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8th International Conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Ariansyah, Dedy Isnan, Mahmud Rahutomo, Reza Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Air pollution is a pressing issue in cities, and managing air quality poses a challenge for urban designers and decision-makers. This study proposes a Digital Twin (DT) Smart City integrated with Mixed Reality technology to enhance visualization and collaboration for addressing urban air pollution. The research adopts an applied research approach, with a focus on developing a DT framework. A use case of DT development for Jakarta, the capital of Indonesia, is presented. By integrating air quality data, meteorological information, traffic patterns, and urban infrastructure data, the DT provides a comprehensive understanding of air pollution dynamics. The visualization capabilities of the DT, utilizing Mixed Reality technology, facilitate effective decision-making and the identification of strategies for managing air quality. However, further research is needed to address data management challenges to build a DT for Smart City at scale. © 2023 The Authors. Published by Elsevier B.V.

关键词： Augmented Reality Digital Twin Mixed Reality Smart City

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GWAS with AHP based SNP prioritization approach to identify SNP biomarkers for Alzheimer's disease

GWAS with AHP based SNP prioritization approach to identify ...

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2012 7th International Symposium on Health Informatics and bioinformatics, HIBIT 2012

作者： Kadioǧlu, Onat Üstünkar, Gürkan Son, Yeşim Aydin Middle East Technical University Bioinformatics Graduate Program Ankara Turkey Middle East Technical University Informatics Institute Department of Information Science Ankara Turkey Middle East Technical University Informatics Institute Department of Health Informatics Ankara Turkey

ISBN: (纸本)9781467308786

Genome wide association studies (GWAS) aim to identify genomic variance associated with certain disease conditions. Major bottleneck of standard GWAS approaches are the prioritization and subset selection after the statistically significant SNPs are determined. Our group has recently developed an analysis pipeline, where p-value and combined p value statistics are integrated with the novel AHP based SNP prioritization algorithm for the detailed analysis of associated SNPs considering statistical significance and biological relevance. Following this pipeline, we have done the GWAS of Alzheimer's Disease (AD) on ADNI SNP genotyping data set where 148 AD cases and 182 controls are investigated. Among the top 100 SNPs 18 of them mapped to AD linked genes. Additionally further analysis of the gene and pathway results suggested new associations, providing basis for new hypothesis for the AD Biomarker research. © 2012 IEEE.

关键词： Genes

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Machine Learning Approaches in Detecting Autism Spectrum Disorder 8

Machine Learning Approaches in Detecting Autism Spectrum Dis...

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8th International Conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Daniel Dominic, Nicholas Cenggoro, Tjeng Wawan Pardamean, Bens Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Early detection of Autism Spectrum Disorder (ASD) needs to be increased to prevent further adverse impacts. Thus, the classification between ASD and Typically Development (TD) individuals is an intriguing task. This review study has collected 26 related papers to answer four research questions, i.e., what are the most used data inputs, brain atlases, and machine learning models for ASD classification, as also to discover the significant parts of the brain correlated with the ASD. It was eventually found that functional connectivity matrix, Support Vector Machine, and CC200 are the most frequently used data input, model, and brain atlas, respectively. Researchers also concluded that the posterior temporal fusiform cortex, intracalcarine cortex, cuneal cortex, subcallosal cortex, occipital pole, and lateral occipital cortex are the brain regions highly correlated with ASD. © 2023 The Authors. Published by Elsevier B.V.

关键词： autism spectrum disorder brain atlas brain region machine learning

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Systematic Literature Review: An Intelligent Pulmonary TB Detection from Chest X-Rays 1

Systematic Literature Review: An Intelligent Pulmonary TB De...

