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

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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Reply to: Embracing the taxonomic and topological stability of phylogenomics

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Scientific reports 2024年第1期14卷 4094页

作者： Hsin Lee Kwen-Shen Lee Chia-Hsin Hsu Chen-Wei Lee Ching-En Li Jia-Kang Wang Chien-Chia Tseng Wei-Jen Chen Ching-Chang Horng Colby T Ford Andreas Kroh Omri Bronstein Hayate Tanaka Tatsuo Oji Jih-Pai Lin Daniel Janies National Museum of Marine Biology and Aquarium Pingtung 944401 Taiwan. Department of Geosciences National Taiwan University Taipei 106319 Taiwan. Institute of Oceanography National Taiwan University Taipei 10617 Taiwan. Biology Department National Museum of Natural Science Taichung 404023 Taiwan. Tuple LLC 2413 Commonwealth Ave Charlotte NC 28205 USA. School of Data Science University of North Carolina at Charlotte 9201 University City Blvd Charlotte NC 28223 USA. Department of Bioinformatics and Genomics University of North Carolina at Charlotte 9201 University City Blvd Charlotte NC 28223 USA. Center for Computational Intelligence to Predict Health and Environmental Risks (CIPHER) University of North Carolina at Charlotte 9201 University City Blvd Charlotte NC 28223 USA. Department of Geology and Palaeontology Natural History Museum Vienna 1010 Vienna Austria. School of Zoology Faculty of Life Sciences Tel Aviv University 6997801 Tel Aviv Israel. Steinhardt Museum of Natural History Tel Aviv University 6997801 Tel Aviv Israel. Department of Biological Sciences Graduate School of Science University of Tokyo Tokyo 113‑0033 Japan. University Museum Nagoya University Furo‑cho Nagoya 464‑8601 Japan. Department of Geosciences National Taiwan University Taipei 106319 Taiwan. alexjplin@ntu.edu.tw.

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The need to implement FAIR principles in biomolecular simulations

