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iTReX: Interactive exploration of mono- and combination therapy dose response profiling data

Pharmacological Research 2022年 175卷 105996-105996页

作者： ElHarouni, Dina Berker, Yannick Peterziel, Heike Gopisetty, Apurva Turunen, Laura Kreth, Sina Stainczyk, Sabine A. Oehme, Ina Pietiäinen, Vilja Jäger, Natalie Witt, Olaf Schlesner, Matthias Oppermann, Sina Bioinformatics and Omics Data Analytics German Cancer Research Center (DKFZ) Heidelberg Germany Hopp Children's Cancer Center (KiTZ) Heidelberg Germany Division of Pediatric Neurooncology German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Clinical Cooperation Unit Pediatric Oncology German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Institute for Molecular Medicine Finland (FIMM) Helsinki Institute of Life Science (HiLIFE) University of Helsinki Helsinki Finland Division of Neuroblastoma Genomics German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Department of Pediatric Oncology Hematology Immunology and Pulmonology Heidelberg University Hospital Heidelberg Germany Biomedical Informatics Data Mining and Data Analytics Faculty of Applied Computer Science and Medical Faculty University of Augsburg Augsburg Germany

High throughput screening methods, measuring the sensitivity and resistance of tumor cells to drug treatments have been rapidly evolving. Not only do these screens allow correlating response profiles to tumor genomic features for developing novel predictors of treatment response, but they can also add evidence for therapy decision making in precision oncology. Recent analysis methods developed for either assessing single agents or combination drug efficacies enable quantification of dose-response curves with restricted symmetric fit settings. Here, we introduce iTReX, a user-friendly and interactive Shiny/R application, for both the analysis of mono- and combination therapy responses. The application features an extended version of the drug sensitivity score (DSS) based on the integral of an advanced five-parameter dose-response curve model and a differential DSS for combination therapy profiling. Additionally, iTReX includes modules that visualize drug target interaction networks and support the detection of matches between top therapy hits and the sample omics features to enable the identification of druggable targets and biomarkers. iTReX enables the analysis of various quantitative drug or therapy response readouts (e.g. luminescence, fluorescence microscopy) and multiple treatment strategies (drug treatments, radiation). Using iTReX we validate a cost-effective drug combination screening approach and reveal the application's ability to identify potential sample-specific biomarkers based on drug target interaction networks. The iTReX web application is accessible at https://*** © 2021 The Authors

关键词： Asymmetric sensitivity scoring Differential combination sensitivity scoring Drug target networks Interactive drug screen analysis Personalized medicine Therapy response profiling and exploration

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Surveying public opinion using label prediction on social media data

Surveying public opinion using label prediction on social me...

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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Marija Stanojevic Jumanah Alshehri Zoran Obradovic Center for Data Analytics and Biomedical Informatics Temple University Philadelphia PA USA

ISBN: (数字)9781450368681

ISBN: (纸本)9781450368681

In this study, a procedure is proposed for surveying public opinion from big social media domain-specific textual data to minimize the difficulties associated with modeling public behavior. Strategies for labeling posts relevant to a topic are discussed. A two-part framework is proposed in which semiautomatic labeling is applied to a small subset of posts, referred to as the “seed” in further text. This seed is used as bases for semi-supervised labeling of the rest of the data. The hypothesis is that the proposed method will achieve better labeling performance than existing classification models when applied to small amounts of labeled data. The seed is labeled using posts of users with a known and consistent view on the topic. A semi-supervised multi-class prediction model labels the remaining data iteratively. In each iteration, it adds context-label pairs to the training set if softmax-based label probabilities are above the threshold. The proposed method is characterized on four datasets by comparison to the three popular text modeling algorithms (n-grams + tfidf, fastText, VDCNN) for different sizes of labeled seeds (5,000 and 50,000 posts) and for several label-prediction significance thresholds. Our proposed semi-supervised method outperformed alternative algorithms by capturing additional contexts from the unlabeled data. The accuracy of the algorithm was increasing by (3-10%) when using a larger fraction of data as the seed. For the smaller seed, lower label probability threshold was clearly a better choice, while for larger seeds no predominant threshold was observed. The proposed framework, using fastText library for efficient text classification and representation learning, achieved the best results for a smaller seed, while VDCNN wrapped in the proposed framework achieved the best results for the bigger seed. The performance was negatively influenced by the number of classes. Finally, the model was applied to characterize a biased dataset of opinions re

