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arXiv 2021年

作者： Nakken, Sigve Gundersen, Sveinung Bernal, Fabian L.M. Polychronopoulos, Dimitris Hovig, Eivind Wesche, Jørgen Department of Tumor Biology Institute for Cancer Research Oslo University Hospital Norway Centre for Cancer Cell Reprogramming Institute of Clinical Medicine Faculty of Medicine University of Oslo Norway Centre for Bioinformatics Department of Informatics University of Oslo Norway University Center for Information Technology University of Oslo Norway Early Data Science and Computational Oncology Research and Early Development Oncology R&D AstraZeneca Cambridge United Kingdom

Genome-scale screening experiments in cancer produce long lists of candidate genes that require extensive interpretation for biological insight and prioritization for follow-up studies. Interrogation of gene lists frequently represents a significant and time-consuming undertaking, in which experimental biologists typically combine results from a variety of bioinformatics resources in an attempt to portray and understand cancer relevance. As a means to simplify and strengthen the support for this endeavor, we have developed oncoEnrichR, a flexible bioinformatics tool that allows cancer researchers to comprehensively interrogate a given gene list along multiple facets of cancer relevance. oncoEnrichR differs from general gene set analysis frameworks through the integration of an extensive set of prior knowledge specifically relevant for cancer, including ranked gene-tumor type associations, literature-supported proto-oncogene and tumor suppressor gene annotations, target druggability data, regulatory interactions, synthetic lethality predictions, as well as prognostic associations, gene aberrations, and co-expression patterns across tumor types. The software produces a structured and user-friendly analysis report as its main output, where versions of all underlying data resources are explicitly logged, the latter being a critical component for reproducible science. We demonstrate the usefulness of oncoEnrichR through interrogation of two candidate lists from proteomic and CRISPR screens. oncoEnrichR is freely available as a web-based workflow hosted by the Galaxy platform (https://***), and can also be accessed as a stand-alone R package (https://***/sigven/oncoEnrichR). © 2021, CC BY-NC-SA.

关键词： Tumors

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WOMEN WITH KNEE OSTEOARTHRITIS: POST-MENOPAUSAL PROGESTERONE IS LINKED TO PAIN INTENSITY, RADIOGRAPHIC SCORE, AND SYNOVIAL ALTERATION

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Osteoarthritis and Cartilage 2025年 33卷 S428-S428页

作者： Carmen Corciulo Fadi Askar Georgios Chatziagorou Sofia Wustenhagen Björn Andersson Evelin Berger Anna Ekman Anna Rudin Dept. of Pharmacology - Institute of Neuroscience and Physiology University of Gothenburg Göteborg Sweden SportsMed at Carlanderska Hospital Göteborg Sweden Department of Rheumatology and Inflammation University of Gothenburg Göteborg Sweden Bioinformatics and Data Center Sahlgrenska Academy University of Gothenburg Göteborg Sweden Proteomics Core Facility Sahlgrenska Academy University of Gothenburg Göteborg Sweden Department of Public Health and Community Medicine University of Gothenburg Göteborg Sweden

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Meta-Analysis of Genome-Wide Association Studies of Acute Myeloid Leukemia (AML) Patients Identifies Variants Associated with Risk of 11q23/KMT2A-Translocated and Core-Binding Factor (CBF) AML and Suggests a Role for Transcription Elongation in Leukemogenesis