arXiv

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arXiv 2024年

作者： Amaro, R. Åqvist, J. Bahar, I. Battistini, F. Bellaiche, A. Beltran, D. Biggin, P.C. Bonomi, M. Bowman, G.R. Bryce, R. Bussi, G. Carloni, P. Case, D. Cavalli, A. Chang, C.A. Cheatham, T.E. Cheung, M.S. Chipot, C. Chong, L.T. Choudhary, P. Cisneros, G.A. Clementi, C. Collepardo-Guevara, R. Coveney, P. Covino, R. Crawford, T.D. Dal Peraro, M. de Groot, B. Delemotte, L. De Vivo, M. Essex, J. Fraternali, F. Gao, J. Gelpí, J.L. Gervasio, F.L. Gonzalez-Nilo, F.D. Grubmüller, H. Guenza, M.G. Guzman, H.V. Harris, S. Head-Gordon, T. Hernandez, R. Hospital, A. Huang, N. Huang, X. Hummer, G. Iglesias-Fernández, J. Jensen, J.H. Jha, S. Jiao, W. Jorgensen, W.L. Kamerlin, S.C.L. Khalid, S. Laughton, C. Levitt, M. Limongelli, V. Lindahl, E. Lindorff-Larsen, K. Loverde, S. Lundborg, M. Luo, Y.L. Luque, F.J. Lynch, C.I. MacKerell, A. Magistrato, A. Marrink, S.J. Martin, H. McCammon, J.A. Merz, K. Moliner, V. Mulholland, A. Murad, S. Naganathan, A.N. Nangia, S. Noe, F. Noy, A. Oláh, J. O'Mara, M. Ondrechen, M.J. Onuchic, J.N. Onufriev, A. Osuna, S. Palermo, G. Panchenko, A.R. Pantano, S. Parish, C. Parrinello, M. Perez, A. Perez-Acle, T. Perilla, J.R. Pettitt, B.M. Pietropaolo, A. Piquemal, J.-P. Poma, A. Praprotnik, M. Ramos, M.J. Ren, P. Reuter, N. Roitberg, A. Rosta, E. Rovira, C. Roux, B. Röthlisberger, U. Sanbonmatsu, K. Schlick, T. Shaytan, A.K. Simmerling, C. Smith, J.C. Sugita, Y. Świderek, K. Taiji, M. Tao, P. Tikhonova, I.G. Tirado-Rives, J. Tuñón, I. Van Der Kamp, M.W. Van der Spoel, D. Velankar, S. Voth, G.A. Wade, R. Warshel, A. Welborn, V.V. Wetmore, S. Wong, C.F. Yang, L.-W. Zacharias, M. Orozco, M. Department of Molecular Biology University of California San DiegoCA United States Department of Cell and Molecular Biology Uppsala University Uppsala Sweden Laufer Center for Physical and Quantitative Biology Renaissance School of Medicine Stony Brook University Stony BrookNY United States Department of Biochemistry and Cell Biology Renaissance School of Medicine Stony Brook University Stony BrookNY United States Barcelona Spain European Molecular Biology Laboratory European Bioinformatics Institute Hinxton United Kingdom Structural Bioinformatics and Computational Biochemistry Department of Biochemistry University of Oxford Oxford United Kingdom Institut Pasteur Université Paris Cité CNRS UMR 3528 Computational Structural Biology Unit Paris France Department of Biochemistry and Biophysics University of Pennsylvania PhiladelphiaPA United States Division of Pharmacy and Optometry University of Manchester Manchester United Kingdom Scuola Internazionale Superiore di Studi Avanzati-SISSA Trieste Italy Computational Biomedicine Institute of Advanced Simulations IAS-5 Institute for Neuroscience and Medicine INM-9 Forschungszentrum Jülich GmbH Jülich Germany Department of Physics and Universitätsklinikum RWTH Aachen University Aachen Germany Department of Chemistry & Chemical Biology Rutgers University PiscatawayNJ United States Istituto Italiano di Tecnologia Drug Discovery and Development Bologna Italy Lausanne Switzerland Department of Chemistry University of California RiversideCA United States Department of Medicinal Chemistry College of Pharmacy University of Utah Salt Lake CityUT United States Department of Physics University of Washington SeattleWA United States Pacific Northwest National Laboratory RichlandWA United States LIA CNRS-UIUC UMR n°7019 Université de Lorraine Vandœuvre-lès-Nancy France Department of Physics University of Illinois at Urbana-Champaign UrbanaIL United States Department of Biochemistry and Molecular Biol

The communities that embraced data archiving efforts decades ago are now, in the era of data-driven biology, those gaining the most from the AI revolution. The structural biology community was a pioneer in this regard by establishing the Protein data Bank (PDB) in 1971 and making data accessible using the FAIR (Findable, Accessible, Interoperable and Reusable) principles even before these were articulated [1, 2]. The genomics/bioinformatics community has followed the example, establishing many widely used databases [3, 4]. By contrast, molecular simulation has been anchored in usage paradigms dating back to the seventies, when molecular dynamics (MD) simulation was first applied to study biomacromolecules [5]. At that time, MD was used by theoretical physicists and chemists in "proof of concept" simulations. Still, fifty years later, MD has evolved into a cornerstone molecular biology technique that can provide accurate quantitative analysis and property prediction. MD is now employed by tens of thousands of researchers worldwide, accounting for roughly 15% of global supercomputer usage. Unfortunately, these rich and costly data are not systematically maintained, and when further analyses are required, simulations have to be rerun-an unacceptable situation from scientific, environmental and sustainability standpoints. In this letter, we argue for a collaborative endeavor to archive MD simulation data and describe ongoing efforts to establish cost-effective and sustainable data archiving strategies. © 2024, CC BY-NC-SA.

