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Comparison of the ROME-III Criteria and a New Multivariable Prediction Model for the Selection of Patients with Symptomatic Cholecystolithiasis for Cholecystectomy

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HPB 2022年 24卷 S502-S503页

作者： Latenstein, C. Hannink, G. van der Bilt, J. Donkervoort, S. Eijsbouts, Q. Heisterkamp, J. Nieuwenhuijs, V. Schreinemakers, J. Wiering, B. Boermeester, M. Drenth, J. van Laarhoven, C. Dijkgraaf, M. de Reuver, P. Radboudumc Surgery Nijmegen Netherlands Radboudumc Department of Operation Rooms Nijmegen Netherlands Flevoziekenhuis Surgery Almere Netherlands OLVG Surgery Amsterdam Netherlands Spaarne Gasthuis Surgery Haarlem Netherlands ETZ Surgery Tilburg Netherlands Isala Hospital Surgery Zwolle Netherlands Amphia Hospital Surgery Breda Netherlands Slingeland Hospital Surgery Doetinchem Netherlands AmsterdamUMC Surgery Amsterdam Netherlands Radboudumc Gastroenterology and Hepatology Nijmegen Netherlands AmsterdamUMC Department of Epidemiology and Data Science Biostatistics and Bioinformatics Amsterdam Netherlands

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Progression subtypes in Parkinson's disease: Results of an AI-based multi-cohort analysis

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Clinical Neurophysiology 2024年 159卷 e39-e39页

作者： Hähnel, T. Raschka, T. Sapienza, S. Klucken, J. Glaab, E. Corvol, J. Falkenburger, B. Fröhlich, H. Department of Neurology University Hospital and Faculty of Medicine Carl Gustav Carus TUD Dresden University of Technology Dresden Germany Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Sankt Augustin Germany Bonn-Aachen International Center for IT University of Bonn Bonn Germany Biomedical Data Science Luxembourg Centre for Systems Biomedicine (LCSB) University of Luxembourg Esch-sur-Alzette Luxembourg Luxembourg Institute of Health (LIH) Strassen Luxembourg Centre Hospitalier de Luxembourg (CHL) Luxembourg Sorbonne Université Paris Brain Institute – ICM Inserm CNRS Assistance Publique Hôpitaux de Paris Pitié-Salpêtrière Hospital Department of Neurology Paris France German Center for Neurodegenerative Diseases (DZNE) Tatzberg 41 01307 Dresden Germany

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Improved Risk Stratification of Smoldering Multiple Myeloma (SMM) Using Trajectory data in the Pangea 2.0 Model: A Multicenter Study in 1,431 Participants

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Blood 2024年 144卷 1017-1017页

作者： Floris Chabrun Daniel Schwartz Susanna Gentile Tulika R Gupta Noelia Collado Gisbert Esperanza Martín-Sánchez Rosalinda Termini Jacqueline F Perry Annie Cowan Federico Ferrari Samuel Freeman Robert A. Redd Vidhi Patel Patrick Costello Catherine Tobia Romanos Sklavenitis-Pistofidis Habib El-Khoury Michael Timonian David J Lee Elizabeth D. Lightbody Irene Ghobrial Dana-Farber Cancer Institute Boston MA Harvard T.H. Chan School of Public Health Boston MA Cancer Center Clinica Universidad de Navarra (CCUN) Centro de Investigacion Medica Aplicada (CIMA) Instituto de Investigacion Sanitaria de Navarra (IdiSNA) CIBER-ONC numbers CB16/12/00369 and CB16/12/00489 Pamplona Spain Biostatistics and Research Decision Sciences Merck & Co Rahway NJ Bioinformatics Program Broad Institute of MIT and Harvard Cambridge MA Department of Data Science Dana-Farber Cancer Institute Boston MA Broad Institute of MIT and Harvard Cambridge MA University of Chicago Medical Center Chicago IL Harvard Medical School Boston MA