关键词： Labeling Social network services data models Predictive models Mathematical model Training Organizations

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Comorbidity network analysis and genetics of colorectal cancer

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informatics in Medicine Unlocked 2020年 21卷

作者： Ljubic, Branimir Pavlovski, Martin Alshehri, Jumanah Roychoudhury, Shoumik Bajic, Vladimir Van Neste, Christophe Obradovic, Zoran Temple University Center for Data Analytics and Biomedical Informatics (DABI) Philadelphia PA United States King Abdullah University of Science and Technology Computational Bioscience Research Center Thuwal Saudi Arabia Center for Medical Genetics (CMGG) Ghent University Ghent Belgium

Background: Colorectal cancer (CRC) is the third most common cancer in the United States and the second leading cause of cancer death. The goal was to identify comorbidities and genes associated with CRC. Methods: A novel social network model was developed on the Healthcare Cost and Utilization Project (HCUP) - State Inpatient databases (SID) California database to study comorbidities of CRC. Ranked lists of comorbidities and comorbidity networks were created, and the prevalence of comorbidities in different stages of CRC was calculated. Ranked lists of comorbidities were utilized for text mining of PubMed and DisGeNET to extract genes associated with CRC. Results: 5,786 comorbidities were identified in females and 5,607 in males in early stages and 5,609 comorbidities in females and 5,427 in males in advanced stages of CRC. Associations between 1,937 different genes and CRC were extracted from PubMed. 150 genes are associated with CRC in DisGeNET. The most mentioned genes associated with CRC were: TP53 (241 abstracts in PubMed), APC (115), and KRAS (106). These 3 genes as well as MLH1 (98) and TGFBR2 (18) had DisGeNET scores of 0.5. PPARG gene (43) had DisGeNET score of 0.6. Conclusions: The results of comorbidity network analyses suggest which comorbidities of CRC are highly expected. Discovered genes could be used to recruit more individuals who would benefit from genetic consultations. Identified associations between comorbidities, CRC, and shared genes can have important implications on early discovery, and prognosis of CRC. Prevention and treatment of discovered comorbidities would potentially lead to improved quality of life and better outcomes of CRC. © 2020 The Authors

关键词： biomedical informatics Colorectal cancer Comorbidities Genes Social networks

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Genes and comorbidities of thyroid cancer

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informatics in Medicine Unlocked 2021年 25卷

作者： Ljubic, Branimir Pavlovski, Martin Roychoudhury, Shoumik Van Neste, Christophe Salhi, Adil Essack, Magbubah Bajic, Vladimir B. Obradovic, Zoran Temple University Center for Data Analytics and Biomedical Informatics (DABI) Philadelphia 19122 PA United States Rutgers University the Office of Advanced Research Computing (OARC) Piscataway 08854 NJ United States Center for Medical Genetics (CMGG) Ghent University Ghent Belgium King Abdullah University of Science and Technology (KAUST) Computational Bioscience Research Center (CBRC) Computer Electrical and Mathematical Science and Engineering (CEMSE) Division Thuwal Saudi Arabia