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BLOOD 2020年 136卷 29-30页

作者： Walker, Christopher J. Wang, Junke Clay-Gilmour, Alyssa I. Mrozek, Krzysztof Nicolet, Deedra Oakes, Christopher C. Kohlschmidt, Jessica Powell, Bayard L. Kolitz, Jonathan E. Carroll, Andrew J. Karaesmen, Ezgi Rizvi, Abbas A. Zhu, Qianqian Yan, Li Preus, Leah Liu, Song Wang, Yiwen Stram, Daniel O. Pooler, Loreall Sheng, Xin Haiman, Christopher Van den Berg, David Webb, Amy Brock, Guy Spellman, Stephen R. Pasquini, Marcelo C. McCarthy, Philip L. Allan, James M. Stolzel, Friedrich Onel, Kenan Stone, Richard M. Garzon, Ramiro Bloomfield, Clara D. Byrd, John C. De la Chapelle, Albert Hahn, Theresa E. Eisfeld, Ann-Kathrin Sucheston-Campbell, Lara E. The Ohio State University Comprehensive Cancer Center Columbus OH College of Pharmacy The Ohio State University Columbus OH Arnold School of Public Health- Department of Epidemiology & Biostatistics University of South Carolina Greenville SC The Ohio State University Comprehensive Cancer Center Columbus OH OSU Columbus OH Alliance Statistics and Data Center The Ohio State University Comprehensive Cancer Center Columbus OH Wake Forest Univ. School of Med. Winston Salem NC Northwell Health Cancer Institute Zucker School of Medicine at Hofstra/Northwell Lake Success NY Department of Genetics University of Alabama at Birmingham Birmingham AL The Ohio State University Columbus OH Department of Biostatistics and Bioinformatics Roswell Park Comprehensive Cancer Center Buffalo NY Department of Biostatistics Roswell Park Comprehensive Cancer Center Buffalo NY University of Southern California Keck School of Medicine of USC Los Angeles CA University of Southern California Los Angeles CA College of Veterinary Medicine Cancer Prevention & Control Columbus OH

The first three authors contributed equally. The last three authors share senior authorship. Background: Although there has been an increased recognition of the contribution of germline variants to development of myeloid neoplasms, only two large-scale case-control genome-wide association studies (GWASs) have been conducted to identify variants that predispose to AML. Importantly, these studies were dedicated to AML predisposition in general, without investigation of molecularly distinct AML subtypes. Thus, we performed the first dedicated meta-analysis combining the two GWASs to investigate predisposing variants to cytogenetic AML subsets characterized by recurrent translocations and inversions. Methods: Two sets of adult de novo AML patients treated on Alliance for Clinical Trials in Oncology (Alliance) protocols, and two sets of adult de novo AML patients reported to CIBMTR (2000-11) from DISCOVeRY-BMT cohorts were compared with four sets of population-matched non-leukemic individuals of European ancestry. Illumina Infinium arrays were used for genotyping. The haplotype reference consortium was used for imputation and comparisons were performed using SNPtest and METAL with fixed-effects, for CBF-AML (n=251, including t(8;21), n=115; inv(16), n=136) and AML with 11q23/ KMT2A translocations (n=177). Blood or bone marrow samples from subsets of these patients and additional AML patients with other cytogenetic abnormalities were used for total transcriptome RNA sequencing with Illumina instruments. Results: Two risk loci reached genome-wide significance in AML patients with 11q23/ KMT2A translocations (Fig 1A). The most significant single nucleotide polymorphism (SNP) in the 4q21.3 risk locus, rs17668899[A] ( P = 2.32 x 10 -8 , odds ratio [OR] = 3.92 [2.43-6.32]) is in intron 6 of the AFF1 gene (also called AF4 ) (Fig 1B), within an enhancer that interacts with the AFF1 transcription start site (Fig 1C, left). KMT2A- translocated AML patients with the risk allele had

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Genome-Wide Association Study of Intraocular Pressure in Population-Based Cohorts in Japan: The Tohoku Medical Megabank Organization Eye Study

Ophthalmology Science

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Ophthalmology Science 2025年第5期5卷

作者： Nobuo Fuse Hayato Anzawa Miyuki Sakurai Ikuko N. Motoike Satoshi Nagaie Tomohiro Nakamura Akiko Miyazawa Eiichi N. Kodama Masatsugu Orui Yohei Hamanaka Tomoko Kobayashi Akira Uruno Makiko Taira Ritsuko Shimizu Naoki Nakaya Mami Ishikuro Taku Obara Fuji Nagami Soichi Ogishima Fumiki Katsuoka Masayuki Yamamoto Tohoku Medical Megabank Organization Tohoku University Sendai Japan Department of System Bioinformatics Tohoku University Graduate School of Information Sciences Sendai Japan Department of Data Science Kyoto Women's University Kyoto Japan Tohoku University International Research Institute of Disaster Science Sendai Japan Tohoku University Graduate School of Medicine Sendai Japan Advanced Research Center for Innovations in Next-Generation Medicine Tohoku University Sendai Japan

