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2nd International Conference on Eco Engineering Development 2018, ICEED 2018

作者： Cenggoro, Tjeng Wawan Tanzil, Fidelson Aslamiah, Ayu Hidayah Karuppiah, Ettikan Kandasamy Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia NVIDIA Corporation Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Deep Learning is currently the state-of-the-art technique for various Computer Vision tasks, including object counting. Despite of its high performance, Deep Learning requires a gigantic amount of training data to show its best result. Getting this massive data in reasonable time requires a proper strategy such as crowdsourcing. However, in case of object counting, we found no crowdsourcing system able to effectively collect necessary data. To tackle this problem, we develop a crowdsourcing system to annotate image for object counting dataset. This system is also equipped with validation system to ensure the quality of collected dataset. © 2018 Institute of Physics Publishing. All rights reserved.

关键词： Crowdsourcing

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The repertoire of mutational signatures in human cancer (vol 578, pg 94, 2020)

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NATURE 2023年第7948期614卷 E41-E41页

作者： Alexandrov, Ludmil B. Kim, Jaegil Haradhvala, Nicholas J. Huang, Mi Ni Ng, Alvin Wei Tian Wu, Yang Boot, Arnoud Covington, Kyle R. Gordenin, Dmitry A. Bergstrom, Erik N. Islam, S. M. Ashiqul Lopez-Bigas, Nuria Klimczak, Leszek J. McPherson, John R. Morganella, Sandro Sabarinathan, Radhakrishnan Wheeler, David A. Mustonen, Ville Getz, Gad Rozen, Steven G. Stratton, Michael R. Department of Cellular and Molecular Medicine Department of Bioengineering Moores Cancer Center University of California San Diego CA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Cellular and Molecular Medicine and Department of Bioengineering University of California San Diego La Jolla CA USA UC San Diego Moores Cancer Center San Diego CA USA University of California San Diego San Diego CA USA Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Massachusetts General Hospital Boston MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama Japan Computational Biology Program Ontario Institute for Cancer Research Toronto ON Canada Programme in Cancer & Stem Cell Biology Duke-NUS Medical School Singapore Singapore Centre for Computational Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Centre for Computational Biology Duke-NUS Medical School Singapore Singapore SingHealth Duke-NUS Institute of Precision Medicine National Heart Centre Singapore Singapore Singapore Cancer Science Institute of Singapore National University of Singapore Singapore Singapore Institute of Molecular and Cell Biology Singapore Singapore Laboratory of Cancer Epigenome Division of Medical Science National Cancer Centre Singapore Singapore Singapore Duke-NUS Medical School Singapore Singapore Human Genome Sequencing Center Baylor College of Medicine Houston TX USA Dan L. Duncan Cancer Center Baylor College of Medicine Houston TX USA Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA Genome Integrity and Structural Biology Laboratory National Ins

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Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

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NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

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Improving AI models for rare thyroid cancer subtype by text guided diffusion models

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

作者： Dai, Fang Yao, Siqiong Wang, Min Zhu, Yicheng Qiu, Xiangjun Sun, Peng Qiu, Cheng Yin, Jisheng Shen, Guangtai Sun, Jingjing Wang, Maofeng Wang, Yun Yang, Zheyu Sang, Jianfeng Wang, Xiaolei Sun, Fenyong Cai, Wei Zhang, Xingcai Lu, Hui Department of Bioinformatics and Biostatistics School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai China SJTU-Yale Joint Center of Biostatistics and Data Science Technical Center for Digital Medicine National Center for Translational Medicine Shanghai Jiao Tong University Shanghai China Department of Clinical Laboratory Medicine Shanghai Tenth People’s Hospital School of Medicine Tongji University Shanghai China Department of Critical Care Medicine Jiuquan Hospital of Shanghai General Hospital Shanghai Jiaotong University School of Medicine Gansu Jiuquan China Department of Ultrasound Pudong New Area People’s Hospital Affiliated to Shanghai University of Medicine and Health Sciences Shanghai China Department of Automation Tsinghua University Beijing China Medical college Nantong University Jiangsu Nantong China Shcool of Artificial Intelligence University of Chinese Academy of sciences Beijing China Xin’an League People’s Hospital Inner Mongolia Xing’an League China Department of Ultrasound Shanghai Fourth People’s Hospital Affiliated to Tongji University Shanghai China Department of Biomedical Sciences Laboratory Affiliated Dongyang Hospital of Wenzhou Medical University Zhejiang Dongyang China Department of Hepatobiliary pancreatic center Xuzhou City Central Hospital Jiangsu Xuzhou China Department of General Surgery Ruijin Hospital Shanghai Jiaotong University School of medicine Shanghai China Nanjing Drum Tower Hospital The Affiliated Hospital of Nanjing University Medical School Jiangsu Nanjing China World Tea Organization Cambridge MA United States Institute of Bioinformatics Shanghai Academy of Experimental Medicine Shanghai China

