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A pilot study of volumetric and density tumor analysis of ACC patients treated with vorinostat in a phase II clinical trial

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HELIYON 2023年第8期9卷 e18680页

作者： Malarkey, Molly Toscano, Alex P. Bagheri, Mohammad Hadi Solomon, Jeffrey Machado, Laura B. Lorusso, Patricia Chen, Alice Folio, Les R. Goncalves, Priscila H. NIH 10 Ctr Dr Bldg 10 Room 1C 340 Bethesda MD 20892 USA NIH Radiol & Imaging Sci Clin Ctr 10 Ctr Dr Bldg 10 Room 1C342 Bethesda MD 20892 USA Frederick Natl Lab Canc Res Clin Monitoring Res Program Directorate Frederick MD 21702 USA Hosp Univ Penn Dept Radiol Div Body Imaging 3400 Spruce St Philadelphia PA 19104 USA Yale Univ 333 Cedar St WWW217 New Haven CT 06510 USA NCI Early Clin Trials Dev Program Div Canc Treatment & Diag NIH 31 Ctr Blvd 3A44 Bethesda MD 20892 USA H Lee Moffitt Canc Ctr & Res Inst 12902 USF Magnolia Dr Tampa FL 33612 USA H Lee Moffitt Canc Ctr & Res Inst Dept Machine Learning 12902 USF Magnolia Dr Tampa FL 33612 USA Univ S Florida Morsani Coll Med Dept Oncol Sci 560 Channelside Dr Tampa FL 33602 USA NIH HIV & AIDS Malignancy Branch Bethesda MD 20892 USA

Rationale and objectives: Adenoid cystic carcinoma (ACC) is a rare salivary gland cancer. The vast majority of clinical trials evaluating systemic therapy efficacy in solid tumors use the Response Evaluation Criteria in Solid Tumors (RECIST) to measure response that is limited to 2 dimensional only evaluations, not taking volume or density into account. The indolent behavior ACC repre-sents a challenge toward an appropriate evaluation of therapy response. Objectives: 1) To describe and contrast volumetric and density changes at each time-point, including changes noted from baseline to best response, to currently used 2 dimensional-only criteria (RECIST) and 2) To report the coefficient of variation in volume measurement among three reviewers on a subset of ACC *** and methods: We retrospectively assessed a cohort of 18 prospectively treated patients with ACC in a phase 2 trial with vorinostat using a volumetric (viable tumor volume, VTV) and density criteria. Three independent and blinded observers segmented target lesions across a sample of randomly selected computed tomography (CT) exams to examine inter-observer ***: We found that the average coefficient of variation among observers for all target lesions was 16.1%, with lung lesions displaying a smaller variation at 14.0% (p-value >0.17). We describe examples of decrease in volume and density in several lesions despite stable disease by ***: This pilot study demonstrates that two-dimensional criteria such as RECIST may not be the best criteria to assess response to therapy, especially with evolving tools within picture archiving and communication system (PACS) that can assess volumetric size, density and texture, however, this should be prospectively studied.

关键词： Adenoid Cystic Carcinoma (ACC) Volume Density Viable Tumor Volume (VTV) Oncology

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Swarm Intelligence in Internet of Medical Things: A Review

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SENSORS 2023年第3期23卷 1466-1466页

作者： Alizadehsani, Roohallah Roshanzamir, Mohamad Izadi, Navid Hoseini Gravina, Raffaele Kabir, H. M. Dipu Nahavandi, Darius Alinejad-Rokny, Hamid Khosravi, Abbas Acharya, U. Rajendra Nahavandi, Saeid Fortino, Giancarlo Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic 3216 Australia Fasa Univ Fac Engn Dept Comp Engn Vali asr Blvd Fasa *** Iran Isfahan Univ Technol Dept Elect & Comp Engn Daneshgah e Sanati Hwy Esfahan 83111 Iran Univ Calabria Dept Informat Modeling Elect & Syst DIMES I-87036 Cosenza Italy UNSW Grad Sch Biomed Engn Biomed Machine Learning Lab BML Sydney NSW 2052 Australia Univ New South Wales UNSW Sydney UNSW Data Sci Hub Sydney NSW 2052 Australia Macquarie Univ AI Enabled Proc AIP Res Ctr Hlth Data Analyt Program Sydney NSW 2109 Australia Ngee Ann Polytech Dept Elect & Comp Engn Singapore 599489 Singapore SUSS Univ Sch Sci & Technol Dept Biomed Engn Singapore 599494 Singapore Asia Univ Dept Biomed Informat & Med Engn Taichung 41354 Taiwan Harvard Univ Harvard Paulson Sch Engn & Appl Sci Allston MA 02134 USA

