolina USA Department of Horticultural Science North Carolina State University Raleigh North Carolina USA Boyce Thompson Institute Cornell University Ithaca New York USA Department of Plant and Environmental Sciences New Mexico State University Las Cruces New Mexico USA Department of Plant Soil and Microbial Sciences Michigan State University East Lansing Michigan USA AgriLife Research and Extension Center at Vernon Texas A&M University College Station Texas USA Department of Horticulture University of Arkansas System Division of Agriculture Fayetteville Arkansas USA School of Integrative Plant Science Plant Breeding and Genetics Section Cornell University Ithaca New York USA USDA-ARS National Laboratory for Genetic Resources Preservation Fort Collins Colorado USA Department of Tropical Plant & Soil Sciences University of Hawaii at Manoa Honolulu Hawaiʻi USA

Plant breeding in the public sector is a multigenerational process that creates new plant varieties intended to meet current and future needs of society. Many public sector plant breeding programs are over a century old, and they continue to curate plant genetic resources that are far older still. While individual breeders serve as temporary leaders of these programs and the plant genetic resources they maintain, it is only their institutions that have the capacity to provide the necessary generational glue, enabling the accrual of long-term value to both breeders and society. Identifying best practices to ensure mutual benefit to both public sector breeders and their institutions is critical to achieving the smooth leadership transitions necessary for the sustainability and long-term impact of public breeding programs. The findings presented here suggest that the successful passing of the torch in such programs depends not only on strategic institutional support but also, critically, on the routine actions and mindset of the breeders entrusted with their leadership. Plant breeding has long been considered an essential public good. Despite many public breeding programs being over a century old, there are no standard procedures for dealing with inevitable leadership transitions. Here, we report on the findings of a workshop convened to explore the practices required for successful transitions and what current plant breeders and their host institutions can do to prevent germplasm loss and ensure program continuity.

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Hierarchical colorings of cographs

arXiv

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arXiv 2019年

作者： Valdivia, Dulce I. Gei, Manuela Hellmuth, Marc Rosales, Maribel Hernandez Stadler, Peter F. Universidad Autonoma de Aguascalientes Centro de Ciencias Basicas Av. Universidad 940 Aguascalientes20131 Mexico Bioinformatics Group Department of Computer Science and Interdisciplinary Center for Bioinformatics Universitat Leipzig Hartelstrae 16-18 LeipzigD-04107 Institute of Mathematics and Computer Science University of Greifswald Walther-Rathenau-Strae 47 GreifswaldD-17487 Center for Bioinformatics Saarland University Building E 2.1 P.O. Box 151150 SaarbruckenD-66041 CONACYT-Instituto de Matematicas UNAM Juriquilla Blvd. Juriquilla 3001 Juriquilla Queretaro76230 Mexico German Centre for Integrative Biodiversity Research Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases Centre for Biotechnology and Biomedicine Leipzig University Leipzig Max Planck Institute for Mathematics in the Sciences Inselstrae 22 LeipzigD-04103 Germany Institute for Theoretical Chemistry University of Vienna Wahringerstrasse 17 WienA-1090 Facultad de Ciencias Universidad National de Colombia Sede Bogota Colombia Santa Fe Insitute 1399 Hyde Park Rd. Santa FeNM87501 United States

Cographs are exactly hereditarily well-colored graphs, i.e., the graphs for which a greedy coloring of every induced subgraph uses only the minimally necessary number of colors (G). In recent work on reciprocal best match graphs so-called hierarchically coloring play an important role. Here we show that greedy colorings are a special case of hierarchical coloring, which also require no more than (G) colors. Copyright © 2019, The Authors. All rights reserved.

关键词： Coloring

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Prognostic factors and treatment response in HER2-positive gastric cancer patients receiving trastuzumab deruxtecan: secondary analysis of the EN-DEAVOR study

ESMO Gastrointestinal Oncology

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ESMO Gastrointestinal Oncology 2025年 8卷