Artificial intelligence applications in oncology imaging often struggle with diagnosing rare tumors. We identify significant gaps in detecting uncommon thyroid cancer types with ultrasound, where scarce data leads to frequent misdiagnosis. Traditional augmentation strategies do not capture the unique disease variations, hindering model training and performance. To overcome this, we propose a text-driven generative method that fuses clinical insights with image generation, producing synthetic samples that realistically reflect rare subtypes. In rigorous evaluations, our approach achieves substantial gains in diagnostic metrics, surpasses existing methods in authenticity and diversity measures, and generalizes effectively to other private and public datasets with various rare cancers. In this work, we demonstrate that text-guided image augmentation substantially enhances model accuracy and robustness for rare tumor detection, offering a promising avenue for more reliable and widespread clinical adoption. © The Author(s) 2025.

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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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The evolutionary history of 2,658 cancers (vol 578, pg 122, 2020)

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NATURE 2023年第7948期614卷 E42-E42页

作者： Gerstung, Moritz Jolly, Clemency Leshchiner, Ignaty Dentro, Stefan C. Gonzalez, Santiago Rosebrock, Daniel Mitchell, Thomas J. Rubanova, Yulia Anur, Pavana Yu, Kaixian Tarabichi, Maxime Deshwar, Amit Wintersinger, Jeff Kleinheinz, Kortine Vazquez-Garcia, Ignacio Haase, Kerstin Jerman, Lara Sengupta, Subhajit Macintyre, Geoff Malikic, Salem Donmez, Nilgun Livitz, Dimitri G. Cmero, Marek Demeulemeester, Jonas Schumacher, Steven Fan, Yu Yao, Xiaotong Lee, Juhee Schlesner, Matthias Boutros, Paul C. Bowtell, David D. Zhu, Hongtu Getz, Gad Imielinski, Marcin Beroukhim, Rameen Sahinalp, S. Cenk Ji, Yuan Peifer, Martin Markowetz, Florian Mustonen, Ville Yuan, Ke Wang, Wenyi Morris, Quaid D. Spellman, Paul T. Wedge, David C. Van Loo, Peter European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Wellcome Sanger Institute Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany University of Ljubljana Ljubljana Slovenia The Francis Crick Institute London UK Big Data Institute University of Oxford Oxford UK Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Big Data Institute Li Ka Shing Centre University of Oxford Oxford UK University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Statistics Columbia University New York NY USA Department of Haematology University of Cambridge Cambridge UK University of Leuven Leuven Belgium Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Charlestown MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Dana-Farber Cancer Institute Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Oxford NIHR Biomedical Research Centre Oxford UK Oxford NIHR Biomedical Research Centre University of Oxford Oxford UK University of Toronto Toronto Ontario Canada Vector Institute Toronto Ontario Canada Vector Institute Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Ontario Institute for Cancer Research Toronto Ontario Canada University of California Los Angeles

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Enhancing rare disease detection with deep phenotyping from EHR narratives: evaluation on Jeune syndrome

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International Journal of Medical Informatics 2025年 203卷

作者： Carole Faviez Xiaomeng Wang Marc Vincent Nicolas Garcelon Sophie Saunier Valérie Cormier-Daire Xiaoyi Chen Anita Burgun Clinical Bioinformatics Lab Université Paris Cité Institut Imagine INSERM UMR 1163 Paris France Université Paris Cité Paris France Data Science Platform Université Paris Cité Institut Imagine INSERM UMR 1163 Paris France Laboratory of Renal Hereditary Diseases Université Paris Cité Institut Imagine INSERM UMR 1163 Paris France Université Paris Cité Centre of Reference for Constitutional Bone Diseases (MOC) Hôpital Necker-Enfants Malades Institut Imagine INSERM UMR 1163 Paris France Division of Computational Health Sciences Department of Surgery University of Minnesota Minneapolis MN USA Département d’informatique Médicale Hôpital Necker-Enfants Malades AP-HP 75015 Paris France

Background Patients with rare diseases frequently experience misdiagnoses and long diagnostic delays. Accelerating their diagnosis is essential to ensure timely access to appropriate care. Given the increasing availability of EHRs, combining artificial intelligence and deep phenotyping from large-scale clinical databases offers a promising approach to identify undiagnosed patients. This study assesses the impact of improved phenotype extraction on a screening algorithm for Jeune syndrome, a rare ciliopathy characterized by skeletal abnormalities. Methods Phenotypes from Jeune syndrome patients and controls were automatically extracted from patient unstructured EHRs relying on two thesauri separately: the standard UMLS Metathesaurus and the UMLS+, an enhanced version incorporating additional terms identified through deep learning. The machine learning pipeline that we designed for classifying patients with renal ciliopathy was adapted for Jeune syndrome detection. The model was trained and tested on both the datasets created using the two phenotyping strategies. Results Using UMLS+ strongly improved the classification of patients with Jeune syndrome, increasing the sensitivity from 49 % to 95 % while maintaining a 90 % specificity. The review of a subset of misclassified controls showed that most of them (69 %) had other genetic skeletal disorders, indicating that the model also captured patients who would benefit from referral to a bone disease geneticist. Conclusion AI-based screening combined with high-quality deep phenotyping can help reduce diagnostic delay in rare diseases. The completeness and accuracy of phenotyping from EHRs have a strong impact on screening performances.

