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arXiv 2025年

作者： Chen, Bohua Schneider, Lucia Chantal Röver, Christian Comets, Emmanuelle Elze, Markus Christian Hooker, Andrew IntHout, Joanna Jannot, Anne-Sophie Julkowska, Daria Mimouni, Yanis Savelieva, Marina Stallard, Nigel Ursino, Moreno Vandemeulebroecke, Marc Weber, Sebastian Posch, Martin Zohar, Sarah Friede, Tim Department of Medical Statistics University Medical Center Göttingen Göttingen Germany UMR_S1085 Univ Rennes Inserm EHESP Rennes France Data Sciences F. Hoffmann-La Roche AG Basel Switzerland Department of Pharmacy Uppsala University UppsalaSE-751 24 Sweden Department of IQ Health Radboud university medical center P.O. Box 9101 Nijmegen6500 HB Netherlands Université Paris Cité HeKA INRIA Paris Inserm Centre de Recherche des Cordeliers Université Paris Cité Paris France European Joint Programme on Rare Diseases Institut Thématique Génétique Génomique et Bioinformatique INSERM Paris75013 France Thematic Institute of Genetics Genomics & Bioinformatics INSERM Paris75013 France Novartis Pharma AG Basel Switzerland UCB Farchim SA Chemin de Croix-Blanche 10 Bulle1630 Switzerland Warwick Clinical Trials Unit Warwick Medical School University of Warwick CoventryCV47AL United Kingdom Medical University of Vienna Center for Medical Data Science Institute of Medical Statistics Vienna Austria Inserm U1346 Université Paris Cité Centre Inria Paris HeKA Paris France partner site Lower Saxony Göttingen Germany Göttingen Germany CAIMed Lower Saxony Center for Artificial Intelligence Göttingen Germany

In the context of clinical research, computational models have received increasing attention over the past decades. In this systematic review, we aimed to provide an overview of the role of so-called in silico clinical trials (ISCTs) in medical applications. Exemplary for the broad field of clinical medicine, we focused on in silico (IS) methods applied in drug development, sometimes also referred to as model informed drug development (MIDD). We searched PubMed and *** for published articles and registered clinical trials related to ISCTs. We identified 202 articles and 48 trials, and of these, 76 articles and 19 trials were directly linked to drug development. We extracted information from all 202 articles and 48 clinical trials and conducted a more detailed review of the methods used in the 76 articles that are connected to drug development. Regarding application, most articles and trials focused on cancer and imaging related research while rare and pediatric diseases were only addressed in 18 and 4 studies, respectively. While some models were informed combining mechanistic knowledge with clinical or preclinical (in-vivo or in-vitro) data, the majority of models were fully data-driven, illustrating that clinical data is a crucial part in the process of generating synthetic data in ISCTs. Regarding reproducibility, a more detailed analysis revealed that only 24% (18 out of 76) of the articles provided an open-source implementation of the applied models, and in only 20% of the articles the generated synthetic data were publicly available. Despite the widely raised interest, we also found that it is still uncommon for ISCTs to be part of a registered clinical trial and their application is restricted to specific diseases leaving potential benefits of ISCTs not fully exploited. © 2025, CC BY.

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Genetic discovery in vesicoureteral reflux using exome sequencing: A pilot study

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Medical Journal Armed Forces India 2024年

作者： Thergaonkar, R.W. Manchanda, Vijeta Bansal, Gourja Yadav, Arti Singh, Jyotsna Varma, Binuja Dash, Debasis Mukerji, Mitali Bagga, Arvind Hari, Pankaj Consultant (Pediatrics) & Paed Nephrologist Command Hospital (Eastern Command) Kolkata India Senior Project Fellow (IT) TRISUTRA (Translational Research and Innovative Science ThRough Ayurgenomics) CSIR-Institute of Genomics & Integrative Biology -South Campus Mathura Road near to Sukhdev Vihar New Delhi India Market Development Manager Arrays & Bioinformatics Premas Life Sciences Pvt Ltd Premas Life Sciences Pvt Ltd Delhi New Delhi India Project Manager (Data Science & Research) CORE Diagnostics Gurgaon India PhD Scholar Neuropathology Laboratory Neurosciences Centre All India Institute of Medical Sciences New Delhi India Principal Scientist TCS Life Sciences New Delhi India Chief Scientist CSIR-Institute of Genomics & Integrative Biology -South Campus Mathura Road near to Sukhdev Vihar New Delhi India Professor & Head (Bioscience & Bioengineering) IIT Jodhpur Rajasthan India Professor & Head (Pediatrics) All India Institute of Medical Sciences New Delhi India Professor (Pediatrics Nephrology Division) AIIMS Ansari Nagar New Delhi India