Introduction: Thyroid cancer represents 3.1 % of diagnosed cancers in the United States. The objective of this research was to identify comorbidities and discover additional genes potentially related to thyroid cancer and improve current knowledge of genetics and comorbidities associated with this cancer. Methods: Healthcare Cost and Utilization Project (HCUP) California State Inpatient database (SID) was used to extract and rank the comorbidities of thyroid cancer. The text mining software - BeFree was utilized to identify and extract genes associated with thyroid cancer and the comorbidities from PubMed abstracts and the DisGeNET expert-curated repositories. Results: Female patients had 4,485, and male patients 2912 different comorbidities in early stages of thyroid cancer. Females had 3,587, and males 2817 different comorbidities in advanced stages. Through PubMed utilizing the BeFree method, 504 different genes associated with thyroid cancer were discovered, as well as five genes on DisGeNET. The most often genes on PubMed, associated with thyroid cancer were: BRAF, RET, SLC5A5, RAS, and PTEN. Genes found via DisGeNET were BRAF, RET, KRAS, NRAS, and PRKAR1A. Conclusion: Identified genes and comorbidities, as potential additional risk factors for thyroid cancer, not previously known, could improve the early diagnosis and the survival of patients with thyroid cancer. Genes discovered in this research in association with thyroid cancer could be used to direct decision making for optimal, more personalized treatment of thyroid cancer. © 2021

关键词： biomedical informatics Comorbidities DisGeNET Genes Text mining Thyroid cancer

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A phenome-wide association study of uterine fibroids reveals a marked burden of comorbidities

Communications Medicine

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Communications Medicine 2025年第1期5卷 1-15页

作者： Jasper, Elizabeth A. Mautz, Brian S. Hellwege, Jacklyn N. Piekos, Jacqueline A. Jones, Sarah H. Zhang, Yanfei Torstenson, Eric S. Pendergrass, Sarah A. Lee, Ming Ta Michael Edwards, Todd L. Velez Edwards, Digna R. Division of Quantitative and Clinical Sciences Department of Obstetrics and Gynecology Vanderbilt University Medical Center Nashville TN United States Center for Precision Medicine Vanderbilt University Medical Center Nashville TN United States Institute for Medicine and Public Health Vanderbilt University Medical Center Nashville TN United States Vanderbilt Epidemiology Center Vanderbilt University Nashville TN United States Vanderbilt Genetics Institute Vanderbilt University Nashville TN United States Population Analytics Analytics & Insights Data Sciences Janssen Research & Development Spring House PA United States Division of Genetic Medicine Department of Medicine Vanderbilt University Medical Center Nashville TN United States Genomic Medicine Institute Geisinger Health Systems Danville PA United States Department of Biomedical Informatics Vanderbilt University Medical Center Nashville TN United States Division of Epidemiology Department of Medicine Vanderbilt University Medical Center Nashville TN United States Genentech South San Francisco CA United States Department of Biomedical and Translational Informatics Geisinger Rockville MD United States

Background: The burden of comorbidities in those with uterine fibroids compared to those without fibroids is understudied. We performed a phenome-wide association study to systematically assess the association between fibroids and other conditions. Methods: Vanderbilt University Medical center’s Synthetic Derivative and Geisinger Health System database, two electronic health record databases, were used for discovery and validation. Non-Hispanic Black and White females were included. Fibroid cases were identified through a previously validated algorithm. Race-stratified and multi-population phenome-wide association analyses, adjusting for age and body mass index, were performed before statistically significant, validated results were meta-analyzed. Results: There were 52,295 and 26,918 (9022 and 10,232 fibroid cases) females included in discovery and validation analyses. In multi-population meta-analysis, 389 conditions were associated with fibroid risk, with evidence of enrichment of circulatory, dermatologic, genitourinary, musculoskeletal, and sense organ conditions. The strongest associations within and across racial groups included conditions previously associated with fibroids. Numerous novel diagnoses, including cancers in female genital organs, were tied to fibroid status. Conclusions: Overall, individuals with fibroids have a marked increase in comorbidities compared to those without fibroids. This approach to evaluate the health context of fibroids highlights the potential to understand fibroid etiology through studying the common biology of comorbid diagnoses and through disease networks. © The Author(s) 2025.