关键词： Diagnosis support Rare disease Electronic health record Supervised machine learning Named entity recognition Deep phenotyping

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Publisher Correction: Comprehensive analysis of chromothripsis in 2,658 human cancers using whole-genome sequencing

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Nature genetics 2023年第5期55卷 893页

作者： Isidro Cortés-Ciriano Jake June-Koo Lee Ruibin Xi Dhawal Jain Youngsook L Jung Lixing Yang Dmitry Gordenin Leszek J Klimczak Cheng-Zhong Zhang David S Pellman Peter J Park Department of Biomedical Informatics Harvard Medical School Boston MA USA. Ludwig Center at Harvard Boston MA USA. Centre for Molecular Science Informatics Department of Chemistry University of Cambridge Cambridge UK. European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK. School of Mathematical Sciences and Center for Statistical Science Peking University Beijing China. Ben May Department for Cancer Research University of Chicago Chicago IL USA. Department of Human Genetics The University of Chicago Chicago IL USA. Genome Integrity and Structural Biology Laboratory National Institute of Environmental Health Sciences US National Institutes of Health Durham NC USA. Integrative Bioinformatics Group National Institute of Environmental Health Sciences US National Institutes of Health Durham NC USA. Department of Data Sciences Dana-Farber Cancer Institute Boston MA USA. Department of Pediatric Oncology Dana-Farber Cancer Institute Boston MA USA. Department of Cell Biology Harvard Medical School Blavatnik Institute Boston MA USA. Howard Hughes Medical Institute Boston MA USA. Department of Biomedical Informatics Harvard Medical School Boston MA USA. peter_park@hms.harvard.edu. Ludwig Center at Harvard Boston MA USA. peter_park@hms.harvard.edu.

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Ecological Show Cave and Wild Cave: Negative Binomial Gllvm's Arthropod Community Modelling 3

Ecological Show Cave and Wild Cave: Negative Binomial Gllvm'...

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3rd International Conference on Computer science and Computational Intelligence, ICCSCI 2018

作者： Caraka, Rezzy Eko Shohaimi, Shamarina Kurniawan, Isma Dwi Herliansyah, Riki Budiarto, Arif Sari, Shinta Purnama Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Indonesia Department of Statistics Padjadjaran University Indonesia Department of Biology UIN Sunan Gunung Djati Bandung Indonesia Kalimantan Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Ecology is a branch of biology that studies the interaction and relationship between organisms and their environment. Abundance, distribution of organisms and patterns of biodiversity are great interests for many ecologists. One of interesting ecosystems to be studied is a cave. A cave has a typical environment character with a vulnerable ecosystem. Many caves in Indonesia, particularly in Gunungsewu karst area have been developed into tourist objects (show caves) and managed imprudently. Such cave management has potential to harm the environment and leads to ecosystem destruction. Arthropods are the most abundance fauna in cave that play critical roles in maintaining cave ecosystems equilibrium. In the heart of statistical ecology, we need to analyze the differences on Arthropods community and abiotic (climatic-edaphic) parameters among show caves and wild caves. Statistical techniques are needed for the extraction of such information. GLLVM is one method that is able to explain spatial-based information and is particularly suitable for ecology. In this paper, we use negative binomial models to see the differences on spatial patterns of predator and decomposer Arthropods, also characteristic of edaphic and climatic in each cave. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Caves

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#1056 Multi-omic characterization of naïve treatment samples of epithelial ovarian cancer addressed to neoadjuvant chemotherapy: an hypothesis-generating study on biomarkers of response to platinum-based chemotherapy

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International Journal of Gynecological Cancer 2024年 34卷 A577-A578页

作者： Rita Trozzi Luca Mastrantoni Camilla Nero Simona Duranti Valentina Iacobelli Angelo Minucci Tina Pasciuto Luciano Giacò Gian Franco Zannoni Diana Giannarelli Domenica Lorusso Maria De Bonis Claudia Marchetti Anna Fagotti Giovanni Scambia Department of Women Children and Public Health Sciences Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy UOC Oncologia medica Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy Università Cattolica del Sacro Cuore Rome Italy Facoltà di Medicina e Chirurgia Università Cattolica del Sacro Cuore Rome Italy Scientific Directorate Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy Departmental Unit of Molecular and Genomic Diagnostics Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy Data Collection Research Core Facility Gemelli Science and Technology Park (GSTeP) Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy Bioinformatics Research Core Facility Gemelli Science and Technology Park (GSTeP) Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy Biostatistics Unit Gemelli Science and Technology Park (GSTeP) Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Rome Italy

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