Continuous advancements of technologies such as machine-to-machine interactions and big data analysis have led to the internet of things (IoT) making information sharing and smart decision-making possible using everyday devices. On the other hand, swarm intelligence (SI) algorithms seek to establish constructive interaction among agents regardless of their intelligence level. In SI algorithms, multiple individuals run simultaneously and possibly in a cooperative manner to address complex nonlinear problems. In this paper, the application of SI algorithms in IoT is investigated with a special focus on the internet of medical things (IoMT). The role of wearable devices in IoMT is briefly reviewed. Existing works on applications of SI in addressing IoMT problems are discussed. Possible problems include disease prediction, data encryption, missing values prediction, resource allocation, network routing, and hardware failure management. Finally, research perspectives and future trends are outlined.

关键词： internet of things internet of medical things internet of things in health swarm intelligence algorithm wearable devices wireless sensor network

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Beyond Ultra-diffuse Galaxies. I. Mass–Size Outliers among the Satellites of Milky Way Analogs

arXiv

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arXiv 2022年

作者： Li, Jiaxuan Greene, Jenny E. Greco, Johnny P. Huang, Song Melchior, Peter Beaton, Rachael Casey, Kirsten Danieli, Shany Goulding, Andy Joseph, Remy Kado-Fong, Erin Kim, Ji Hoon MacArthur, Lauren A. Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States The Ohio State University ColumbusOH43210 United States Department of Astronomy Tsinghua Center for Astrophysics Tsinghua University Beijing100084 China Center for Statistics & Machine Learning Princeton University PrincetonNJ08544 United States Oskar Klein Centre for Cosmoparticle Physics Department of Physics Stockholm University StockholmSE-106 91 Sweden Astronomy Program Department of Physics and Astronomy Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of SNU Astronomy Research Center Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of

Large diffuse galaxies are hard to find, but understanding the environments where they live, their numbers, and ultimately their origins, is of intense interest and importance for galaxy formation and evolution. Using Subaru’s Hyper Suprime-Cam Strategic Survey program, we perform a systematic search for low surface brightness galaxies and present novel and effective methods for detecting and modeling them. As a case study, we surveyed 922 Milky Way analogs in the nearby Universe (0.01 UPG = 0.31 ± 0.05 ultra-puffy galaxies on average, which is consistent with but slightly lower than the observed abundance at this halo mass in the Local Volume. We also construct a sample of ultra-diffuse galaxies (UDGs) in MW analogs and find an abundance of NUDG = 0.44 ± 0.05 per host. With literature results, we confirm that the UDG abundance scales with the host halo mass following a sublinear power law. We argue that our definition for ultra-puffy galaxies, which is based on the mass–size relation, is more physically motivated than the common definition of ultra-diffuse galaxies, which depends on the surface brightness and size cuts and thus yields different surface mass density cuts for quenched and star-forming galaxies. © 2022, CC BY.

关键词： Galaxies

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Position: Bayesian Deep learning is Needed in the Age of Large-Scale AI 41

Position: Bayesian Deep Learning is Needed in the Age of Lar...

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41st International Conference on machine learning, ICML 2024

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. Copyright 2024 by the author(s)

关键词： Contrastive learning

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The SCUBA-2 Large eXtragalactic Survey: 850μm map, catalogue and the bright-end number counts of the XMM-LSS field

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MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 2023年第3期520卷 3669-3687页