Background The 20/2/20 model is the current gold standard to stratify smoldering multiple myeloma (SMM) patients at baseline into three subgroups (low, intermediate, and high) according to the risk of progression based on the free light chain ratio (FLCr), M-protein concentration, and percentage of bone marrow (BM) plasma cells (PC). Evolving patterns that may alter the risk of progression are not considered in this static model. We previously proposed the PANGEA model that allows for personalized risk prediction using FLCr, M-protein, creatinine, age, hemoglobin trajectory, and optionally BM PC. We developed an improved PANGEA 2.0 model that includes trajectory modeling of these biomarkers to capture evolving patterns and improve predictions of MM progression. Methods We conducted a retrospective review of clinical data from 1,431 participants diagnosed with SMM at 4 international sites (Dana-Farber Cancer Institute, Boston, US, n = 737; National and Kapodistrian University of Athens, Greece, n = 379; University College London, UK, n = 97; and University of Navarra, Spain, n = 218). Dana-Farber participants comprised a training cohort to identify biomarker trajectories and to develop the PANGEA 2.0 model. The model was validated on two international cohorts: validation cohort 1 included patients from Greece and the UK (n = 476) and validation cohort 2 included patients from Spain (n = 218). The longitudinal data collected from 2018-2024 included current values and historical trajectories of age, M-protein, FLCr, creatinine, and hemoglobin, as well as BM PC (optional). We used a systematic grid search with 5-fold cross-validation to determine optimal trajectory definitions for M-protein, FLCr, creatinine, and hemoglobin. For each, we evaluated seven binary trajectory definitions based on average increase over time (slopes) or recent increase from the previous visit on absolute or relative (%) increase scales, with varying thresholds and time periods. We used Cox reg

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SWEET: a single-sample network inference method for deciphering individual features in disease

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Briefings in bioinformatics 2023年第2期24卷 bbad032页

作者： Hsin-Hua Chen Chun-Wei Hsueh Chia-Hwa Lee Ting-Yi Hao Tzu-Ying Tu Lan-Yun Chang Jih-Chin Lee Chun-Yu Lin Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu 300 Taiwan. School of Medical Laboratory Science and Biotechnology College of Medical Science and Technology Taipei Medical University Taipei 110 Taiwan. TMU Research Center of Cancer Translational Medicine Taipei Medical University Taipei 110 Taiwan. Ph.D. Program in Medical Biotechnology College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Department of Otolaryngology-Head and Neck Surgery Tri-Service General Hospital National Defense Medical Center Taipei 110 Taiwan. Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu 300 Taiwan. Institute of Data Science and Engineering National Yang Ming Chiao Tung University Hsinchu 300 Taiwan. Center for Intelligent Drug Systems and Smart Bio-devices National Yang Ming Chiao Tung University Hsinchu 300 Taiwan. School of Dentistry Kaohsiung Medical University Kaohsiung 807 Taiwan.

Recently, extracting inherent biological system information (e.g. cellular networks) from genome-wide expression profiles for developing personalized diagnostic and therapeutic strategies has become increasingly important. However, accurately constructing single-sample networks (SINs) to capture individual characteristics and heterogeneity in disease remains challenging. Here, we propose a sample-specific-weighted correlation network (SWEET) method to model SINs by integrating the genome-wide sample-to-sample correlation (i.e. sample weights) with the differential network between perturbed and aggregate networks. For a group of samples, the genome-wide sample weights can be assessed without prior knowledge of intrinsic subpopulations to address the network edge number bias caused by sample size differences. Compared with the state-of-the-art SIN inference methods, the SWEET SINs in 16 cancers more likely fit the scale-free property, display higher overlap with the human interactomes and perform better in identifying three types of cancer-related genes. Moreover, integrating SWEET SINs with a network proximity measure facilitates characterizing individual features and therapy in diseases, such as somatic mutation, mut-driver and essential genes. Biological experiments further validated two candidate repurposable drugs, albendazole for head and neck squamous cell carcinoma (HNSCC) and lung adenocarcinoma (LUAD) and encorafenib for HNSCC. By applying SWEET, we also identified two possible LUAD subtypes that exhibit distinct clinical features and molecular mechanisms. Overall, the SWEET method complements current SIN inference and analysis methods and presents a view of biological systems at the network level to offer numerous clues for further investigation and clinical translation in network medicine and precision medicine.