Background: Vesicoureteral reflux (VUR) is a genetically heterogenous disorder. We conducted this pilot study to explore the possibility of genetic discovery in patients with VUR using exome sequencing. Methods: Whole exome sequencing was performed on the DNA of ten Indian children (nine boys) with bilateral grade IV/V reflux and bilateral renal hypodysplasia (extreme phenotype) as well as that of a family with 2 affected male siblings with bilateral VUR and chronic kidney disease. For prioritization of single nucleotide variations (SNVs) in the ten children with extreme phenotype, extremely rare and deleterious variations were selected from a list of 778 genes of biological relevance. Hypothesis-free prioritization was performed in the VUR family. Copy-number variations (CNV) were also called and prioritized from whole exome data. Results: Among ten patients with extreme phenotype, rare and novel and deleterious variants related to VUR and renal hypodysplasia were identified in 7/10 patients each. At least one prioritized variant in either genes related to VUR per se or renal hypodysplasia was seen in 8/10 patients. All patients carried different variations. Among the remaining two patients, one carried a pathogenic CNV. In the VUR family, a rare and deleterious variant in SLIT1 gene was identified. Conclusion: Sharp phenotyping in combination with exome sequencing using a combination of approaches including hypothesis-driven and hypothesis-free for prioritization of SNVs and study of CNVs seems to be an optimal method of genetic discovery of VUR. VUR is genetically heterogenous. © 2024

关键词： Copy-number variation Deconvolution Exome Genetics Vesicoureteral reflux

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data for Genomics in Healthcare: The Potential of Next-Generation Sequencing in Developing Precision Medicine

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

作者： Dafa Dwi Antara Michael Yahezkiel Lie Muhammad Darisza Nicola Kertapati Ivan Valentino Bharuno Mahesworo Ika Nurlaila Haryono Soeparno Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480

Recent advances in genomics on healthcare technology have opened the path towards more precise treatments by analysing patients' genome using a high throughput DNA sequencing technology called Next Generation Sequencing (NGS). The information obtained is further used to determine a more effective treatment path for patients based on their genetic profiles. It can also be employed to predict the risk of patients developing certain diseases. Despite particular difficulties it might create in the first phase of settling the integrative frameworks down, a successful combination of NGS and Big data technology would offer a huge improvement in terms of the effectiveness of treatments and medicine administration for the patients. This paper elucidates a general overview of how NGS-based DNA sequencing makes precision medicine possible and describes the use of Big data for genomics in healthcare-wise.

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Identification of PET/CT radiomic signature for classification of locally recurrent rectal cancer: A network-based feature selection approach

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Heliyon 2025年第1期11卷 e41404页

作者： Dalmonte, Sara Cocozza, Maria Adriana Cuicchi, Dajana Remondini, Daniel Faggioni, Lorenzo Castellucci, Paolo Farolfi, Andrea Fortunati, Emilia Cappelli, Alberta Biondi, Riccardo Cattabriga, Arrigo Poggioli, Gilberto Fanti, Stefano Castellani, Gastone Coppola, Francesca Curti, Nico IRCCS Rizzoli Orthopedic Institute Medical Technology Laboratory Bologna 40138 Italy Medical Physics Specialization School University of Bologna Bologna 40127 Italy Department of Radiology IRCCS Azienda Ospedaliera-Universitaria di Bologna Bologna 40138 Italy Medical and Surgical Department of Digestive Hepatic and Endocrine-Metabolic Diseases IRCCS Azienda Ospedaliera-Universitaria di Bologna Bologna 40138 Italy Department of Physics and Astronomy University of Bologna Bologna 40127 Italy Nuclear Medicine IRCCS Azienda Ospedaliero-Universitaria di Bologna Bologna 40138 Italy IRCCS Institute of Neurological Sciences of Bologna Data Science and Bioinformatics Laboratory Bologna 40139 Italy Department of Medical and Surgical Sciences University of Bologna Bologna 40138 Italy Dipartimento Diagnostica per Immagini AUSL Romagna UOC Radiologia Faenza Faenza 48018 Italy Academic Radiology Department of Translational Research and of New Surgical and Medical Technologies University of Pisa Pisa 56126 Italy

