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Lockdown strategy worth lives: The SEIRD modelling in COVID-19 outbreak in Indonesia

IOP Conference Series: Earth and Environmental science 2021年第1期729卷

作者： I Nurlaila A A Hidayat B Pardamean Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480 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

Lockdowns, despite their conflicting restrictions and consequences they might offer when enforced as a national strategy, are deemed to be suggestive for a prompt conquer to the Coronavirus Disease-19 (COVID-19) outbreak. There have been some success stories such as in China, South Korea and our ASEAN member fellow, Vietnam, which exhibited extremely fallen numbers of COVID-19 cases post the enforcement of lockdown. Indonesia, however, remains in the crux of dispute whether or not the lockdown is opted to force COVID-19 transmission down under control. We, in this respect, employ the most popular model which has been broadly applied in the field of epidemiology, referred to as SEIRD (Susceptible, Exposed, Infectious, Recovered, and Death), the extension form of an age-structured SEIR, where the Death (D) is included to provide more factual situation. We modify the I (Infectious) fraction as symp-tomatic (Is) and asymptomatic (Ia) infectives. There are three lockdown scenarios simulated in our modified SEIRD with the starting date are 26 April 2020 when the immediate lockdown was enacted-and a-week and two-week-gaps, respectively. We figured out that by stipulating the lockdown without delay (26 April 2020), the new cases could be kept below 10,000. A week delay escalates case number to 5,000 and delay for one more week results in beyond 20,000 new cases. Furthermore, when the intervention is delayed following the delayed lockdown, normalization would demand a longer period within which dealing with more critical and dying patients is unavoidable and should be more anticipated.

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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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APPROXIMATE BAYESIAN INFERENCE OF DIRECTED ACYCLIC GRAPHS IN BIOLOGY WITH FLEXIBLE PRIORS ON EDGE STATES

arXiv

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arXiv 2019年

作者： Martin, Evan A. Fu, Audrey Qiuyan The Bioinformatics and Computational Biology Program University of Idaho United States The Bioinformatics and Computational Biology Program Department of Mathematics and Statistical Science Institute for Modeling Collaboration and Innovation Institute for Interdisciplinary Data Sciences University of Idaho United States

Graphical models or networks describe the statistical dependence among multiple variables and are widely used in biology (e.g., gene regulatory networks). Under appropriate assumptions, directed edges may represent causal relationships. A key feature of a biological network is sparsity, defined by how likely an edge is present, of which we often have some knowledge. However, most existing Bayesian methods use priors for the entire graph, making it difficult to specify the level of sparsity. The few methods that use priors on edges estimate the two directions independently;the sum of the two probabilities can exceed 1. Here, we present baycn (BAYesian Causal Network), a novel approximate Bayesian method that represents a graph in terms of three states of edges: the two directions and edge absence, and specifies priors on these edge states. We design a pseudo Bayesian sampling algorithm for efficient inference. We apply baycn to two genomic problems: i) distinguishing direct and indirect target genes of genetic variants, using these variants as instrumental variables, and ii) inferring combinatorial binding of highly-correlated transcription factors in Drosophila. In both cases and in extensive simulations, our method demonstrates much improved accuracy over existing methods for the whole graph and for individual edges. Copyright © 2019, The Authors. All rights reserved.

关键词： Inference engines

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Analysis of Acoustic Features in Gender Identification Model for English and Bahasa Indonesia Telephone Speeches

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Procedia Computer science 2019年 157卷 199-204页

作者： Muhamad Fitra Kacamarga Tjeng Wawan Cenggoro Arif Budiarto Reza Rahutomo Bens Pardamean Eureka.ai 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 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

One of the most interesting topics in auditory problem is determining gender of the speaker. In recent years, machine learning has gained significant attentions as a way to build a classifier from labeled data which also can be implemented to build a gender classifier. In this study we develop gender classifier using two different datasets with different languages, English and Bahasa Indonesia. Each data from both datasets is represented by 20 acoustic features. Multi Layer Perceptron (MLP) is used to build the classification model using all these features and trained only on English dataset. This model is evaluated in both dataset to get the performance matrices consist of accuracy, AUROC, precision and recall. Ultimately, using this model we also identify and compare important features from both dataset to see the different characteristics of English and Bahasa Indonesia speeches.

关键词： Gender Identification Acoustic Feature Speech Recognition

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DNAku Consumers Profile: One of The First Direct to Customer Genetics Testing in Indonesia

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

作者： Deby Erina Parung Kians Azizatikarna Dian Amirulloh Erlin Listiyaningsih Bharuno Mahesworo Arif Budiarto Simon Bens Pardamean Genetics Indonesia Jakarta Indonesia 12940 Bioinformatics & 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

DNAku test is one of the first direct to customer genetics testing in Indonesia. DNAku test came with a different approach as it gave their customer reports of their genetic predisposition to corresponding traits. Four months after launch, we create consumer report of this genetics testing based on DNAku questionnaire results. We applied three statistical methods, including descriptive statistics, multivariate normality test, and t-test to the demographic data of the DNAku customers. The result shows population clusters which may represent population groups that are interested in genetics testing. The questionnaire also has questions about the customer's lifestyle. The results of these questions were processed to find the lifestyle of DNAku customers.