关键词： drug repurposing gene expression network inference network medicine precision medicine single-sample network

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Defining autocatalysis in chemical reaction networks

arXiv

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arXiv 2021年

作者： Andersen, Jakob Lykke Flamm, Christoph Merkle, Daniel Stadler, Peter F. Department of Mathematics and Computer Science University of Southern Denmark Campusvej 55 Odense MDK-5230 Denmark Institute for Theoretical Chemistry University of Vienna Währingerstraße 17 WienA-1090 Austria Bioinformatics Group Department of Computer Science University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Interdisciplinary Center for Bioinformatics University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Halle-Jena-Leipzig University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Competence Center for Scalable Data Services and Solutions Dresden-Leipzig University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Leipzig Research Center for Civilization Diseases University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Centre for Biotechnology and Biomedicine University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Facultad de Ciencias Universidad Nacional de Colombia Sede Bogotá Colombia Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States

Autocatalysis is a deceptively simple concept, referring to the situation that a chemical species X catalyzes its own formation. From the perspective of chemical kinetics, autocatalysts show a regime of superlinear growth. Given a chemical reaction network, however, it is not at all straightforward to identify species that are autocatalytic in the sense that there is a sub-network that takes X as input and produces more than one copy of X as output. The difficulty arises from the need to distinguish autocatalysis e.g. from the superposition of a cycle that consumes and produces equal amounts of X and a pathway that produces X. To deal with this issue, a number of competing notions, such as exclusive autocatalysis and autocatalytic cycles, have been introduced. A closer inspection of concepts and their usage by different authors shows, however, that subtle differences in the definitions often makes conceptually matching ideas difficult to bring together formally. In this contribution we make some of the available approaches comparable by translating them into a common formal framework that uses integer hyperflows as a basis to study autocatalysis in large chemical reaction networks. As an application we investigate the prevalence of autocatalysis in metabolic networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Growth kinetics

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Perceptual and technical barriers in sharing and formatting metadata accompanying omics studies

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Cell Genomics 2025年第5期5卷 100845-100845页

作者： Huang, Yu-Ning Munteanu, Viorel Love, Michael I. Ronkowski, Cynthia Flaire Deshpande, Dhrithi Wong-Beringer, Annie Corbett-Detig, Russell Dimian, Mihai Moore, Jason H. Garmire, Lana X. Reddy, T.B.K. Butte, Atul J. Robinson, Mark D. Eskin, Eleazar Abedalthagafi, Malak S. Mangul, Serghei Department of Clinical Pharmacy Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences University of Southern California Los Angeles 90089 CA United States Department of Computers Informatics and Microelectronics Technical University of Moldova Chisinau 2045 Moldova Department of Biological and Morphofunctional Sciences College of Medicine and Biological Sciences Stefan cel Mare University of Suceava Suceava 720229 Romania Department of Genetics University of North Carolina at Chapel Hill Chapel Hill 27514 NC United States Department of Biostatistics University of North Carolina at Chapel Hill Chapel Hill 27514 NC United States Department of Biomolecular Engineering University of California Santa Cruz Santa Cruz 95064 CA United States Genomics Institute University of California Santa Cruz Santa Cruz 95064 CA United States Department of Computers Electronics and Automation Stefan cel Mare University of Suceava Suceava 720229 Romania Department of Computational Biomedicine Cedars-Sinai Medical Center Los Angeles 90069 CA United States Department of Computational Medicine and Bioinformatics University of Michigan Medical School Ann Arbor 48105 MI United States US Department of Energy Joint Genome Institute Lawrence Berkeley National Laboratory Berkeley 94720 CA United States Bakar Computational Health Sciences Institute University of California San Francisco (UCSF) San Francisco 94143 CA United States Center for Data-Driven Insights and Innovation University of California Oakland Oakland 94607 CA United States SIB Swiss Institute of Bioinformatics and Department of Molecular Life Sciences University of Zurich Zurich 8057 Switzerland Department of Computer Science University of California Los Angeles Los Angeles CA United States Department of Computational Medicine University of California Los Angeles Los Angeles CA United States Department of Human Genetics University of California Los Angeles Los Angeles CA United Stat

Metadata, or “data about data,” is essential for organizing, understanding, and managing large-scale omics datasets. It enhances data discovery, integration, and interpretation, enabling reproducibility, reusability, and secondary analysis. However, metadata sharing remains hindered by perceptual and technical barriers, including the lack of uniform standards, privacy concerns, study design limitations, insufficient incentives, inadequate infrastructure, and a shortage of trained personnel. These challenges compromise data reliability and obstruct integrative meta-analyses. Addressing these issues requires standardization, education, stronger roles for journals and funding agencies, and improved incentives and infrastructure. Looking ahead, emerging technologies such as artificial intelligence and machine learning may offer promising solutions to automate metadata processes, increasing accuracy and scalability. Fostering a collaborative culture of metadata sharing will maximize the value of omics data, accelerating innovation and scientific discovery. © 2025 The Authors