Background: The modern approach to treating rectal cancer, which involves total mesorectal excision directed by imaging assessments, has significantly enhanced patient outcomes. However, locally recurrent rectal cancer (LRRC) continues to be a significant clinical issue. Identifying LRRC through imaging is complex, due to the mismatch between fibrosis and inflammatory pelvic tissue. This work aimed to develop a machine learning model for predicting LRRC using radiomic features extracted from 18F-FDG Positron Emission Tomography/Computed Tomography (PET/CT). Methods: CT and PET images of PET/CT examinations were retrospectively collected from 44 patients, with 29 cases of recurrence (66 %) and 15 cases with no local recurrence (34 %). The whole analysis was conducted separately for CT and PET images to evaluate their different predictive power. Radiomic features were extracted from suspected lesion volumes identified by physicians and the most relevant radiomic features were selected to predict the presence or absence of LRRC. Feature selection was performed using a novel approach derived from gene expression analysis, based on the DNetPRO algorithm. The prediction was done using a Support Vector Classifier (SVC) with a 10-fold cross-validation. The efficiency of the pipeline in predicting LRRC was evaluated according to the sensitivity, specificity, Balanced Accuracy Score (BAS) and Matthews's Correlation Coefficient (MCC). Results: CT features were found to be the most predictive, showing a sensitivity of 0.80, a specificity of 0.82, a BAS of 0.81 and an MCC of 0.61. PET features obtained a sensitivity of 0.93, a specificity of 0.61, a BAS of 0.77 and a MCC of 0.52. The combination of PET and CT features obtained a sensitivity of 0.80, a specificity of 0.75, a BAS of 0.77 and a MCC of 0.53. Conclusions: To the best of our knowledge, the DNetPRO algorithm was applied for the first time to medical image analysis and proved suitable for the selection of radiomic featu

关键词： CT Machine learning Medical imaging PET Radiomics Rectal cancer Recurrence

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A simple linear-time algorithm for the common refinement of rooted phylogenetic trees on a common leaf set

arXiv

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arXiv 2021年

作者： Schaller, David Hellmuth, Marc Stadler, Peter F. Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Universität Leipzig Härtelstrasse 16-18 LeipzigD-04107 Germany Department of Mathematics Faculty of Science Stockholm University StockholmSE-10691 Sweden Competence Center for Scalable Data Services and Solutions Dresden/Leipzig Germany Leipzig Research Center for Civilization Diseases Universität Leipzig Augustusplatz 12 LeipzigD-04107 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Department of Theoretical Chemistry University of Vienna Währinger Straße 17 ViennaA-1090 Austria Facultad de Ciencias Universidad Nacional de Colombia Sede Bogotá Ciudad Universitaria D.C. BogotáCOL-111321 Colombia Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States

Background The supertree problem, i.e., the task of finding a common refinement of a set of rooted trees is an important topic in mathematical phylogenetics. The special case of a common leaf set L is known to be solvable in linear time. Existing approaches refine one input tree using information of the others and then test whether the results are isomorphic. Results A linear-time algorithm, LinCR, for constructing the common refinement T of k input trees with a common leaf set is proposed that explicitly computes the parent function of T in a bottom-up approach. Conclusion LinCR is simpler to implement than other asymptotically optimal algorithms for the problem and outperforms the alternatives in empirical comparisons. Availability An implementation of LinCR in Python is freely available at https://***/david-schaller/tralda. Copyright © 2021, The Authors. All rights reserved.

关键词： Phylogenetic

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HPV-CCDC106 integration alters local chromosome architecture and hijacks an enhancer by three-dimensional genome structure remodeling in cervical cancer

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Journal of Genetics and Genomics 2020年第8期47卷 435-448页