作者： K. Nakanishi N. Sugimoto Y. Kodera H. Kawakami A. Makiyama H. Konishi S. Morita Y. Narita K. Minashi M. Imano R. Inamoto T. Nishina T. Kawakami M. Hagiwara H. Kume K. Yamaguchi W. Hashimoto K. Muro Department of Gastroenterological Surgery Nagoya University Graduate School of Medicine Nagoya Japan Department of Genetic Oncology Osaka International Cancer Institute Chuo-ku Osaka Japan NHO Nagoya Medical Center Nagoya Aichi Japan Department of Medical Oncology Kindai University Faculty of Medicine Osakasayama City Osaka Japan Department of Clinical Oncology Tohoku University Graduate School of Medicine Sendai Miyagi Japan Cancer Center Gifu University Hospital Gifu Japan Department of Surgery Kyoto Prefectural University of Medicine Kyoto Japan Department of Biomedical Statistics and Bioinformatics Kyoto University Graduate School of Medicine Yoshidakonoecho Kyoto Japan Department of Clinical Oncology Aichi Cancer Center Hospital Nagoya Aichi Japan Clinical Trial Promotion Department and Department of Gastroenterology Chiba Cancer Center Chiba Japan Department of Surgery Kindai University Faculty of Medicine Osakasayama Osaka Japan Department of Gastroenterology Saitama Cancer Center Saitama Japan Department of Gastrointestinal Medical Oncology NHO Shikoku Cancer Center Minamiumemotomachi Matsuyama Ehime Japan Division of Gastrointestinal Oncology Shizuoka Cancer Center Sunto-gun Shizuoka Japan Department of Surgery Nihonkai General Hospital Sakata Yamagata Japan Oncology Medical Science Department I Daiichi Sankyo Co. Ltd. Chuo City Tokyo Japan Data Intelligence Department Daiichi Sankyo Co. Ltd. Chuo City Tokyo Japan

Background EN-DEAVOR was a multi-institutional retrospective study evaluating the effectiveness and safety of trastuzumab deruxtecan (T-DXd) in gastric cancer patients. This secondary analysis investigated prognostic factors for real-world progression-free survival (rwPFS) and objective response rate (ORR) for T-DXd as third- or later-line treatment. Patients and methods Patients aged ≥20 years with histopathologically confirmed human epidermal growth factor receptor 2 (HER2)-positive unresectable advanced or recurrent gastric or gastroesophageal junction adenocarcinoma, who had worsened after chemotherapy, were included. Patients received T-DXd as third- or later-line therapy between September 2020 and September 2021. Univariate and multivariate analyses identified prognostic factors for rwPFS and ORR. Results Of the 307 patients, 75.6% were male and 69.1% were aged ≥65 years. The median duration of prior trastuzumab treatment was 6.5 months (range 0-81.5 months). Multivariate analysis showed HER2 immunohistochemistry (IHC) 3+ [hazard ratio (HR) 0.65, 95% confidence interval (CI) 0.49-0.86], intestinal type lesions (HR 0.59, 95% CI 0.43-0.79), modified Glasgow Prognostic Score (mGPS) 0 and 1 (HR 0.71, 95% CI 0.53-0.95), and longer duration of prior trastuzumab treatment (≥ median) (HR 0.75, 95% CI 0.58-0.97) as positive prognostic factors for rwPFS. Longer prior trastuzumab treatment was also a positive prognostic factor for ORR (odds ratio 2.02, 95% CI 1.13-3.63). Conclusions Patients with clinical benefits from prolonged trastuzumab treatment are likely to benefit from T-DXd. Similarly, HER2 IHC 3+, intestinal type lesions, and better mGPS (0 or 1) are associated with longer rwPFS. However, T-DXd should not be withheld even in patients without these factors, as a median PFS of 3.42 months was observed in those with the shortest prior trastuzumab exposure.

关键词： EN-DEAVOR gastric or gastroesophageal junction cancer HER2 positive prognostic factors trastuzumab deruxtecan modified Glasgow Prognostic Score

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Leveraging implicit knowledge in neural networks for functional dissection and engineering of proteins (vol 1, pg 225, 2019)

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NATURE MACHINE INTELLIGENCE 2019年第6期1卷 290-290页