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Novel genomic loci shape two neuroanatomical dimensions of AD in non-demented populations

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Alzheimer's & Dementia 2023年第S10期19卷

作者： Junhao Wen Zhijian Yang Ioanna Skampardoni Marilena De Pian Guray Erus Elizabeth Mamourian Yuhan Cui Bingxin Zhao Ilya M. Nasrallah Arthur W. Toga Haochang Shou Li Shen Christos Davatzikos Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA University of Pennsylvania philadelphia PA USA Upenn Philadelphia PA USA University of Pennsylvania Philadelphia PA USA Laboratory of Neuro Imaging (LONI) University of Southern California Los Angeles CA USA Department of Biostatistics Epidemiology & Informatics University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA Greece

Background Artificial intelligence has been in burgeoning demand to be applied to magnetic imaging resonance (MRI) data to parse the neuroanatomical heterogeneity of Alzheimer’s disease (AD) and/or mild cognitive impairment (MCI) (Wen et al., 2021a, 2021b; Yang et al., 2021). However, the genetic heterogeneity leading to this neuroanatomical heterogeneity, especially at the early asymptomatic stages, is not fully elucidated. Method A deep semi-supervised representation learning method, called Surreal-GAN (Yang et al., 2022), was applied to T1-weighted MRI from AD/MCI patients from ADNI to define the two neuroanatomical dimensions of AD at the symptomatic stages. The R1 dimension was characterized by widespread brain atrophy without an exclusive focus on the medial temporal lobe, whereas the R2 dimension showed focal medial temporal lobe atrophy (Wen et al., 2022). We then applied the trained model to 33,540 non-demented individuals from UK Biobank. Imputed genotyping data were analyzed in genome-wide association studies (GWAS). We performed multiple linear regressions controlling for confounding factors (e.g., age) via Plink (Purcell et al., 2007). We defined a genomic locus (represented by the top lead SNP) considering linkage disequilibrium. Finally, we manually queried all leading SNPs on GWAS Catalog (Buniello et al., 2019) to determine if the genomic loci were novel, provided they were not previously associated with any clinical traits. Result GWAS discovered 24 genomic loci, of which 7 were novel for R1 and R2, respectively (Fig. 1). For R1, 8 mapped genes, based on physical positions, eQTL, and chromatin interactions, were associated with AD-related traits in the literature. For example, the KDM2B gene from the genomic locus (rs9795600, P-value = 1.13e-08) was previously associated with AD (Gouveia et al., 2022). For R2, 13 mapped genes were associated with AD-related traits. For example, the TESC gene from the genomic locus (rs77956314, P-value = 9.99e-29) w

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P18.04.B SINGLE CELL TRANSCRIPTOMIC ANALYSIS IDENTIFIES DISTINCT INTRATUMORAL SUBCLONES IN MENINGIOMA

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Neuro-Oncology 2023年第SUPPLEMENT_2期25卷 ii122–ii122页

作者： Blume, C Tang, Y Dogan, H Ritter, M Dentro, S von Deimling, A Gerstung, M Schlesner, M Sahm, F Maas, S L N University Hospital Heidelberg Dept. of Neuropathology Heidelberg Germany German Cancer Research Center (DKFZ) German Consortium for Translational Cancer Research (DKTK) Clinical Cooperation Unit Neuropathology Heidelberg Germany Division of AI in Oncology German Cancer Research Center (DKFZ) Heidelberg Germany European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Hinxton United Kingdom Biomedical Informatics Data Mining and Data Analytics Faculty of Applied Computer Science and Medical Faculty University of Augsburg Augsburg Germany Department of Pathology Leiden University Medical Center Leiden Netherlands Department of Pathology Erasmus Medical Center Rotterdam Netherlands

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Correction: Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study

JMIR aging

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JMIR aging 2025年 8卷 e75690页

作者： Natthanaphop Isaradech Wachiranun Sirikul Nida Buawangpong Penprapa Siviroj Amornphat Kitro Department of Community Medicine Faculty of Medicine Chiang Mai University 110 Intrawarorot Road Meaung 50200 Thailand 66 53935472 66 935476. Environmental and Occupational Medicine Excellence Center Faculty of Medicine Chiang Mai University Chiang Mai Thailand. Center of Data Analytics and Knowledge Synthesis for Health Care Faculty of Medicine Chiang Mai University Chiang Mai Thailand. Department of Biomedical Informatics and Clinical Epidemiology Faculty of Medicine Chiang Mai University Chiang Mai Thailand. Department of Family Medicine Faculty of Medicine Chiang Mai University Chiang Mai Thailand.