Purpose This study was conducted to elucidate the distribution and determinants of ocular biometric parameters and to assess the association between intraocular pressure (IOP) and single nucleotide polymorphisms (SNPs) in the Japanese population–based genome cohort studies. Design Cross-sectional analysis involving genome-wide association studies (GWASs). Participants In total, 22 150 participants aged >18 years from the population cohort (Community-Based Cohort [CommCohort]) and 11 302 participants from the Birth and Three-Generation (BirThree) Cohort of the Tohoku Medical Megabank Organization Eye Study were examined. Methods Participant underwent interviews, ophthalmic and physiological examinations, laboratory tests, and microarray analyses. Genome-wide association studies were conducted in the CommCohort (discovery stage) and the BirThree Cohort (replication stage), followed by a meta-analysis. Associations of SNPs and IOP were evaluated using a genome-wide significance threshold (5 × 10 − 8 ). Main Outcome Measures Association of SNPs with IOP and distributions of IOP by sex and age. Results In the discovery stage, the mean IOP of the right and left eye was 13.95 and 14.02 mmHg, respectively. In the replication stage, the corresponding values were 14.32 and 14.27 mmHg, respectively. A significant age-related reduction in IOP was observed in both stages ( P < 0.001). Genome-wide association studies identified 573 and 2 genome-wide significant SNPs in the discovery and replication stages, respectively. Meta-analysis revealed 1601 significant SNPs across 21 loci on 11 chromosomes (Chrs). Of these loci, 17 were previously known to be associated with IOP or glaucoma, while four—septin-8 ( SEPT8 ; Chr5), aldehyde dehydrogenase 2 ( ALDH2 ; Chr12), collagen type VI alpha 2 chain ( COL6A2 ; Chr21), and Wnt family member 7B ( WNT7B ; Chr22)—were newly identified. Conclusions This large-scale GWAS in a Japanese population identified 21 loci associated with IOP, including

关键词： Intraocular pressure Population-based cohort study Genome-wide association study (GWAS) AL axial length ALDH2 aldehyde dehydrogenase 2 ANAPC1 anaphase promoting complex subunit 1 ARHGEF12 Rho Guanine Nucleotide Exchange Factor 12 ABCA1 ATP binding cassette subfamily A member 1 BirThree Birth and Three-Generation CAMTA2 calmodulin binding transcription activator 2 CAV caveolin CCT central corneal thickness CDKN2B-AS1 CDKN2B and CDKN2A antisense cis and trans regulatory RNA 1 Chr chromosome CI confidence interval CommCohort Community-Based Cohort COL6A2 collagen type VI alpha 2 chain FNDC3B fibronectin type III domain-containing protein 3B FOXC1 Forkhead Box C1 GAS7 growth arrest specific 7 GWAS genome-wide association study IOP intraocular pressure OMIM Online Mendelian Inheritance in Man POAG primary open-angle glaucoma SNP single nucleotide polymorphism SEPT8 septin-8 TMM Tohoku Medical Megabank ToMMo Tohoku Medical Megabank Organization TLCD5 TLC domain containing 5 WNT7B Wnt family member 7B

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Implementation of online learning during the COVID-19 pandemic in primary schools

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AIP Conference Proceedings 2023年第1期2594卷

作者： A. Anugrahana E. Liswantiani D. Sudigyo T. Suparyanto B. Pardamean Elementary School Teacher Education Study Program (PGSD) Faculty of Teacher Training and Education Sanata Dharma University Yogyakarta Indonesia 55281 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

The COVID-19 pandemic requires face-to-face learning to shift to online or distance learning. Therefore, the elementary school, which is the most basic level of education affected by this phenomenon, where this research is carried out. This study aimed to analyze the process of implementing online learning, the supporting and inhibiting factors for teachers, students, and parents in implementing online learning during the COVID-19 pandemic. In addition, good practices for online learning were also analyzed in this study to understand these phenomena in elementary schools. The research method utilized descriptive qualitative research and was performed at a private primary school in Yogyakarta. The subjects consisted of teachers, students, and parents that were involved in the online learning process. Online questionnaires and interviews were used to collect data. The results of this study show the enormous impact of the COVID-19 pandemic on the learning process. Learning that is usually carried out face-to-face (conventional) has now been converted into online learning in elementary school.