作者： Garratt, T. K. Geach, J. E. Tamura, Y. Coppin, K. E. K. Franco, M. Ao, Y. Chen, C. -C Cheng, C. Clements, D. L. Dai, Y. S. Dannerbauer, H. Greve, T. R. Hatsukade, B. Hwang, H. S. Jiang, L. Kohno, K. Koprowski, M. P. Michalowski, M. J. Sawicki, M. Scott, D. Shim, H. Takeuchi, T. T. Wang, W. -H Xue, Y. Q. Yang, C. Univ Hertfordshire Ctr Astrophys Res Hatfield AL10 9AB England Nagoya Univ Div Particle & Astrophys Sci Nagoya Aichi 4648602 Japan Chinese Acad Sci Key Lab Radio Astron Purple Mt Observ 10 Yuanhua Rd Nanjing 210023 Peoples R China Univ Sci & Technol China Sch Astron & Space Sci Hefei 230026 Anhui Peoples R China Acad Sinica Sect 4 Inst Astron & Astrophys 1 Roosevelt Rd Taipei 10617 Taiwan Chinese Acad Sci Natl Astron Observ South Amer Ctr Astron Beijing 100101 Peoples R China Imperial Coll London Blackett Lab Prince Consort Rd London SW7 2AZ England Chinese Acad Sci Natl Astron Observ 20A Datun Rd Beijing 100101 Peoples R China Inst Astrofis Canarias IAC E-38205 Tenerife Spain Univ Laguna Dept Astrofis E-38206 Tenerife Spain Tech Univ Denmark Natl Space Inst DTU Space Elektrovej 327 DK-2800 Lyngby Denmark UCL Dept Phys & Astron Gower St London WC1E 6BT England Univ Tokyo Inst Astron Grad Sch Sci 2-21-1 Osawa Mitaka Tokyo 1810015 Japan Seoul Natl Univ Dept Phys & Astron Astron Program 1 Gwanak Ro Seoul 08826 South Korea Seoul Natl Univ SNU Astron Res Ctr Astron Program 1 Gwanak Ro Seoul 08826 South Korea Peking Univ Kavli Inst Astron & Astrophys 5 Yiheyuan Rd Beijing 100871 Peoples R China Nicolaus Copernicus Univ Inst Astron Fac Phys Astron & Informat Grudziadzka 5 PL-87100 Torun Poland Adam Mickiewicz Univ Astron Observ Inst Fac Phys ul Sloneczna 36 PL-60286 Poznan Poland St Marys Univ Inst Computat Astrophys Dept Astron & Phys Halifax NS B3H 3C3 Canada Univ British Columbia Dept Phys & Astron Vancouver BC Canada Kyungpook Natl Univ Dept Earth Sci Educ Daegu 41566 South Korea Inst Stat Math Res Ctr Stat Machine Learning 10-3 Midori Cho Tachikawa Tokyo 1908562 Japan Univ Sci & Technol China Dept Astron CAS Key Lab Res Galaxies & Cosmol Hefei 230026 Peoples R China Univ Technol Dept Space Earth & Environm Chalmers Gothenburg Sweden

We present 850 mu m imaging of the XMM-LSS field observed for 170 h as part of the James Clerk Maxwell Telescope SCUBA-2 Large eXtragalactic Survey (S2LXS). S2LXS XMM-LSS maps an area of 9deg(2) reaching a moderate depth of 1 sigma?4mJy beam(-1) 1J?4mJy beam(-1?). This is the largest contiguous area of extragalactic sky mapped by James Clerk Maxwell Telescope (JCMT) at 850 mu m to date. The wide area of the S2LXS XMM-LSS survey allows us to probe the ultra-bright (?S-850 mu m?15mJy ), yet rare submillimetre population. We present the S2LXS XMM-LSS catalogue, which comprises 40 sources detected at > 5 sigma significance, with deboosted flux densities in the range of 77-48mJy48mJy?. We robustly measure the bright-end of the 850 mu m number counts at flux densities > 7mJy > 7mJy?, reducing the Poisson errors compared to existing measurements. The S2LXS XMM-LSS observed number counts show the characteristic upturn at bright fluxes, expected to be motivated by local sources of submillimetre emission and high-redshift strongly lensed galaxies. We find that the observed 850 mu m number counts are best reproduced by model predictions that include either strong lensing or source blending from a 15-arcsec beam, indicating that both may make an important contribution to the observed overabundance of bright single dish 850 mu m selected sources. We make the S2LXS XMM-LSS 850 mu m map and > 5 sigma catalogue presented here publicly available.