作者： zu Belzen, Julius Upmeier Buergel, Thore Holderbach, Stefan Bubeck, Felix Adam, Lukas Gandor, Catharina Klein, Marita Mathony, Jan Pfuderer, Pauline Platz, Lukas Przybilla, Moritz Schwendemann, Max Heid, Daniel Hoffmann, Mareike Daniela Jendrusch, Michael Schmelas, Carolin Waldhauer, Max Lehmann, Irina Niopek, Dominik Eils, Roland University of Heidelberg Synthetic Biology Group Institute for Pharmacy and Molecular Biotechnology and Center for Quantitative Analysis of Molecular and Cellular Biosystems Heidelberg Germany (GRID:grid.7700.0) (ISNI:0000 0001 2190 4373) Digital Health Center Berlin Institute of Health (BIH) and Charité University Medicine Berlin Germany (GRID:grid.6363.0) (ISNI:0000 0001 2218 4662) University of Heidelberg Synthetic Biology Group Institute for Pharmacy and Molecular Biotechnology and Center for Quantitative Analysis of Molecular and Cellular Biosystems Heidelberg Germany (GRID:grid.7700.0) (ISNI:0000 0001 2190 4373) University of Heidelberg Synthetic Biology Group Institute for Pharmacy and Molecular Biotechnology and Center for Quantitative Analysis of Molecular and Cellular Biosystems Heidelberg Germany (GRID:grid.7700.0) (ISNI:0000 0001 2190 4373) German Cancer Research Center (DKFZ) Department of Theoretical Bioinformatics Heidelberg Germany (GRID:grid.7497.d) (ISNI:0000 0004 0492 0584) University Hospital Heidelberg Department of Infectious Diseases Virology Heidelberg Germany (GRID:grid.5253.1) (ISNI:0000 0001 0328 4908) BioQuant Center and Cluster of Excellence CellNetworks at Heidelberg University Heidelberg Germany (GRID:grid.7700.0) (ISNI:0000 0001 2190 4373) Berlin Institute of Health (BIH) and Charité University Medicine Molecular Epidemiology Unit Berlin Germany (GRID:grid.484013.a) University of Heidelberg Synthetic Biology Group Institute for Pharmacy and Molecular Biotechnology and Center for Quantitative Analysis of Molecular and Cellular Biosystems Heidelberg Germany (GRID:grid.7700.0) (ISNI:0000 0001 2190 4373) University Hospital Heidelberg Health Data Science Unit Heidelberg Germany (GRID:grid.5253.1) (ISNI:0000 0001 0328 4908) Digital Health Center Berlin Institute of Health (BIH) and Charité University Medicine Berlin Germany (GRID:grid.6363.0) (ISNI:0000 0001 2218 4662) University Hospital Heidelberg Health Data Science Unit Heidelberg Germany (GRI

Proteins are nature’s most versatile molecular machines. Deep neural networks trained on large protein datasets have recently been used to tackle the unmet complexity of protein sequence–function relationships. The implicit knowledge contained in these networks represents a powerful, but thus far inaccessible, resource for understanding protein biology. Here, we show that occlusion-based sensitivity analysis can leverage the knowledge present in deep-neural-network-based protein sequence classifiers to identify functionally relevant parts of proteins. We first validated our approach by successfully predicting positions that mediate small molecule binding or catalytic activity across different protein classes. Next, we inferred the impact of point mutations on the activity of ERK and HRas, signalling factors frequently deregulated in cancer. Finally, we used our approach to identify engineering hotspots in CRISPR–Cas9 and anti-CRISPR protein AcrIIA4. Our work demonstrates how implicit knowledge in neural networks can be harnessed for protein functional dissection and protein *** neural networks are a powerful tool for predicting protein function, but identifying the specific parts of a protein sequence that are relevant to its functions remains a challenge. An occlusion-based sensitivity technique helps interpret these deep neural networks, and can guide protein engineering by locating functionally relevant protein positions.

关键词： Proteins

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Building a precision medicine infrastructure at a national level: The Swedish experience

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Cambridge prisms. Precision medicine 2023年 1卷 e15页