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Sample size estimation for comparing dynamic treatment regimens in a SMART: a Monte Carlo-based approach and case study with longitudinal overdispersed count outcomes

arXiv

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arXiv 2021年

作者： Yap, Jamie Dziak, John J. Maiti, Raju Lynch, Kevin McKay, James R. Chakraborty, Bibhas Nahum-Shani, Inbal Institute for Social Research Ann ArborMI United States Bennett Pierce Prevention Research Center Pennsylvania State University PA United States Economic Research Unit Indian Statistical Institute Kolkata India Department of Psychiatry University of Pennsylvania PA United States Philadephia Veterans Affairs Medical Center PA United States Centre for Quantitative Medicine Duke-NUS Medical School Singapore Department of Statistics and Data Science National University of Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States

Dynamic treatment regimens (DTRs), also known as treatment algorithms or adaptive interventions, play an increasingly important role in many health domains. DTRs are motivated to address the unique and changing needs of individuals by delivering the type of treatment needed, when needed, while minimizing unnecessary treatment. Practically, a DTR is a sequence of decision rules that specify, for each of several points in time, how available information about the individual’s status and progress should be used in practice to decide which treatment (e.g., type or intensity) to deliver. The sequential multiple assignment randomized trial (SMART) is an experimental design widely used to empirically inform the development of DTRs. Sample size planning resources for SMARTs have been developed for continuous, binary, and survival outcomes. However, an important gap exists in sample size estimation methodology for SMARTs with longitudinal count outcomes. Further, in many health domains, count data are overdispersed – having variance greater than their mean. We propose a Monte Carlo-based approach to sample size estimation applicable to many types of longitudinal outcomes and provide a case study with longitudinal overdispersed count outcomes. A SMART for engaging alcohol and cocaine-dependent patients in treatment is used as motivation. © 2021, CC BY.

关键词： Patient treatment

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Prediction of Clinically Significant Prostate Cancer by a Specific Collagen-related Transcriptome, Proteome, and Urinome Signature

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European urology oncology 2024年第3期8卷 652-662页

作者： Isabel Heidegger Maria Frantzi Stefan Salcher Piotr Tymoszuk Agnieszka Martowicz Enrique Gomez-Gomez Ana Blanca Guillermo Lendinez Cano Agnieszka Latosinska Harald Mischak Antonia Vlahou Christian Langer Friedrich Aigner Martin Puhr Anne Krogsdam Zlatko Trajanoski Dominik Wolf Andreas Pircher Department of Urology Medical University of Innsbruck Innsbruck Austria. Electronic address: Isabel-maria.heidegger@i-med.ac.at. Department of Biomarker Research Mosaiques Diagnostics GmbH Hannover Germany. Department of Internal Medicine V Hematology and Oncology Medical University of Innsbruck Innsbruck Austria. Data Analytics As a Service Tirol Wörgl Austria. Urology Department Reina Sofía University Hospital Maimonides Institute of Biomedical Research of Cordoba University of Cordoba Cordoba Spain. Urology Department Biomedical Institute of Seville University Hospital Virgen del Rocío Seville Spain. Systems Biology Center Biomedical Research Foundation Academy of Athens Athens Greece. Department of Radiology Medical University of Innsbruck Innsbruck Austria. Department of Urology Medical University of Innsbruck Innsbruck Austria. Biocenter Institute of Bioinformatics Medical University of Innsbruck Innsbruck Austria. Department of Internal Medicine V Hematology and Oncology Medical University of Innsbruck Innsbruck Austria. Electronic address: andreas.pircher@i-med.ac.at.