关键词： catalogues surveys galaxies:high redshift submillimetre:galaxies

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Genome-wide transcription factor-binding maps reveal cell-specific changes in the regulatory architecture of human HSPCs

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BLOOD 2023年第17期142卷 1448-1462页

作者： Subramanian, Shruthi Thoms, Julie A. I. Huang, Yizhou Cornejo-Paramo, Paola Koch, Forrest C. Jacquelin, Sebastien Shen, Sylvie Song, Emma Joshi, Swapna Brownlee, Chris Woll, Petter S. Chacon-Fajardo, Diego Beck, Dominik Curtis, David J. Yehson, Kenneth Antonenas, Vicki O'Brien, Tracey Trickett, Annette Powell, Jason A. Lewis, Ian D. Pitson, Stuart M. Gandhi, Maher K. Lane, Steven W. Vafaee, Fatemeh Wong, Emily S. Goettgens, Berthold Alinejad-Rokny, Hamid Wong, Jason W. H. Pimanda, John E. Univ New South Wales Sch Clin Med Sydney Australia Univ New South Wales Sch Biomed Sci Sydney Australia Univ Technol Sydney Sch Biomed Engn Ctr Hlth Technol Sydney Australia Victor Chang Cardiac Res Inst Sydney Australia Univ New South Wales Fac Sci Sch Biotechnol & Biomol Sci Sydney Australia Macrophage Biol Lab Mater Res Brisbane Australia Prince Wales Hosp Bone Marrow Transplant Lab NSW Hlth Pathol Randwick NSW Australia Univ New South Wales Mark Wainwright Analyt Ctr Sydney Australia Karolinska Inst Ctr Hematol & Regenerat Med Dept Med Huddinge Sweden Monash Univ Australian Ctr Blood Dis Melbourne Vic Australia NSW Hlth Pathol Blood Transplant & Cell Therapies Lab Westmead NSW Australia Sydney Childrens Hosp Sydney Australia Univ South Australia Ctr Canc Biol SA Pathol Adelaide Australia Univ Adelaide Adelaide Med Sch Adelaide Australia Univ Queensland Blood Canc Res Grp Mater Res Brisbane Qld Australia QIMR Berghofer Med Res Canc Program Brisbane Australia Univ New South Wales UNSW Data Sci Hub Sydney Australia Wellcome MRC Cambridge Stem Cell Inst Cambridge England Univ New South Wales Grad Sch Biomed Engn Biomed Machine Learning Lab Sydney Australia Univ Hong Kong Li Ka Shing Fac Med Sch Biomed Sci Hong Kong Peoples R China Prince Wales Hosp Haematol Dept Sydney Australia Univ New South Wales Lowy Canc Res Ctr Level 2 Sydney NSW 2052 Australia Pokfulam L1-51Lab Block 21 Sassoon Rd Hong Kong Peoples R China

Hematopoietic stem and progenitor cells (HSPCs) rely on a complex interplay among transcription factors (TFs) to regulate differentiation into mature blood cells. A heptad of TFs (FLI1, ERG, GATA2, RUNX1, TAL1, LYL1, LMO2) bind regulatory elements in bulk CD34(+) HSPCs. However, whether specific heptad-TF combinations have distinct roles in regulating hematopoietic differentiation remains unknown. We mapped genome-wide chromatin contacts (HiC, H3K27ac, HiChIP), chromatin modifications (H3K4me3, H3K27ac, H3K27me3) and 10 TF binding profiles (heptad, PU.1, CTCF, STAG2) in HSPC subsets (stem/multipotent progenitors plus common myeloid, granulocyte macrophage, and megakaryocyte erythrocyte progenitors) and found TF occupancy and enhancer-promoter interactions varied significantly across cell types and were associated with cell-type-specific gene expression. Distinct regulatory elements were enriched with specific heptad-TF combinations, including stem-cell-specific elements with ERG, and myeloid-and erythroid-specific elements with combinations of FLI1, RUNX1, GATA2, TAL1, LYL1, and LMO2. Furthermore, heptad-occupied regions in HSPCs were subsequently bound by lineage-defining TFs, including PU.1 and GATA1, suggesting that heptad factors may prime regulatory elements for use in mature cell types. We also found that enhancers with cell-type-specific heptad occupancy shared a common grammar with respect to TF binding motifs, suggesting that combinatorial binding of TF complexes was at least partially regulated by features encoded in DNA sequence motifs. Taken together, this study comprehensively characterizes the gene regulatory landscape in rare subpopulations of human HSPCs. The accompanying data sets should serve as a valuable resource for understanding adult hematopoiesis and a framework for analyzing aberrant regulatory networks in leukemic cells.