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Brugada syndrome in Japan and Europe: a genome-wide association study reveals shared genetic architecture and new risk loci

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European heart journal 2024年第26期45卷 2320-2332页

作者： Taisuke Ishikawa Tatsuo Masuda Tsuyoshi Hachiya Christian Dina Floriane Simonet Yuki Nagata Michael W T Tanck Kyuto Sonehara Charlotte Glinge Rafik Tadros Apichai Khongphatthanayothin Tzu-Pin Lu Chihiro Higuchi Tadashi Nakajima Kenshi Hayashi Yoshiyasu Aizawa Yukiko Nakano Akihiko Nogami Hiroshi Morita Seiko Ohno Takeshi Aiba Christian Krijger Juárez John Mauleekoonphairoj Yong Poovorawan Jean-Baptiste Gourraud Wataru Shimizu Vincent Probst Minoru Horie Arthur A M Wilde Richard Redon Jyh-Ming Jimmy Juang Koonlawee Nademanee Connie R Bezzina Julien Barc Toshihiro Tanaka Yukinori Okada Jean-Jacques Schott Naomasa Makita Omics Research Center National Cerebral and Cardiovascular Center Suita Japan. Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan. StemRIM Institute of Regeneration-Inducing Medicine Osaka University Graduate School of Medicine Suita Japan. Department of Obstetrics and Gynecology Osaka University Graduate School of Medicine Suita Japan. Institute for Biomedical Sciences Iwate Medical University Iwate Japan. L'institut du thorax Nantes Université CHU Nantes CNRS INSERM Nantes France. Bioresource Research Center Tokyo Medical and Dental University Tokyo Japan. Department of Human Genetics and Disease Diversity Tokyo Medical and Dental University Tokyo Japan. Epidemiology and Data Science Amsterdam UMC location University of Amsterdam Amsterdam The Netherlands. Department of Genome Informatics Graduate School of Medicine the University of Tokyo Tokyo Japan. Department of Experimental Cardiology Amsterdam UMC location University of Amsterdam Heart Centre Amsterdam The Netherlands. Heart Failure & Arrhythmias Amsterdam Cardiovascular Sciences Amsterdam The Netherlands. Department of Cardiology The Heart Centre Copenhagen University Hospital Rigshospitalet Copenhagen Denmark. European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart (ERN GUARD-HEART https://guardheart.ern-net.eu). Montreal Heart Institute Universite de Montreal Cardiovascular Genetics Centre Montreal Quebec Canada. Department of Medicine Center of Excellence in Arrhythmia Research Faculty of Medicine Chulalongkorn University Bangkok Thailand. Department of Pediatrics Division of Cardiology Faculty of Medicine Chulalongkorn University Bangkok Thailand. Department of Cardiology Bangkok Hospital Bangkok Thailand. Department of Public Health Institute of Health Data Analytics and Statistics National Taiwan University Taipei Taiwan. Artificial Intelligence Center for Health and Biomedical Research National Institutes of Bio