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1st International Conference on Computer Science and Artificial Intelligence, ICCSAI 2021

作者： Jimmy Cenggoro, Tjeng Wawan Pardamean, Bens Bina Nusantara University BINUS Graduate Program-Master of Computer Science Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Tuberculosis (TB) is one of the top ten reasons for death from an infectious agent. Although TB is curable and preventable, delay in diagnosis and treatment can lead the patient to death. Advancements in computer-Aided diagnosis (CAD), particularly in medical images classification, significantly contribute to early TB detection. The current state-of-Art CAD for medical images classification applications using a method base on deep learning techniques. The problem faced in this deep learning technique is that, in general, it only uses a single modal for the model. In contrast, in medical practice, the data used for TB analysis not only focuses on images but also includes clinical data such as demographics, patient assessments, and lab test results. This systematic literature review describes different deep learning methods using single modal or multimodal techniques that combined images with other clinical data. We conducted a systematic search on Springer, PubMed, ResearchGate, and google scholar for original research leveraging deep learning for Pulmonary TB detection. © 2021 IEEE.

关键词： Computer aided diagnosis

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A bioinformatics Analysis and Homology 3D Modeling of Dihydroxy-orotate-dehydrogenase of Plasmodium ovale

A Bioinformatics Analysis and Homology 3D Modeling of Dihydr...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Nirwantono, Rudi Assidqi, Khoirunnisa Hidayat, Alam Ahmad Pebrianto Trinugroho, Joko Pardamean, Bens Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Food Biotechnology Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Departement Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

The infection of Plasmodium vivax is relatively less virulent than the deathliest Plasmodium falciparum. However, it still can lead to a fatal case and often induces recurring malaria due to dormant parasites in the liver. Thus, the research to study the drug to treat Plasmodium vivax is essential, where the enzyme dihydroorotate dehydrogenase (DHODH) has recently become a new drug target. However, the drug-enzyme interaction study has only recently been conducted in Plasmodium falciparum (pfDHODH) due to the lack of the 3D structure of enzyme DHODH from Plasmodium vivax that is crucial for the study. Therefore, this study aimed to perform the modelling study of Plasmodium vivax DHODH (PvDHODH) to create a 3D structure as a basis for drug-protein interaction study in upcoming studies. Sequence pvDHODH (Accession: SCO68359.1) was used in homology modelling using Modeller 10.2 with the crystal structure of DHODH from DHODH (Accession: 7KZY) as a template. Overall, the model generated from the homology modelling was considered a good model, and the 3D structure was close to the native state according to several parameters, including DOPE score, GA341 score, QMEAN4 value, 3D-1D score, ERRAT2 score, and ProSA score. Ramachandran plot also revealed that almost all amino acids were distributed in the desirable and allowed area (99.1%), and only 0.9% were in generously allowed regions. Superimposition of the model with the template also indicated that the model has an almost similar structure and amino acids positioning, including in the binding pocket and active site. Therefore, the model can be used for downstream analysis in drug-protein interaction studies. Nevertheless, some improvements can still be performed to upgrade the quality of the model by deleting unaligned residues on the C-terminal and realigning the residues at the doubting site. © 2023 IEEE.

关键词： Amino acids

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The largest genome-wide association study for breast cancer in Taiwanese Han population

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Breast Cancer Research and Treatment 2024年第2期203卷 291-306页

作者： Hsu, Yu-Ching Chen, Hung-Lin Cheng, Chi-Fung Chattopadhyay, Amrita Chen, Pei-Shan Lin, Che-Chen Chiang, Hsiu-Yin Liu, Ting-Yuan Huang, Chi-Hao Kuo, Chin-Chi Chuang, Eric Y. Lu, Tzu-Pin Tsai, Fuu-Jen Bioinformatics Program Taiwan International Graduate Program National Taiwan University Taipei Taiwan Bioinformatics Program Institute of Statistical Science Taiwan International Graduate Program Academia Sinica Taipei Taiwan Institute of Health Data Analytics and Statistics Department of Public Health College of Public Health National Taiwan University Taipei Taiwan Big Data Center China Medical University Hospital Taichung Taiwan Center for Translational Genomic Research Department of Medical Research China Medical University Hospital Taichung Taiwan Million-Person Precision Medicine Initiative Department of Medical Research China Medical University Hospital Taichung Taiwan Division of Breast Surgery Department of Surgery China Medical University Hospital Taichung Taiwan Division of Nephrology Department of Internal Medicine China Medical University Hospital Taichung Taiwan Department of Medical Research China Medical University Hospital Taichung Taiwan Bioinformatics and Biostatistics Core Center of Genomic and Precision Medicine National Taiwan University Taipei Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Biomedical Technology and Device Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan Genetic Center Department of Medical Research China Medical University Hospital Taichung Taiwan