作者： Anders Edsjö Anna Lindstrand David Gisselsson Paula Mölling Mikaela Friedman Lucia Cavelier Maria Johansson Hans Ehrencrona Therese Fagerqvist Tobias Strid Lovisa Lovmar Bo Jacobsson Åsa Johansson Lars Engstrand Craig E Wheelock Per Sikora Valtteri Wirta Thoas Fioretos Richard Rosenquist Department of Clinical Genetics Pathology and Molecular Diagnostics Office for Medical Services Region Skåne Lund Sweden. Division of Pathology Department of Clinical Sciences Lund University Lund Sweden. Department of Molecular Medicine and Surgery Karolinska Institutet Stockholm Sweden. Clinical Genetics Karolinska University Hospital Solna Sweden. Genomic Medicine Center Karolinska Karolinska University Hospital Stockholm Sweden. Division of Clinical Genetics Department of Laboratory Medicine Lund University Lund Sweden. Department of Laboratory Medicine Faculty of Medicine and Health Örebro University Örebro Sweden. Lund University Collaboration Office Lund University Lund Sweden. Innovation Partnership Office Uppsala University Uppsala Sweden. Department of Clinical Pathology Biological and Clinical Sciences Linköping University Linköping Sweden. Clinical Genomics Linköping Linköping University Linköping Sweden. Department of Clinical Genetics and Genomics Sahlgrenska University Hospital Gothenburg Sweden. Department of Obstetrics and Gynecology Sahlgrenska Academy Gothenburg University Gothenburg Sweden. Department of Obstetrics and Gynecology Sahlgrenska University Hospital Gothenburg Sweden. Department of Immunology Genetics and Pathology Science for Life Laboratory Uppsala University Uppsala Sweden. Department of Microbiology Tumor and Cell Biology Centre for Translational Microbiome Research Karolinska Institutet Solna Sweden. Unit of Integrative Metabolomics Institute of Environmental Medicine Karolinska Institutet Stockholm Sweden. Department of Respiratory Medicine and Allergy Karolinska University Hospital Stockholm Sweden. Clinical Genomics Gothenburg Science for Life Laboratory University of Gothenburg Gothenburg Sweden. Bioinformatics Data Center Core Facilities Sahlgrenska Academy University of Gothenburg Gothenburg Sweden. Department of Microbiology Tumor and Cell Biology Clinical Genomics Stockholm Science Life Labor

Precision medicine has the potential to transform healthcare by moving from one-size-fits-all to personalised treatment and care. This transition has been greatly facilitated through new high-throughput sequencing technologies that can provide the unique molecular profile of each individual patient, along with the rapid development of targeted therapies directed to the Achilles heels of each disease. To implement precision medicine approaches in healthcare, many countries have adopted national strategies and initiated genomic/precision medicine initiatives to provide equal access to all citizens. In other countries, such as Sweden, this has proven more difficult due to regionally organised healthcare. Using a bottom-up approach, key stakeholders from academia, healthcare, industry and patient organisations joined forces and formed Genomic Medicine Sweden (GMS), a national infrastructure for the implementation of precision medicine across the country. To achieve this, Genomic Medicine Centres have been established to provide regionally distributed genomic services, and a national informatics infrastructure has been built to allow secure data handling and sharing. GMS has a broad scope focusing on rare diseases, cancer, pharmacogenomics, infectious diseases and complex diseases, while also providing expertise in informatics, ethical and legal issues, health economy, industry collaboration and education. In this review, we summarise our experience in building a national infrastructure for precision medicine. We also provide key examples how precision medicine already has been successfully implemented within our focus areas. Finally, we bring up challenges and opportunities associated with precision medicine implementation, the importance of international collaboration, as well as the future perspective in the field of precision medicine.

关键词： genomic medicine implementation national infrastructure precision medicine

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CCTV traffic congestion analysis at Pejompongan using case based reasoning 1

CCTV traffic congestion analysis at Pejompongan using case b...

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1st International Conference on Information and Communications Technology, ICOIACT 2018

作者： Surjandy Anindra, Firman Soeparno, Haryono Napitupulu, Togar Alam Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Departement BINUS Graduate Program - Master of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Departement of Informatics and Communication Universitas Nasional Jakarta12520 Indonesia Information Systems Department BINUS Graduate Program - Master of Information Systems Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781538609545

Congestion in Jakarta is a chronic problem and is still being sought. The growing city of Jakarta is facing various international events, increasing the complexity of these congestion problems. This research is aimed to propose an alternative solution for congestion problem in Jakarta using Cased-Based Reasoning (CBR) approach on Pejompongan area. The CBR integrated model analyzes congestion at a given point in a particular hour to identify the same case. The Cased-Based Management (CBM) algorithm approach is used to capture transient problems and ultimately reduce case base cases for their contribution to long-term maintenance. Congestion data is obtained from CCTV cameras via website *** in some strategic areas. With that data, we use CBR's integrated model to adjust traffic time for traffic flow to be optimized so that congestion will decompose. The results of this study can be used by relevant government agencies and policymakers in Jakarta to formulate the best solution to solve the congestion problem in the Indonesian capital. Image analysis at nights and bad weather conditions become a limitation and also the integration with traffic lights control in this paper that can be a new topic for further research. © 2018 IEEE.

关键词： Traffic congestion

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