关键词： Biotic

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Correction: Real-world effectiveness and safety of trastuzumab-deruxtecan in Japanese patients with HER2-positive advanced gastric cancer (EN-DEAVOR study)

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Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association 2025年第1期28卷 62页

作者： Hisato Kawakami Koki Nakanishi Akitaka Makiyama Hirotaka Konishi Satoshi Morita Yukiya Narita Naotoshi Sugimoto Keiko Minashi Motohiro Imano Rin Inamoto Yasuhiro Kodera Hiroki Kume Keita Yamaguchi Wataru Hashimoto Kei Muro Department of Medical Oncology Kindai University Faculty of Medicine Osakasayama Japan. kawakami_h@med.kindai.ac.jp. Department of Gastroenterological Surgery Nagoya University Graduate School of Medicine Nagoya Japan. Cancer Center Gifu University Hospital Gifu Japan. Division of Digestive Surgery Kyoto Prefectural University of Medicine Kyoto Japan. Department of Biomedical Statistics and Bioinformatics Kyoto University Graduate School of Medicine Kyoto Japan. Department of Clinical Oncology Aichi Cancer Center Hospital Nagoya Japan. Department of Genetic Oncology Osaka International Cancer Institute Osaka Japan. Division of Gastroenterology Chiba Cancer Center Chiba Japan. Department of Surgery Kindai University Faculty of Medicine Osakasayama Japan. Department of Gastroenterology Saitama Cancer Center Saitama Japan. Oncology Medical Science Department I Daiichi Sankyo Co. Ltd. Tokyo Japan. Data Intelligence Department Daiichi Sankyo Co. Ltd. Tokyo Japan.

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Proteomics Standards Initiative's ProForma 2.0: Unifying the Encoding of Proteoforms and Peptidoforms

arXiv

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arXiv 2021年

作者： LeDuc, Richard D. Deutsch, Eric W. Binz, Pierre-Alain Fellers, Ryan T. Cesnik, Anthony J. Klein, Joshua A. Van Den Bossche, Tim Gabriels, Ralf Yalavarthi, Arshika Perez-Riverol, Yasset Carver, Jeremy Bittremieux, Wout Kawano, Shin Pullman, Benjamin Bandeira, Nuno Kelleher, Neil L. Thomas, Paul M. Vizcaíno, Juan Antonio National Resource for Translational and Developmental Proteomics Northwestern University EvanstonIL60611 United States Institute for Systems Biology SeattleWA98109 United States Clinical Chemistry Service Lausanne University Hospital Lausanne1011 Switzerland Department of Genetics Stanford University StanfordCA94305 United States Chan Zuckerberg Biohub 499 Illinois St San FranciscoCA94158 United States SciLifeLab School of Engineering Sciences in Chemistry Biotechnology and Health KTH-Royal Institute of Technology Stockholm 113 51 SolnaSE-171 21 Sweden Program for Bioinformatics Boston University BostonMA02215 United States VIB-UGent Center for Medical Biotechnology VIB Technologiepark 75-FSVM II Ghent9052 Belgium Department of Biomolecular Medicine Faculty of Medicine and Health Sciences Ghent University Ghent9000 Belgium Cambridge HinxtonCB10 1SD United Kingdom La Jolla CA92093 United States La Jolla CA92093 United States La Jolla CA92093 United States Toyama University of International Studies Toyama Higashikuromaki 6 5-1 Toyama930-1292 Japan Database Center for Life Science Joint Support-Center for Data Science Research Research Organization of Information and Systems Chiba Kashiwa277-0871 Japan

It is important for the proteomics community to have a standardized manner to represent all possible variations of a protein or peptide primary sequence, including natural, chemicallyinduced and artifactualmodifications. The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) in collaboration with several members of the Consortium for Top-Down Proteomics (CTDP) has developed a standard notation called ProForma 2.0, which is a substantial extension of the original ProForma notation developed by the CTDP. ProForma 2.0 aims to unify the representation of proteoforms and peptidoforms. ProForma 2.0 supports use cases needed for bottom-up and middle-/top-down proteomics approaches and allows the encoding of highly modified proteins and peptides using a humanand machine-readable string. ProForma 2.0 can be used to represent protein modifications in a specified or ambiguous location, designated by mass shifts, chemical formulas, or controlled vocabulary terms, including cross-links (natural and chemical), and atomic isotopes. Notational conventions are based on public controlled vocabularies and ontologies. The most up-to-date full specification document and information about software implementations are available at http://***/proforma. © 2021, CC0.