作者： Canhui Cao Ping Hong Xingyu Huang Da Lin Gang Cao Liming Wang Bei Feng Ping Wu Hui Shen Qian Xu Ci Ren Yifan Meng Wenhua Zhi Ruidi Yu Juncheng Wei Wencheng Ding Xun Tian Qinghua Zhang Wei Li Qinglei Gao Gang Chen Kezhen Li Wing-Kin Sung Zheng Hu Hui Wang Guoliang Li Peng Wu Department of Gynecologic Oncology Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430030China Cancer Biology Research Center(Key Laboratory of the Ministry of Education) Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430030China National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural UniversityWuhan430070China Agricultural Bioinformatics Key Laboratory of Hubei Province Hubei Engineering Technology Research Center of Agricultural Big DataCollege of InformaticsHuazhong Agricultural UniversityWuhan430070China State Key Laboratory of Agricultural Microbiology Huazhong Agricultural UniversityWuhan430070China Bio-Medical Center Huazhong Agricultural UniversityWuhan430070China College of Veterinary Medicine Huazhong Agricultural UniversityWuhan430070China Department of Obstetrics and Gynecology Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430022China Department of Obstetrics and Gynecology Key Laboratory for Molecular Diagnosis of Hubei ProvinceThe Central Hospital of WuhanTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430010China Department of Computational and Systems Biology Genome Institute of SingaporeSingapore Department of Gynecological Oncology The First Affiliated Hospital of Sun Yat-sen UniversityGuangzhou510080China

Integration of human papillomavirus(HPV)DNA into the human genome is a reputed key driver of cervical ***,the effects of HPV integration on chromatin structural organization and gene expression are largely *** studied a cohort of 61 samples and identified an integration hot spot in the CCDC106 gene on chromosome *** then selected fresh cancer tissue that contained the unique integration loci at CCDC106 with no HPV episomal DNA and performed whole-genome,RNA,chromatin immunoprecipitation and high-throughput chromosome conformation capture(Hi-C)sequencing to identify the mechanisms of HPV integration in cervical *** analyses indicated that chromosome 19 exhibited significant genomic variation and differential expression densities,with correlation found between three-dimensional(3D)structural change and gene ***,HPV integration divided one topologically associated domain(TAD)into two smaller TADs and hijacked an enhancer from PEG3 to CCDC106,with a decrease in PEG3 expression and an increase in CCDC106 *** expression dysregulation was further confirmed using 10 samples from our cohort,which exhibited the same HPV-CCDC106 *** summary,we found that HPV-CCDC106 integration altered local chromosome architecture and hijacked an enhancer via 3D genome structure ***,this study provides insight into the 3D structural mechanism underlying HPV integration in cervical carcinogenesis.

关键词： Cervical cancer HPV integration Fusion gene Hi-C Enhancer TAD

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Features Importance in Classification Models for Colorectal Cancer Cases Phenotype in Indonesia

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Procedia Computer science 2019年 157卷 313-320页

作者： Tjeng Wawan Cenggoro Bharuno Mahesworo Arif Budiarto James Baurley Teddy Suparyanto Bens Pardamean 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 BioRealm LLC Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Recently there has been an interest in predicting complex disease risk using models that combine the effects of many genetic factors together. These are known as polygenic models and are useful in evaluating risk of disease in patients. These models, however, often do not include important non-genetic factors that are important to the prediction of the disease. In this paper, we explore the prediction of colorectal cancer in Indonesia from non-genetic factors using common machine learning algorithms: XGBoost and Elastic Net. The result of this study identified 8 features with strong importance from both XGBoost and Elastic Net. We recommend including these features as covariates in future genetic association studies of colorectal cancer in Indonesia. Ultimately these models may be implemented as tools to screen patients for colorectal cancer risk.

关键词： Feature Importance Feature Selection XGBoost Generalized Linear Model Elastic Net Colorectal Cancer

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Electrocardiographic Deep Learning for Predicting Post-Procedural Mortality

arXiv

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arXiv 2022年

作者： Ouyang, David Theurer, John Stein, Nathan R. Hughes, J. Weston Elias, Pierre He, Bryan Yuan, Neal Duffy, Grant Sandhu, Roopinder K. Ebinger, Joseph Botting, Patrick Jujjavarapu, Melvin Claggett, Brian Tooley, James E. Poterucha, Tim Chen, Jonathan H. Nurok, Michael Perez, Marco Perotte, Adler Zou, James Y. Cook, Nancy R. Chugh, Sumeet S. Cheng, Susan Albert, Christine M. Department of Cardiology Smidt Heart Institute Cedars-Sinai Medical Center United States Division of Artificial Intelligence in Medicine Department of Medicine Cedars-Sinai Medical Center United States Department of Computer Science Stanford University United States Milstein Division of Cardiology Department of Medicine Columbia University Irving Medical Center United States Department of Biomedical Informatics Columbia University Irving Medical Center United States Division of Cardiovascular Medicine Department of Medicine Brigham and Women's Hospital United States Division of Cardiology Department of Medicine Stanford University United States Division of Bioinformatics Research Department of Medicine Stanford University United States Division of Anesthesia Department of Surgery Cedars-Sinai Medical Center United States Department of Biomedical Data Science Stanford University United States Division of Preventive Medicine Department of Medicine Brigham and Women's Hospital United States