BACKGROUND AND OBJECTIVE:While collagen density has been associated with poor outcomes in various cancers, its role in prostate cancer (PCa) remains elusive. Our aim was to analyze collagen-related transcriptomic, proteomic, and urinome alterations in the context of detection of clinically significant PCa (csPCa, International Society of Urological Pathology [ISUP] grade group ≥2). METHODS:Comprehensive analyses for PCa transcriptome (n = 1393), proteome (n = 104), and urinome (n = 923) data sets focused on 55 collagen-related genes. Investigation of the cellular source of collagen-related transcripts via single-cell RNA sequencing was conducted. Statistical evaluations, clustering, and machine learning models were used for data analysis to identify csPCa signatures. KEY FINDINGS AND LIMITATIONS:Differential expression of 30 of 55 collagen-related genes and 34 proteins was confirmed in csPCa in comparison to benign prostate tissue or ISUP 1 cancer. A collagen-high cancer cluster exhibited distinct cellular and molecular characteristics, including fibroblast and endothelial cell infiltration, intense extracellular matrix turnover, and enhanced growth factor and inflammatory signaling. Robust collagen-based machine learning models were established to identify csPCa. The models outcompeted prostate-specific antigen (PSA) and age, showing comparable performance to multiparametric magnetic resonance imaging (mpMRI) in predicting csPCa. Of note, the urinome-based collagen model identified four of five csPCa cases among patients with Prostate Imaging-Reporting and data System (PI-IRADS) 3 lesions, for which the presence of csPCa is considered equivocal. The retrospective character of the study is a limitation. CONCLUSIONS AND CLINICAL IMPLICATIONS:Collagen-related transcriptome, proteome, and urinome signatures exhibited superior accuracy in detecting csPCa in comparison to PSA and age. The collagen signatures, especially in cases of ambiguous lesions on mpMRI, successfull

关键词： Cancer aggressiveness Cancer biology Collagen metabolism Machine learning Multiparametric-magnetic resonance imaging Predictive biomarker Prostate cancer detection

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Blood Pressure Value Prediction in Hemodialysis Patients Using the CNN+GRU Network Architecture

Blood Pressure Value Prediction in Hemodialysis Patients Usi...

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IEEE International Conference on Orange Technologies (ICOT)

作者： Hsiang-Wei Hu Wun-Long Jheng En-Han Huang Kuan-Yu Chen Chih-Hao Liu Hsin-Yin Chi Wu Chin-Yu Yu-Cheng Chen Jian-An Wang Hsuan-Ming Lin Supratip Ghose Che-Wen Chen Jhing-Fa Wang National Cheng Kung University Tainan Taiwan International Academia of Biomedical Innovation Technology Taipei Taiwan National Defense Medical Center Taipei Taiwan Acusense Biomedical Technology Corp Tainan Taiwan Institute of Biomedical Electronics and Bioinformatics Taipei Taiwan Chinese Culture University Taipei Taiwan National Cheng Kung University Institute of Data Science National Cheng-Kung University Tainan Taiwan China Medical University Tainan Taiwan

ISBN: (纸本)9781665478434

During clinical operation of Hemodialysis, Blood pressure (BP) variability, affect significant global risks and offshoot complication linked to adverse mortality. However, on this task, a prediction model has not been studied extensively with the advent of big data models. Earlier machine learning models lacked the exploitation of precise modeling regarding temporal context information affecting stratified BP values in the output. Recently deep learning models introduced hierarchical temporal feature engineering and context information for Spatio-temporal tasks, and thus this study geared to exploit the advantages of deep learning models, specially, in the form of the Convolutional Neural Network (CNN) model, variants of RNN models, and adaptive hybrid networks combining gated recurrent units (GRU) and CNN for the time-series tasks. In addition, a hybrid deep architecture of CNN and LSTM was employed to predict BP value and make it more efficient and effective when applied to health care. The benchmark results on our dataset for the validation of the CNN+GRU hybrid deep architecture model yielded promising contributions on the prediction task with most significant accuracy value of 72.7% corresponding to delta 15 mmHg and 83.7% when 20 mmHg.