关键词： Mass spectrometry

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An Improved Version of Texture-based Foreground Segmentation: Block-based Adaptive Segmenter

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Procedia Computer science 2018年 135卷 579-586页

作者： Kahlil Muchtar Faris Rahman Tjeng Wawan Cenggoro Arif Budiarto Bens Pardamean Nodeflux Inc. Jakarta Indonesia Syiah Kuala University Aceh Indonesia 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 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Foreground segmentation is one of moving object detection techniques of computer vision applications. To date, modern moving object detection methods require complex background modeling and thresholds tuning to confront illumination changes. This paper proposes an adaptive approach based on non-overlapping block texture representation. It aims to design a computationally light and efficient solution to improve the robustness of detection. We evaluate our proposed method on internal and public sequences and provide the quantitative and qualitative measurements. Experimental results show that the proposed method can improve the results of previous method and suitable for real-time challenges.

关键词： change detection foreground segmentation texture features

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Health Assistant Wearable-Based data science System Model: A Pilot Study

Health Assistant Wearable-Based Data Science System Model: A...

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International Conference on Information Management and Technology (ICIMTech)

作者： Arif Budiarto Trisna Febriana Teddy Suparyanto Rezzy Eko Caraka Bens Pardamean Computer Science Department Bina Nusantara University Jakarta Information Systems Department Bina Nusantara University Jakarta Bioinformatics and Data Science Research Center Bina Nusantara University Faculty of Science The National University of Malaysia Bangi Computer Science Department Bina Nusantara University Jakarta Indonesia

The rapid development of Information and Communication Technology (ICT) has an impact on the innovation of new technology that gives benefits for health. One of its latest development is wearable device technology. Nowadays, the growth of this technology market is very positive. But unfortunately the growth of this technology is not followed by the availability of supporting applications that provide maximum benefits for users by utilizing personal health and activity data. This paper is intended to provide a report of our initial research in developing a health assistant solution that utilizes wearable device generated data. The whole research process is executed using Design Thinking approach that put the users as the main consideration. This initial research is started by designing a field research that can potentially gathered users' data generated from wearable device. A series of statistical and machine learning techniques are presented to analyze time series data to create a model. Finally, to implement the model, a mobile application prototype is also proposed. The outcomes from this initial research will be used to develop a fully working solution.

关键词： Biomedical monitoring data models data science Computer science Machine learning Medical services Heart rate

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Generalized Spatio Temporal Autoregressive Rainfall-Enso Pattern in East Java Indonesia 1

Generalized Spatio Temporal Autoregressive Rainfall-Enso Pat...

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1st Indonesian Association for Pattern Recognition International Conference, INAPR 2018

作者： Caraka, Rezzy Eko Ulhusna, Mishbah Darmawan Supatmanto, Budi Goldameir, Noor Ell Hutapea, Bonar Darmawan, Gumgum Novitasari, Dian Candra Rini Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia School of Mathematical Sciences National University of Malaysia Malaysia Department of Statistics Padjadjaran University Indonesia Modifikasi Cuaca - BPPT Balai Besar Teknologi Indonesia Faculty of Psychology Tarumanagara University Indonesia Department of Mathematics UIN Sunan Ampel Surabaya Indonesia Riau University Indonesia 'Yptk' Padang Universitas Putra Indonesia Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781538694220

Monthly rainfall analysis and forecasts are made to be able to provide a clear picture of the rain and climate conditions that have occurred and will occur in the territory of Indonesia. The information can be known in an area that has the most substantial maximum rainfall and in an area which has the lowest rainfall described and presented spatial pattern by using the ArcView-GIS. The purpose of making monthly rainfall forecasts is to be used and utilized by users, especially in the agriculture and irrigation sectors to plan and make decisions within the next 1 to 3 months related to climatic conditions, especially rainfall. In this paper, we use annual rainfall data in six location East Java. We analysis ENSO phenomena as well as rainfall forecasting in January - March 2018 by using generalized space-time autoregressive and get an accuracy MAPE out samp;e amount 2.95% dan RMSE out sample amount 4.77. © 2018 IEEE.