Background. Pre-operative risk assessments used in clinical practice are limited in their ability to identify risk for post-operative mortality. We hypothesize that electrocardiograms contain hidden risk markers that can help prognosticate post-operative mortality. Methods. In a derivation cohort of 45,969 pre-operative patients (age 59±19 years, 55% women), a deep learning algorithm was developed to leverage waveform signals from pre-operative ECGs to discriminate post-operative mortality. Model performance was assessed in a holdout internal test dataset and in two external hospital cohorts and compared with the Revised Cardiac Risk Index (RCRI) score. Results. In the derivation cohort, there were 1,452 deaths. The algorithm discriminates mortality with an AUC of 0.83 (95% CI 0.79-0.87) surpassing the discrimination of the RCRI score with an AUC of 0.67 (CI 0.61-0.72) in the held out test cohort. Patients determined to be high risk by the deep learning model's risk prediction had an unadjusted odds ratio (OR) of 8.83 (5.57- 13.20) for post-operative mortality as compared to an unadjusted OR of 2.08 (CI 0.77-3.50) for postoperative mortality for RCRI >2. The deep learning algorithm performed similarly for patients undergoing cardiac surgery with an AUC of 0.85 (CI 0.77 - 0.92), non-cardiac surgery with an AUC of 0.83 (0.79 - 0.88), and catherization/endoscopy suite procedures with an AUC of 0.76 (0.72 - 0.81). The algorithm similarly discriminated risk for mortality in two separate external validation cohorts from independent healthcare systems with AUCs of 0.79 (0.75-0.83) and 0.75 (0.74-0.76) respectively. Conclusion. The findings demonstrate how a novel deep learning algorithm, applied to preoperative ECGs, can improve discrimination of post-operative mortality. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning algorithms

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Comparing the Accuracy of Multiple Commercial Wearable Devices: A Method

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Procedia Computer science 2019年 157卷 567-572页

作者： Bens Pardamean Haryono Soeparno Bharuno Mahesworo Arif Budiarto James Baurley Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 BioRealm LLC USA

To maintain a good fitness level, performing a minimum daily physical activity is important. Many people think that they have already met the suggested guidelines. However, most of them were wrong when they quantify their activity level using a personal activity tracker device. This problem indicates the need for personal physical activity tracker or wearable device to maintain the fitness level. However, as wide-range wearable devices available today, it is difficult to find the most suitable one regarding the price and quality. This paper proposes a study to explore the association between the quality and the price of the devices. The quality is evaluated by comparing the accuracy of the heart rate sensor and step counter of several wearable devices with the ground truth measurements. The test result is then analyzed using a series of statistical methods including, a paired t-test, Pearson’s correlation test, and Bland-Altman plot. Both t-test and correlation coefficient is used to plot the association between quality and price.

关键词： Wearable Device Fitness Tracker Correlation Heart Rate Step Counting Bland-Altman

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Fast and Effective Clustering Method for Ancestry Estimation

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Procedia Computer science 2019年 157卷 306-312页

作者： Arif Budiarto Bharuno Mahesworo James Baurley Teddy Suparyanto Bens Pardamean 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 BioRealm LLC USA Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Ancestry estimation which provides family history information is one of the most popular services in direct-to-consumer genomic testing. It is also an important task which aimed to reduce the confounding by ancestry on the relationship of genotypes and disease risk in assocation studies. Several methods have been developed to generate the best ancestry estimated scores even though some of them are still facing inefficient computation time. In this paper, a combination method between KMeans clustering and PCA is proposed estimate ancestry estimation from SNP genotyping data. This method was compared with baseline model, called fastSTRUCTURE, in term of the quality of clustering and computation time. Public data from 1000 Genome project is used to train and evaluate the proposed model and the baseline model. The proposed model can successfully generate clusters with better accuracy than fastSTRUCTURE (91.02% over 90.39%). More importantly, it can boost the computation time until 100 times faster than fastSTRUCTURE (from 490 seconds to 4.86 seconds).

关键词： Ancestry Estimation Population Stratification Clustering bioinformatics Genomics

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