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A deep-learning algorithm to classify skin lesions from mpox virus infection

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NATURE MEDICINE 2023年第3期29卷 738-+页

作者： Thieme, Alexander H. H. Zheng, Yuanning Machiraju, Gautam Sadee, Chris Mittermaier, Mirja Gertler, Maximilian Salinas, Jorge L. Srinivasan, Krithika Gyawali, Prashnna Carrillo-Perez, Francisco Capodici, Angelo Uhlig, Maximilian Habenicht, Daniel Loeser, Anastassia Kohler, Maja Schuessler, Maximilian Kaul, David Gollrad, Johannes Ma, Jackie Lippert, Christoph Billick, Kendall Bogoch, Isaac Hernandez-Boussard, Tina Geldsetzer, Pascal Gevaert, Olivier Stanford Univ Dept Med Stanford CA 94305 USA Stanford Univ Stanford Ctr Biomed Informat Res BMIR Dept Biomed Data Sci Stanford CA 94305 USA Charite Univ Med Berlin Dept Radiat Oncol Berlin Germany Charite Univ Med Berlin Berlin Inst Hlth BIH Biomed Innovat Acad BIH Charite Digital Clinician Scientist Program Berlin Germany Stanford Univ Dept Biomed Data Sci Stanford CA USA Charite Univ Med Berlin Dept Infect Dis & Resp Med Berlin Germany Charite Univ Med Berlin Inst Trop Med & Int Hlth Berlin Germany Stanford Univ Dept Med Div Infect Dis & Geog Med Stanford CA USA Univ Granada Dept Architecture & Comp Technol ATC Granada Spain Univ Bologna Dept Biomed & Neuromotor Sci Alma Mater Studiorum Bologna Italy Justus Liebig Univ Giessen Dept Med Giessen Germany Tech Univ Berlin Berlin Germany Univ Med Ctr Schleswig Holstein Dept Radiotherapy Lubeck Germany Heidelberg Univ Hosp Heidelberg Inst Global Hlth Heidelberg Germany Univ Basel Ctr Cognit & Decis Sci Dept Psychol Basel Switzerland Fraunhofer Heinrich Hertz Inst Dept Artificial Intelligence Berlin Germany Univ Potsdam Hasso Plattner Inst Digital Hlth & Machine Learning Potsdam Germany Icahn Sch Med Mt Sinai Hasso Plattner Inst Digital Hlth Mt Sinai New York NY USA Univ Hlth Network Toronto Western Hosp Div Dermatol Toronto ON Canada Univ Hlth Network Toronto Gen Hosp Div Infect Dis Toronto ON Canada Stanford Univ Dept Surg Stanford CA USA Stanford Univ Dept Med Div Primary Care & Populat Hlth Stanford CA USA Chan Zuckerberg Biohub San Francisco CA USA

A deep-learning algorithm was developed to identify skin lesions caused by the mpox virus and was then implemented in a web-based app designed for patient use. Undetected infection and delayed isolation of infected individuals are key factors driving the monkeypox virus (now termed mpox virus or MPXV) outbreak. To enable earlier detection of MPXV infection, we developed an image-based deep convolutional neural network (named MPXV-CNN) for the identification of the characteristic skin lesions caused by MPXV. We assembled a dataset of 139,198 skin lesion images, split into training/validation and testing cohorts, comprising non-MPXV images (n = 138,522) from eight dermatological repositories and MPXV images (n = 676) from the scientific literature, news articles, social media and a prospective cohort of the Stanford University Medical Center (n = 63 images from 12 patients, all male). In the validation and testing cohorts, the sensitivity of the MPXV-CNN was 0.83 and 0.91, the specificity was 0.965 and 0.898 and the area under the curve was 0.967 and 0.966, respectively. In the prospective cohort, the sensitivity was 0.89. The classification performance of the MPXV-CNN was robust across various skin tones and body regions. To facilitate the usage of the algorithm, we developed a web-based app by which the MPXV-CNN can be accessed for patient guidance. The capability of the MPXV-CNN for identifying MPXV lesions has the potential to aid in MPXV outbreak mitigation.