关键词： Rain

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The importance of transparency and reproducibility in artificial intelligence research

arXiv

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arXiv 2020年

作者： Haibe-Kains, Benjamin Adam, George Alexandru Hosny, Ahmed Khodakarami, Farnoosh Waldron, Levi Wang, Bo McIntosh, Chris Kundaje, Anshul Greene, Casey S. Hoffman, Michael M. Leek, Jeffrey T. Huber, Wolfgang Brazma, Alvis Pineau, Joelle Tibshirani, Robert Hastie, Trevor Ioannidis, John P.A. Quackenbush, John Aerts, Hugo J.W.L. Princess Margaret Cancer Centre University Health Network TorontoON Canada Department of Medical Biophysics University of Toronto TorontoON Canada Department of Computer Science University of Toronto TorontoON Canada Ontario Institute for Cancer Research TorontoON Canada Vector Institute for Artificial Intelligence TorontoON Canada Program Brigham and Women's Hospital Harvard Medical School BostonMA United States Radiation Oncology and Radiology Dana-Farber Cancer Institute Brigham and Women's Department of Epidemiology and Biostatistics Institute for Implementation Science in Population Health CUNY Graduate School of Public Health and Health Policy New YorkNY United States Peter Munk Cardiac Centre University Health Network TorontoON Canada Hospital Harvard Medical School BostonMA United States Department of Genetics Stanford University School of Medicine StanfordCA United States Dept. of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Childhood Cancer Data Lab Alex's Lemonade Stand Foundation PhiladelphiaPA United States Department of Biostatistics Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany European Molecular Biology Laboratory European Bioinformatics Institute EMBL-EBI Hinxton United Kingdom McGill University MontrealQC Canada Montreal Institute for Learning Algorithms QC Canada StanfordCA United States Department of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Departments of Medicine of Health Research and Policy and of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Department of Statistics Stanford University School of Humanities and Sciences StanfordCA United States Department of Biostatistics Harvard T.H Chan School of Public Health BostonMA Un

In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening. However, the lack of detailed methods and computer code undermines its scientific value. We identify obstacles hindering transparent and reproducible AI research as faced by McKinney et al and provide solutions with implications for the broader field. Copyright © 2020, The Authors. All rights reserved.

关键词： Artificial intelligence

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Reciprocal best match graphs

arXiv

引用

arXiv 2019年

作者： Geiß, Manuela Stadler, Peter F. Hellmuth, Marc Bioinformatics Group Department of Computer Scienceand Interdisciplinary Center of Bioinformatics University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Leipzig Research Center for Civilization Diseases Leipzig University Härtelstraße 16-18 LeipzigD-04107 Germany Max-Planck-Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Inst. f. Theoretical Chemistry University of Vienna Währingerstraße 17 WienA-1090 Austria Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States Institute of Mathematics and Computer Science University of Greifswald Walther-Rathenau-Straße 47 GreifswaldD-17487 Germany Center for Bioinformatics Saarland University Building E 2.1 P.O. Box 151150 SaarbrückenD-66041 Germany

Reciprocal best matches play an important role in numerous applications in computational biology, in particular as the basis of many widely used tools for orthology assessment. Nevertheless, very little is known about their mathematical structure. Here, we investigate the structure of reciprocal best match graphs (RBMGs). In order to abstract from the details of measuring distances, we define reciprocal best matches here as pairwise most closely related leaves in a gene tree, arguing that conceptually this is the notion that is pragmatically approximated by distance- or similarity-based heuristics. We start by showing that a graph G is an RBMG if and only if its quotient graph w.r.t. a certain thinness relation is an RBMG. Furthermore, it is necessary and sufficient that all connected components of G are RBMGs. The main result of this contribution is a complete characterization of RBMGs with 3 colors/species that can be checked in polynomial time. For 3 colors, there are three distinct classes of trees that are related to the structure of the phylogenetic trees explaining them. We derive an approach to recognize RBMGs with an arbitrary number of colors;it remains open however, whether a polynomial-time for RBMG recognition exists. In addition, we show that RBMGs that at the same time are cographs (co-RBMGs) can be recognized in polynomial time. Co-RBMGs are characterized in terms of hierarchically colored cographs, a particular class of vertex colored cographs that is introduced here. The (least resolved) trees that explain co-RBMGs can be constructed in polynomial time. Copyright © 2019, The Authors. All rights reserved.

关键词： bioinformatics

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