Purpose: Breast cancer is a molecularly heterogeneous disease, and multiple genetic variants contribute to its development and prognosis. Most of previous genome-wide association studies (GWASs) and polygenic risk scores (PRSs) analyses focused on studying breast cancers of Caucasian populations, which may not be applicable to other population. Therefore, we conducted the largest breast cancer cohort of Taiwanese population to fill in the knowledge gap. Methods: A total of 152,534 Participants recruited by China Medical University Hospital between 2003 and 2019 were filtered by several patient selection criteria and GWAS quality control steps, resulting in the inclusion of 2496 cases and 9984 controls for this study. We then conducted GWAS for all breast cancers and PRS analyses for all breast cancers and the four breast cancer subtypes, including luminal A, luminal B, basal-like, and HER2-enriched. Results: The GWAS analyses identified 113 SNPs, 50 of which were novel. The PRS models for all breast cancers and the luminal A subtype showed positively correlated trends between the PRS and the risk of developing breast cancer. The odds ratios (95% confidence intervals) for the groups with the highest PRS in all breast cancers and the luminal A subtype were 5.33 (3.79–7.66) and 3.55 (2.13–6.14), respectively. Conclusion: In summary, we explored the association of genetic variants with breast cancer in the largest Taiwanese cohort and developed two PRS models that can predict the risk of developing any breast cancer and the luminal A subtype in Taiwanese women. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Breast cancer Genome-wide association study Polygenic risk score Taiwanese population

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Evolving hypernetwork classifiers for microRNA expression profile analysis

Evolving hypernetwork classifiers for microRNA expression pr...

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Congress on Evolutionary Computation

作者： Sun Kim Soo-Jin Kim Byoung-Tak Zhang The Center for Bioinformatics Technology Graduate Program in Bioinformatics Seoul National University Seoul South Korea The School of Computer Science and Engineering Seoul National University Seoul South Korea

High-throughput microarrays inform us on different outlooks of the molecular mechanisms underlying the function of cells and organisms. While computational analysis for the microarrays show good performance, it is still difficult to infer modules of multiple co-regulated genes. Here, we present a novel classification method to identify the gene modules associated with cancers from microarray data. The proposed approach is based on 'hypernetworks', a hypergraph model consisting of vertices and weighted hyperedges. The hypernetwork model is inspired by biological networks and its learning process is suitable for identifying interacting gene modules. Applied to the analysis of microRNA (miRNA) expression profiles on multiple human cancers, the hypernetwork classifiers identified cancer-related miRNA modules. The results show that our method performs better than decision trees and naive Bayes. The biological meaning of the discovered miRNA modules has been examined by literature search.

关键词： Cancer Gene expression Biological system modeling Data analysis Support vector machines Support vector machine classification Evolutionary computation Humans Decision trees Diseases

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Correction to: Droplet Hi-C enables scalable, single-cell profiling of chromatin architecture in heterogeneous tissues (Nature Biotechnology, (2024), 10.1038/s41587-024-02447-1)