关键词： Public health Viral infection

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Optimal multiparametric set-up modelled for best survival outcomes in palliative treatment of liver malignancies: unsupervised machine learning and 3 PM recommendations

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EPMA JOURNAL 2020年第3期11卷 505-515页

作者： Goldstein, Elisha Yeghiazaryan, Kristina Ahmad, Ashar Giordano, Frank A. Froehlich, Holger Golubnitschaja, Olga Weizmann Inst Sci Dept Bioinformat Machine Learning Res Grp Rehovot Israel Rheinische Friedrich Wilhelms Univ Bonn State NRW Israel Program Bonn Germany Rheinische Friedrich Wilhelms Univ Bonn Univ Hosp Bonn IT Dept Bonn Germany Fraunhofer Inst Algorithms & Sci Comp SCAI Dept Bioinformat AI & Data Sci D-53754 St Augustin Germany Rheinische Friedrich Wilhelms Univ Bonn Bonn Aachen Int Ctr IT D-53115 Bonn Germany Rheinische Friedrich Wilhelms Univ Bonn Univ Hosp Bonn Dept Radiat Oncol Bonn Germany Rheinische Friedrich Wilhelms Univ Bonn Univ Hosp Bonn Dept Radiat Oncol Predict Prevent & Personalised 3P Med Venusberg Campus 1 D-53127 Bonn Germany

Over the last decade, a rapid rise in deaths due to liver disease has been observed especially amongst young people. Nowadays liver disease accounts for approximately 2 million deaths per year worldwide: 1 million due to complications of cirrhosis and 1 million due to viral hepatitis and hepatocellular carcinoma. Besides primary liver malignancies, almost all solid tumours are capable to spread metastases to the liver, in particular, gastrointestinal cancers, breast and genitourinary cancers, lung cancer, melanomas and sarcomas. A big portion of liver malignancies undergo palliative care. To this end, the paradigm of the palliative care in the liver cancer management is evolving from "just end of the life" care to careful evaluation of all aspects relevant for the survivorship. In the presented study, an evidence-based approach has been taken to target molecular pathways and subcellular components for modelling most optimal conditions with the longest survival rates for patients diagnosed with advanced liver malignancies who underwent palliative treatments. We developed an unsupervised machine learning (UML) approach to robustly identify patient subgroups based on estimated survival curves for each individual patient and each individual potential biomarker. UML using consensus hierarchical clustering of biomarker derived risk profiles resulted into 3 stable patient subgroups. There were no significant differences in age, gender, therapy, diagnosis or comorbidities across clusters. Survival times across clusters differed significantly. Furthermore, several of the biomarkers demonstrated highly significant pairwise differences between clusters after correction for multiple testing, namely, "comet assay" patterns of classes I, III, IV and expression rates of calgranulin A (S100), SOD2 and profilin-all measured ex vivo in circulating leucocytes. Considering worst, intermediate and best survival curves with regard to identified clusters and corresponding patterns of para

关键词： Predictive preventive personalised medicine (PPPM 3 PM) Liver diseases Liver malignancy Palliative medicine Unsupervised machine learning Multiparametric modelling Patient stratification Hepatocellular carcinoma Colorectal cancer Breast cancer Prostate cancer Metastasis Liquid biopsy Ex vivo Circulating leucocytes Multi-omics Biomarker patterns Multi-level diagnostics Survival Prognosis Metalloproteinase Comet assay Calgranulin A S100 Catalase Superoxide-dismutase 2 SOD-2 Profilin Rho A Thioredoxin Scavenger Antioxidant compounds ROS inhibition Selective internal radiation therapy (SIRT) Trans-arterial chemo-embolisation (TACE) Individual outcomes Covid-19 Viral infection Hepatitis Hypoxia Inflammation Detoxification Impairments Individualised patient profile Redox status Genoprotection Phytochemicals Redox-based therapy Risk mitigation