关键词： barriers in metadata sharing practices data metadata metadata completeness

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A Model of Visual Intelligent System for Genus Identification of Fish in the Siluriformes Order

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IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Taufik Budhi Pramono R Ardharyan Islamy Saprudin Joni Johanda Putra Teddy Suparyanto Kartika Purwandari Tjeng Wawan Cenggoro Bens Pardamean Aquaculture Study Program Faculty of Fisheries and Marine Science Jenderal Soedirman University Purwokerto Central Java Indonesia 53123 Aquatic Resources Management Brawijaya University Malang East Java Indonesia Graduate Program Aquatic Resources Faculty of Fisheries and Marine Science Jenderal Soedirman University Purwokerto Central Java Indonesia 53123 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Ambiguity in the fish naming is present in several fish species database, especially for fish in Siluformes order. To fix the ambiguity, a visual intelligent system is needed to automate the fish naming correction in the database. In this study, we developed a deep-learning-based model as the core of the intelligent system. The proposed model achieved 89% accuracy for the classification of three genera in Siluformes order: Mystus, Hemibagrus, and Glyptothorax.

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Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection

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Computational intelligence and neuroscience 2023年第1期2023卷 4776770页

作者： Sujatha Krishnamoorthy Yaxi Zhang Seifedine Kadry Muhammad Attique Khan Majed Alhaisoni Nasser Mustafa Weifeng Yu Abdullah Alqahtani Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Wenzhou Zhejiang Province China. Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Wenzhou Zhejiang Province China. Department of Neurology Wenzhou Central Hospital Medical Group Wenzhou 325000 China. Department of Applied Data Science Noroff University College Kristiansand Norway. Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 UAE. Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon. Department of Computer Science HITEC University Taxila Pakistan. Computer Sciences Department College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh 11671 Saudi Arabia. Wenzhou-Kean University School of Science and Technology Wenzhou Zhejiang Province China. College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia.

Malfunctions in the immune system cause multiple sclerosis (MS), which initiates mild to severe nerve damage. MS will disturb the signal communication between the brain and other body parts, and early diagnosis will help reduce the harshness of MS in humankind. Magnetic resonance imaging (MRI) supported MS detection is a standard clinical procedure in which the bio-image recorded with a chosen modality is considered to assess the severity of the disease. The proposed research aims to implement a convolutional neural network (CNN) supported scheme to detect MS lesions in the chosen brain MRI slices. The stages of this framework include (i) image collection and resizing, (ii) deep feature mining, (iii) hand-crafted feature mining, (iii) feature optimization with firefly algorithm, and (iv) serial feature integration and classification. In this work, five-fold cross-validation is executed, and the final result is considered for the assessment. The brain MRI slices with/without the skull section are examined separately, presenting the attained results. The experimental outcome of this study confirms that the VGG16 with random forest (RF) classifier offered a classification accuracy of >98% MRI with skull, and VGG16 with K-nearest neighbor (KNN) provided an accuracy of >98% without the skull.

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Subgrouping of Burkitt lymphoma variants by DNA methylation is driven by an EBV-associated epigenotype

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Hematological Oncology 2023年第S2期41卷

作者： S. Glaser C. Del Val R. Wagener H. Kretzmer W. Klapper M. Hummel O. Ammerpohl P. Lichter B. Burkhardt L. Trümper S. Mbulaiteye Institute of Human Genetics Ulm University and Ulm University Medical Center Ulm Germany Department of Computer Science and Artifical Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) Universtiy of Granada Granada Spain Transciptome Bioinformatics Group LIFE Research Center for Civilization Diseases University of Leipzig Leipzig Germany Department of Pathology University of Kiel/University Hospital Schleswig-Holstein Kiel Germany Institute of Pathology Charité – University Medicine Berlin Berlin Germany Division of Molecular Genetics German Cancer Research Center Heidelberg Germany Department of Pediatric Hematology and Oncology University Hospital Münster Münster Germany Hematology and Oncology University of Göttingen Göttingen Germany Division of Cancer Epidemiology and Genetics National Cancer Institute National Institutes of Health Bethesda MC USA

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Radiogenomics Consortium Genome-Wide Association Study Meta-Analysis of Late Toxicity After Prostate Cancer Radiotherapy (vol 112, pg 179, 2020)