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Nature Biotechnology 2025年

作者： Chang, Lei Xie, Yang Taylor, Brett Wang, Zhaoning Sun, Jiachen Armand, Ethan J. Mishra, Shreya Xu, Jie Tastemel, Melodi Lie, Audrey Gibbs, Zane A. Indralingam, Hannah S. Tan, Tuyet M. Bejar, Rafael Chen, Clark C. Furnari, Frank B. Hu, Ming Ren, Bing Department of Cellular and Molecular Medicine University of California San Diego La JollaCA United States Biomedical Sciences Graduate Program University of California San Diego La JollaCA United States Medical Scientist Training Program University of California San Diego La JollaCA United States Department of Quantitative Health Sciences Lerner Research Institute Cleveland Clinic Foundation ClevelandOH United States Systems Biology and Bioinformatics PhD Program Case Western Reserve University School of Medicine ClevelandOH United States Bioinformatics and Systems Biology Program University of California San Diego La JollaCA United States Moores Cancer Center UC San Diego La JollaCA United States Department of Neurosurgery University of Minnesota MinneapolisMN United States Department of Medicine University of California San Diego School of Medicine La JollaCA United States Center for Epigenomics Institute for Genomic Medicine School of Medicine University of California San Diego La JollaCA United States

Correction to: Nature Biotechnologyhttps://***/10.1038/s41587-024-02447-1, published online 18 October 2024. In the version of the article initially published, in the Methods section, both mentions of "MboII" should have read "MboI (NEB, R0147M)" and have now been corrected in the HTML and PDF versions of the article. © The Author(s) 2025.

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A new approach to predict interactions between integral membrane proteins in yeast

A new approach to predict interactions between integral memb...

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Congress on Evolutionary Computation

作者： Kelvin Xi Zhang B.F. Francis Ouellette Ontario Institute for Cancer Research Toronto ONT Canada Graduate Program in Bioinformatics University of British Columbia BC Canada

Protein-protein interactions (PPIs) play an extremely important role in performing a variety of biological functions. The interactomes of several model organisms including budding yeast Saccharomyces cerevisiae have recently been studied using experimental techniques such as the yeast two-hybrid assay. However, these techniques are generally biased against integral membrane proteins due to their intrinsic limitations. Given the fact that the interactions between integral membrane proteins cover a large fraction of the whole interactome, we report a study of predicting interactions between integral membrane proteins in yeast by a quantitative model We integrate protein-protein interaction and domain-domain interaction (DDI) data from disparate sources and apply a log likelihood scoring method on all putative integral membrane proteins in yeast to predict their interactions based on a cut-off threshold. We show that our approach improves on other predictive approaches when tested on a ldquogold-standardrdquo data set and achieves 74.6% true positive rate at the expense of 0.43% false positive rate. Furthermore, we find that two integral membrane proteins are more likely to interact with each other if they share more common interaction partners. This study allows us to reach a more extensive understanding of the yeast integral membrane proteins from a network view, which also complements the previous prediction approaches based on the genomic context.

关键词： Evolutionary computation

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Mechanically-driven growth and competition in a Voronoi model of tissues

arXiv

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arXiv 2024年

作者： Brézin, Louis Korolev, Kirill S. Department of Physics Graduate Program in Bioinformatics Biological Design Center Boston University BostonMA02215 United States

The mechanisms leading cells to acquire a fitness advantage and establish themselves in a population are paramount to understanding the development and growth of cancer. Although there are many works that study separately either the evolutionary dynamics or the mechanics of cancer, little has been done to couple evolutionary dynamics to mechanics. To address this question, we study a confluent model of tissue using a Self-Propelled Voronoi (SPV) model with stochastic growth rates that depend on the mechanical variables of the system. The SPV model is an out-of-equilibrium model of tissue derived from an energy functional that has a jamming/unjamming transition between solid-like and liquid-like states. By considering several scenarios of mutants invading a resident population in both phases, we determine the range of parameters that confer a fitness advantage and show that the preferred area and perimeter are the most relevant ones. We find that the liquid-like state is more resistant to invasion and show that the outcome of the competition can be determined from the simulation of a non-growing mixture. Moreover, a mean-field approximation can accurately predict the fate of a mutation affecting mechanical properties of a cell. Our results can be used to infer evolutionary dynamics from tissue images, understand cancer-suppressing effects of tissue mechanics, and even search for mechanics-based therapies. © 2024, CC BY.

关键词： Tissue

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