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data Engineering Pipeline to Analyse Jakarta's Air Quality during COVID-19-Caused Lockdown Periods

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

作者： Reza Rahutomo Bens Pardamean Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia 11480 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

Jakarta lifted up lockdown after passing more than 50 days of large-scale social activity restriction and initiated phase opening to new normal. To analyse Jakarta's air quality after passing lockdown, a pipeline of data engineering is needed. By acquiring time series data from ***, a time-series database system is developed with Python programming language and its fundamental libraries namely Pandas, NumPy, SQLite. After PM 2.5 data are pre-processed into average per-hour and grouped by applicable periods (pre-lockdown, lockdown, and phase opening), a pattern of PM 2.5 in South Jakarta is revealed by using data visualization library Matplotlib. The apex of PM 2.5 occurs earlier during lockdown (04:00) and phase opening (02:00) rather than when it was normal or pre-lockdown (08:00) even though the nadir of PM2.5 still occurs at the same time (16:00 – 17:00).

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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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6Développement de nouveaux ”endpoints” cliniques à l'aide de biomarqueurs numériques basés sur la voix pour le suivi à distance, en vie réelle, de patients en recherche clinique: exemple de biomarqueurs vocaux pour le suivi du statut symptomatique de patients avec COVID-19

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Revue d'Épidémiologie et de Santé Publique 2022年 70卷 S260-S261页

作者： G. Fagherazzi L. Zhang A. Elbéji M. Pizzimenti P. Nazarov G. Aguayo A. Fischer Deep Digital Phenotyping Research Unit Department of Precision Health Luxembourg Institute of Health Strassen Luxembourg Bioinformatics Platform Luxembourg Institute of Health 1A-B rue Thomas Edison L-1445 Strassen Luxembourg Luxembourg Multiomics Data Science Department of Cancer Research Luxembourg Institute of Health 1A-B rueThomas Edison L-1445 Strassen Luxembourg

Introduction Les opportunités offertes par les nouvelles technologies en santé permettent de repenser la conduite d’études cliniques, afin d'alléger le fardeau de participation des patients, diminuer le nombre de visites sur site, ainsi que d'améliorer le suivi à distance inter-visites. Pour cela, parmi toutes les technologies numériques émergentes, le champ des biomarqueurs vocaux est particulièrement prometteur [1]. Nous proposons ici une méthodologie d'identification et de validation de biomarqueurs vocaux à l'aide de Méthodes d'analyse du signal et d'intelligence artificielle (Fig. 1), que nous illustrons avec des Résultats dans le cadre du suivi de patients avec COVID-19 ou un COVID long pour le suivi du statut symptomatique. Méthodes Nous avons régulièrement collecté des enregistrements audio standardisés pendant les deux premières semaines de suivi en vie réelle de N=272 participants de l’étude de cohorte Predi-COVID [2], tous ayant eu la maladie COVID-19, confirmée par test PCR positif. Des biomarqueurs vocaux ont été entrainés pour prédire le statut symptomatique, défini à partir d'une liste quotidienne des symptômes liés à COVID-19 les plus fréquents. Les audios (lecture d'un texte standardisé de 25 secondes) ont été nettoyés et harmonisés, puis 6473 caractéristiques audio en ont été extraites, afin d'entrainer des modèles de machine learning, dont les performances ont été évaluées à l'aide de métriques habituelles pour la discrimination (AUC, accuracy, precision, recall, F1-score) et le score de Brier pour la calibration. Résultats Au total, 1775 enregistrements ont été analysés (6,5 enregistrements/participant en moyenne). Le modèle de Support Vector Machine a fourni les meilleurs performances (AUC=0.91, accuracy=0,85) avec une bonne calibration (Brier Score=0,11) (Fig. 2). Le biomarqueur vocal dérivé de ces modèles, à partir de la probabilité prédite d’être symptomatique, permettait de discriminer les personnes asymptomatiques des personnes symptomatiqu

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Author Correction: Functional genomics data: privacy risk assessment and technological mitigation

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Nature reviews. Genetics 2022年第4期23卷 259页

作者： Gamze Gürsoy Tianxiao Li Susanna Liu Eric Ni Charlotte M Brannon Mark B Gerstein Computational Biology and Bioinformatics Program Yale University New Haven CT USA. Molecular Biophysics and Biochemistry Yale University New Haven CT USA. Molecular Cellular and Developmental Biology Yale University New Haven CT USA. Statistics and Data Science Yale University New Haven CT USA. Department of Biology Stanford University Stanford CA USA. Computational Biology and Bioinformatics Program Yale University New Haven CT USA. mark@***. Molecular Biophysics and Biochemistry Yale University New Haven CT USA. mark@***. Statistics and Data Science Yale University New Haven CT USA. mark@***. Computer Science Yale University New Haven CT USA. mark@***.