关键词： Deep learning Adaptation models Predictive models Logic gates Blood pressure data models Convolutional neural networks

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The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos

arXiv

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arXiv 2023年

作者： Turishcheva, Polina Fahey, Paul G. Hansel, Laura Froebe, Rachel Ponder, Kayla Vystrčilová, Michaela Willeke, Konstantin F. Bashiri, Mohammad Wang, Eric Ding, Zhiwei Tolias, Andreas S. Sinz, Fabian H. Ecker, Alexander S. Qiur, Yongrong Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Department of Neuroscience Baylor College of Medicine HoustonTX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Department of Ophthalmology Byers Eye Institute Stanford University School of Medicine StanfordCA United States Stanford Bio-X Stanford University StanfordCA United States Wu Tsai Neurosciences Institute Stanford University StanfordCA United States International Max Planck Research School for Intelligent Systems University of Tübingen Germany Institute for Bioinformatics and Medical Informatics University of Tübingen Germany Department of Electrical Engineering Stanford University StanfordCA United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Understanding how biological visual systems process information is challenging due to the complex nonlinear relationship between neuronal responses and high-dimensional visual input. Artificial neural networks have already improved our understanding of this system by allowing computational neuroscientists to create predictive models and bridge biological and machine vision. During the Sensorium 2022 competition, we introduced benchmarks for vision models with static input (i.e. images). However, animals operate and excel in dynamic environments, making it crucial to study and understand how the brain functions under these conditions. Moreover, many biological theories, such as predictive coding, suggest that previous input is crucial for current input processing. Currently, there is no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we propose the Sensorium 2023 Benchmark Competition with dynamic input (https://***/). This competition includes the collection of a new large-scale dataset from the primary visual cortex of ten mice, containing responses from over 78,000 neurons to over 2 hours of dynamic stimuli per neuron. Participants in the main benchmark track will compete to identify the best predictive models of neuronal responses for dynamic input (i.e. video). We will also host a bonus track in which submission performance will be evaluated on out-of-domain input, using withheld neuronal responses to dynamic input stimuli whose statistics differ from the training set. Both tracks will offer behavioral data along with video stimuli. As before, we will provide code, tutorials, and strong pre-trained baseline models to encourage participation. We hope this competition will continue to strengthen the accompanying Sensorium benchmarks collection as a standard tool to measure progress in large-scale neural system identification models of the entire mouse visual hierarchy and

关键词： Mammals

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Real-time Weed Identification Using Machine Learning and Image Processing in Oil Palm Plantations

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IOP Conference Series: Earth and Environmental Science 2022年第1期998卷

作者： Erick Firmansyah Teddy Suparyanto Alam Ahmad Hidayat Bens Pardamean Agricultural Engineering Department Institute of Agricultural STIPER Yogyakarta 55282 Indonesia 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 Indonesia 11480

The effectiveness and efficiency of the operation of oil palm plantations are considered to be the most crucial factor to develop the productivity and profitability of the palm oil business. One of the major obstacles for the plants to optimally produce crops based on their capacity is caused by the presence of noxious weeds in the plantation area. However, weed control via chemical processes may potentially harm the surrounding environment if it is not properly managed. Therefore, an automatic system to assist the farmers to identify and control the weeds is required to minimize harmful impacts on the environment. Machine Learning (ML) and Artificial Intelligence (AI)-based systems provide powerful tools to perform such tasks. In this work, we aim for an ML-based system design to perform an automatic weed recognition task. The methodology can provide an effort for environmental sustainability in oil palm plantations. The weed identification involves the description, the local names, and tolerance class of the weeds as well as suggestions to control them. The flow of this work consists of weed and herbicide data acquisition, data labeling, model configurations, and data training. Further, the proposed system can be adopted as an android-based application in mobile devices that can deploy the trained model to predict weed category in both real-time and non-real-time tasks.

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