BACKGROUND AND AIMS:Brugada syndrome (BrS) is an inherited arrhythmia with a higher disease prevalence and more lethal arrhythmic events in Asians than in Europeans. Genome-wide association studies (GWAS) have revealed its polygenic architecture mainly in European populations. The aim of this study was to identify novel BrS-associated loci and to compare allelic effects across ancestries. METHODS:A GWAS was conducted in Japanese participants, involving 940 cases and 1634 controls, followed by a cross-ancestry meta-analysis of Japanese and European GWAS (total of 3760 cases and 11 635 controls). The novel loci were characterized by fine-mapping, gene expression, and splicing quantitative trait associations in the human heart. RESULTS:The Japanese-specific GWAS identified one novel locus near ZSCAN20 (P = 1.0 × 10-8), and the cross-ancestry meta-analysis identified 17 association signals, including six novel loci. The effect directions of the 17 lead variants were consistent (94.1%; P for sign test = 2.7 × 10-4), and their allelic effects were highly correlated across ancestries (Pearson's R = .91; P = 2.9 × 10-7). The genetic risk score derived from the BrS GWAS of European ancestry was significantly associated with the risk of BrS in the Japanese population [odds ratio 2.12 (95% confidence interval 1.94-2.31); P = 1.2 × 10-61], suggesting a shared genetic architecture across ancestries. Functional characterization revealed that a lead variant in CAMK2D promotes alternative splicing, resulting in an isoform switch of calmodulin kinase II-δ, favouring a pro-inflammatory/pro-death pathway. CONCLUSIONS:This study demonstrates novel susceptibility loci implicating potentially novel pathogenesis underlying BrS. Despite differences in clinical expressivity and epidemiology, the polygenic architecture of BrS was substantially shared across ancestries.

关键词： SCN5A Brugada syndrome Cross-ancestry meta-analysis Genetic risk score Genome-wide association studies

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A Taxonomy of Generative AI in HEOR: Concepts, Emerging Applications, and Advanced Tools - An ISPOR Working Group Report

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Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research 2025年

作者： Rachael L Fleurence Xiaoyan Wang Jiang Bian Mitchell K Higashi Turgay Ayer Hua Xu Dalia Dawoud Jagpreet Chhatwal National Institute of Biomedical Imaging and Bioengineering Bethesda MD United States. Electronic address: Rachael.fleurence@nih.gov. Tulane University School of Public Health and Tropical Medicine New Orleans LA Intelligent Medical Objects Rosemont IL United States. Health Outcomes and Biomedical Informatics College of Medicine University of Florida FL United States Biomedical Informatics Clinical and Translational Science Institute University of Florida FL United States Office of Data Science and Research Implementation University of Florida Health Gainesville FL United States Regenstrief Institute Indianapolis IN United States Biostatistics and Health Data Science School of Medicine Indiana University Indianapolis IN United States. ISPOR The Professional Society for Health Economics and Outcomes Research Lawrenceville NJ United States. Center for Health & Humanitarian Systems Georgia Institute of Technology Atlanta GA Value Analytics Labs Boston MA United States. Institute Department of Biomedical Informatics and Data Science School of Medicine Yale University New Haven CT United States. National Institute for Health and Care Excellence London United Kingdom Cairo University Faculty of Pharmacy Cairo Egypt. Institute for Technology Assessment Massachusetts General Hospital Harvard Medical School Boston MA United States Center for Health Decision Science Harvard University Boston MA United States.

OBJECTIVE:This article presents a taxonomy of generative artificial intelligence (AI) for health economics and outcomes research (HEOR), explores emerging applications, outlines methods to improve the accuracy and reliability of AI-generated outputs and describes current limitations. METHODS:Foundational generative AI concepts are defined, and current HEOR applications are highlighted, including for systematic literature reviews, health economic modeling, real-world evidence generation, and dossier development. Techniques such as prompt engineering (e.g., zero-shot, few-shot, chain-of-thought, persona pattern prompting), retrieval-augmented generation, model fine-tuning, and domain-specific models, and use of agents are introduced to enhance AI performance. Limitations associated with the use of generative AI foundation models are described. RESULTS:Generative AI demonstrates significant potential in HEOR, offering enhanced efficiency, productivity, and innovative solutions to complex challenges. While foundation models show promise in automating complex tasks, challenges persist in scientific accuracy and reproducibility, bias and fairness and operational deployment. Strategies to address these issues and improve AI accuracy are discussed. CONCLUSION:Generative AI has the potential to transform HEOR by improving efficiency and accuracy across diverse applications. However, realizing this potential requires building HEOR expertise and addressing the limitations of current AI technologies. Ongoing research and innovation will be key to shaping AI's future role in our field.

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