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FAIR Principles:Interpretations and Implementation Considerations

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data Intelligence 2020年第1期2卷 10-29,293-302,322页

作者： Annika Jacobsen Ricardo de Miranda Azevedo Nick Juty Dominique Batista Simon Coles Ronald Cornet Melanie Courtot Merce Crosas Michel Dumontier Chris T.Evelo Carole Goble Giancarlo Guizzardi Karsten Kryger Hansen Ali Hasnain Kristina Hettne Jaap Heringa Rob W.W.Hooft Melanie Imming Keith G.Jeffery Rajaram Kaliyaperumal Martijn GKersloot Christine R.Kirkpatrick Tobias Kuhn Ignasi Labastida Barbara Magagna PeterMcQuilton Natalie Meyers Annalisa Montesanti Mirjam van Reisen Philippe Rocca-Serra Robert Pergl Susanna-Assunta Sansone Luiz Olavo Bonino da Silva Santos Juliane Schneider George Strawn Mark Thompson Andra Waagmeester Tobias Weigel Mark D.Wilkinson Egon L.Willighagen Peter Wittenburg Marco Roos Barend Mons Erik Schultes Leiden University Medical Center Leiden2333 ZAThe Netherlands Institute of Data Science Maastricht UniversityUniversiteitssingel 60Maastricht 6229 ERThe Netherlands Department of Computer Science The University of ManchesterOxford RoadManchester M139PLUK Oxford e-Research Centre Department of Engineering SciencesUniversity of OxfordOxford OX13PJUK School of Chemistry Faculty of Engineering and Physical SciencesUniversity of SouthamptonSO171BJUK Amsterdam UMC University of AmsterdamAmsterdam 1000 GGThe Netherlands European Bioinformatics Institute(EMBL-EBI) HinxtonCambridgeCB101SDUK Harvard University CambridgeMassachusetts 02138USA Department of Bioinformatics–BiGCaT NUTRIMMaastricht UniversityMaastricht 6229 ERThe Netherlands Conceptual and Cognitive Modeling Research Group(CORE) Free University of Bozen-BolzanoBolzano 39100Italy Aalborg University Aalborg DK-9220Denmark Insight Centre for Data Analytics National University of Ireland GalwayH91 TK33Ireland Centre for Digital Scholarship Leiden University LibrariesLeiden2333 ZAThe Netherlands Department of Computer Science Vrije Universiteit AmsterdamDe Boelelaan 11051081 HV AmsterdamThe Netherlands Dutch Techcentre for Life Sciences(DTL) UtrechtThe Netherlands SURF Utrecht 3511 EPThe Netherlands Keith G Jeffery Consultants FaringdonUK Castor EDC Paasheuvelweg 25Wing 5D1105 BPAmsterdamThe Netherlands San Diego Supercomputer Center University of California San DiegoLa JollaCalifornia 92093USA Learning and Research Resources Centre(CRAI) Universitat de Barcelona08007 BarcelonaSpain Environment Agency Austria A-1090 ViennaAustria University of Notre Dame 75004 ParisFrance Health Research Board(HRB) Dublin 2DO2 H638Ireland Liacs Institute of Advanced Computer Science Leiden University2311 GJ LeidenThe Netherlands Czech Technical University in Prague Faculty of Information Technology(FIT CTU)16000 Prague 6Czech Republic GO FAIR International Support&Coordination Office(GFISCO) LeidenThe Netherlands Harvard Catalys

The FAIR principles have been widely cited,endorsed and adopted by a broad range of stakeholders since their publication in *** intention,the 15 FAIR guiding principles do not dictate specific technological implementations,but provide guidance for improving Findability,Accessibility,Interoperability and Reusability of digital *** has likely contributed to the broad adoption of the FAIR principles,because individual stakeholder communities can implement their own FAIR ***,it has also resulted in inconsistent interpretations that carry the risk of leading to incompatible ***,while the FAIR principles are formulated on a high level and may be interpreted and implemented in different ways,for true interoperability we need to support convergence in implementation choices that are widely accessible and(re)-*** introduce the concept of FAIR implementation considerations to assist accelerated global participation and convergence towards accessible,robust,widespread and consistent FAIR *** self-identified stakeholder community may either choose to reuse solutions from existing implementations,or when they spot a gap,accept the challenge to create the needed solution,which,ideally,can be used again by other communities in the ***,we provide interpretations and implementation considerations(choices and challenges)for each FAIR principle.

关键词： FAIR guiding principles FAIR implementation FAIR convergence FAIR communities choices and challenges

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