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JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE 2020年第6期112卷 654-654页

作者： Kerns, Sarah L. Fachal, Laura Dorling, Leila Barnett, Gillian C. Baran, Andrea Peterson, Derick R. Hollenberg, Michelle Hao, Ke Di Narzo, Antonio Ahsen, Mehmet Eren Pandey, Gaurav Bentzen, Soren M. Janelsins, Michelle Ellicot, Rebecca M. Pharoah, Paul D. Burnet, Neil G. Dearnaley, David P. Gulliford, Sarah L. Hall, Emma Sydes, Matthew R. Aguado-Barrera, Miguel E. Gomez-Caamano, Antonio Carballo, Ana M. Peleteiro, Paula Lobato-Busto, Ramon Stock, Richard Stone, Nelson N. Ostrer, Harry Usmani, Nawaid Singhal, Sandeep Tsuji, Hiroshi Imai, Takashi Saito, Shiro Eeles, Rosalind DeRuyck, Kim Parliament, Matthew Dunning, Alison M. Vega, Ana Rosenstein, Barry S. West, Catharine M. L. Departments of Radiation Oncology and Surgery University of Rochester Medical Center Rochester NY Department of Oncology Department of Public Health and Primary Care Department of Public Health and Primary Care Centre for Cancer Genetic Epidemiology Strangeways Research Laboratory University of Cambridge Cambridge UK Department of Oncology Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Biostatistics and Computational Biology University of Rochester Medical Center Rochester NY Department of Biostatistics and Computational Biology University of Rochester Medical Center Rochester NY Department of Computational Biology University of Rochester Rochester NY Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology Icahn School of Medicine at Mount Sinai New York NY Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology Icahn School of Medicine at Mount Sinai New York NY Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology Icahn School of Medicine at Mount Sinai New York NY Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology Icahn School of Medicine at Mount Sinai New York NY Division of Biostatistics and Bioinformatics Department of Epidemiology and Public Health University of Maryland Greenebaum Cancer Center School of Medicine University of Maryland Baltimore Departments of Radiation Oncology and Surgery University of Rochester Medical Center Rochester NY Division of Cancer Sciences the University of Manchester Manchester Academic Health Science Centre Christie Hospital Manchester UK Centre for Cancer Genetic Epidemiology Strangeways Research Laboratory University of Cambridge Cambridge UK Department of Oncology Cambridge University Hospitals NHS Foundation Trust Cambridge UK Division of Cancer Sciences the University of Manchester Manches

Background A total of 10%-20% of patients develop long-term toxicity following radiotherapy for prostate cancer. Identification of common genetic variants associated with susceptibility to radiotoxicity might improve risk prediction and inform functional mechanistic *** We conducted an individual patient data meta-analysis of six genome-wide association studies (n = 3871) in men of European ancestry who underwent radiotherapy for prostate cancer. Radiotoxicities (increased urinary frequency, decreased urinary stream, hematuria, rectal bleeding) were graded prospectively. We used grouped relative risk models to test associations with approximately 6 million genotyped or imputed variants (time to first grade 2 or higher toxicity event). Variants with two-sided Pmeta less than 5 × 10−8 were considered statistically significant. Bayesian false discovery probability provided an additional measure of confidence. Statistically significant variants were evaluated in three Japanese cohorts (n = 962). All statistical tests were *** Meta-analysis of the European ancestry cohorts identified three genomic signals: single nucleotide polymorphism rs17055178 with rectal bleeding (Pmeta = 6.2 × 10−10), rs10969913 with decreased urinary stream (Pmeta = 2.9 × 10−10), and rs11122573 with hematuria (Pmeta = 1.8 × 10−8). Fine-scale mapping of these three regions was used to identify another independent signal (rs147121532) associated with hematuria (Pconditional = 4.7 × 10−6). Credible causal variants at these four signals lie in gene-regulatory regions, some modulating expression of nearby genes. Previously identified variants showed consistent associations (rs17599026 with increased urinary frequency, rs7720298 with decreased urinary stream, rs1801516 with overall toxicity) in new cohorts. rs10969913 and rs17599026 had similar effects in the photon-treated Japanese *** This study increases the understanding of the architecture of common gen

关键词： radiation therapy hematuria genes genome single nucleotide polymorphism urinary tract genetics prostate cancer rectal bleeding urinary frequency toxic effect genome-wide association study japanese imputation

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