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BioAutoMATED: An end-to-end automated machine learning tool for explanation and design of biological sequences

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CELL SYSTEMS 2023年第6期14卷 525-+页

作者： Valeri, Jacqueline A. Soenksen, Luis R. Collins, Katherine M. Ramesh, Pradeep Cai, George Powers, Rani Angenent-Mari, Nicolaas M. Camacho, Diogo M. Wong, Felix Lu, Timothy K. Collins, James J. MIT Dept Biol Engn 77 Massachusetts Ave Cambridge MA 02139 USA MIT Inst Med Engn & Sci 77 Massachusetts Ave Cambridge MA 02139 USA Harvard Univ Wyss Inst Biolog Inspired Engn Boston MA 02115 USA Broad Inst & Harvard Cambridge MA 02142 USA MIT Dept Mech Engn 77 Massachusetts Ave Cambridge MA 02139 USA MIT Dept Elect Engn & Comp Sci 77 Massachusetts Ave Cambridge MA 02139 USA MIT Dept Brain & Cognit Sci 77 Massachusetts Ave Cambridge MA 02139 USA Univ Cambridge Dept Engn Trumpington St Cambridge CB2 1PZ England Pluto Biosci Golden CO 80402 USA MIT Res Lab Elect Synthet Biol Grp Cambridge MA 02139 USA Harvard MIT Program Hlth Sci & Technol Cambridge MA 02139 USA MIT Abdul Latif Jameel Clin Machine Learning Hlth Cambridge MA 02139 USA

The design choices underlying machine-learning (ML) models present important barriers to entry for many biologists who aim to incorporate ML in their research. Automated machine-learning (AutoML) algorithms can address many challenges that come with applying ML to the life sciences. However, these algorithms are rarely used in systems and synthetic biology studies because they typically do not explicitly handle biological sequences (e.g., nucleotide, amino acid, or glycan sequences) and cannot be easily compared with other AutoML algorithms. Here, we present BioAutoMATED, an AutoML platform for biological sequence analysis that integrates multiple AutoML methods into a unified framework. Users are automat-ically provided with relevant techniques for analyzing, interpreting, and designing biological sequences. BioAutoMATED predicts gene regulation, peptide-drug interactions, and glycan annotation, and designs optimized synthetic biology components, revealing salient sequence characteristics. By auto-mating sequence modeling, BioAutoMATED allows life scientists to incorporate ML more readily into their work.

关键词： automated machine learning architecture search hyperparameter optimization biological sequences

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learning the sequence of influenza A genome assembly during viral replication using point process models and fluorescence in situ hybridization

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PLOS COMPUTATIONAL BIOLOGY 2019年第1期15卷 e1006199-e1006199页

作者： Majarian, Timothy D. Murphy, Robert F. Lakdawala, Seema S. Carnegie Mellon Univ Computat Biol Dept Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Biol Sci 4400 5th Ave Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Biomed Engn Pittsburgh PA 15213 USA Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA Univ Pittsburgh Sch Med Dept Microbiol & Mol Genet Pittsburgh PA 15260 USA Broad Inst Program Med & Populat Genet Cambridge MA USA

Within influenza virus infected cells, viral genomic RNA are selectively packed into progeny virions, which predominantly contain a single copy of 8 viral RNA segments. Intersegmental RNA-RNA interactions are thought to mediate selective packaging of each viral ribonucleoprotein complex (vRNP). Clear evidence of a specific interaction network culminating in the full genomic set has yet to be identified. Using multi-color fluorescence in situ hybridization to visualize four vRNP segments within a single cell, we developed image-based models of vRNP-vRNP spatial dependence. These models were used to construct likely sequences of vRNP associations resulting in the full genomic set. Our results support the notion that selective packaging occurs during cytoplasmic transport and identifies the formation of multiple distinct vRNP sub-complexes that likely form as intermediate steps toward full genomic inclusion into a progeny virion. The methods employed demonstrate a statistically driven, model based approach applicable to other interaction and assembly problems.

关键词： INFLUENZA A virus FLUORESCENCE HYBRIDIZATION RNA NUCLEIC acids TRANSCRIPTOMES NUCLEOPROTEINS

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