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International Symposium on Health Informatics and bioinformatics (HIBIT)

作者： Levent Çarkacwğlu Ceyhun Gedikoğlu Yeşim Aydin Son Bioinformatics Graduate Program Middle East Technical University 06800 Ankara Turkey Bioinformatics Graduate Program Middle East Technical University Ankara Turkey Health Informatics Department Middle East Technical University Ankara Turkey

Single Nucleotide Polymorphisms (SNP) is a DNA sequence variation that occurs when a single nucleotide differs between members of a biological species or paired chromosomes in an individual. Studies on SNP data are important for scientists to shed light on identifying genetic variations underlying complex diseases. While recent advances in high-throughput genotyping technologies are resulting in data accumulation at large scales, the need for collection and service of this data under a standard format and globally normalized and structured metadata that houses the structured SNP data is becoming essential. Here, we present iSNP that is a regularly updated, machine curated database that holds SNP and its associated metadata from publicly available databases under a structured standard format that can be efficiently utilized within different applications.

关键词： Databases bioinformatics Genomics Humans Diseases

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Using a Recurrent Neural Network Model for Classification of Tweets Conveyed Influenza-related Information 1

Using a Recurrent Neural Network Model for Classification of...

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1st International Workshop on Digital Disease Detection using Social Media, DDDSM 2017, co-located with the 8th International Joint Conference on Natural Language Processing, IJCNLP 2017

作者： Wang, Chen-Kai Singh, Onkar Tang, Zhao-Li Dai, Hong-Jie Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang-Ming University Taipei Taiwan Department of Computer Science and Information Engineering National Taitung University Taitung Taiwan Interdisciplinary Program of Green and Information Technology National Taitung University Taitung Taiwan

ISBN: (纸本)9781948087070

Traditional disease surveillance systems depend on outpatient reporting and virological test results released by hospitals. These data have valid and accurate information about emerging outbreaks but it's often not timely. In recent years the exponential growth of users getting connected to social media provides immense knowledge about epidemics by sharing related information. Social media can now flag more immediate concerns related to outbreaks in real time. In this paper we apply the long short-term memory recurrent neural network (RNN) architecture to classify tweets conveyed influenza-related information and compare its performance with baseline algorithms including support vector machine (SVM), decision tree, naive Bayes, simple logistics, and naive Bayes multinomial. The developed RNN model achieved an F-score of 0.845 on the MedWeb task test set, which outperforms the F-score of SVM without applying the synthetic minority oversampling technique by 0.08. The F-score of the RNN model is within 1% of the highest score achieved by SVM with oversampling technique. © 2017 AFNLP

关键词： Support vector machines

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Expression of G protein-coupled receptors and related proteins in HEK293, AtT20, BV2, and N18 cell lines as revealed by microarray analysis

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BMC Genomics 2011年第1期12卷 1-14页

作者： Atwood, Brady K. Lopez, Jacqueline Wager-Miller, James Mackie, Ken Straiker, Alex Department of Psychological and Brain Sciences The Gill Center for Biomolecular Science Indiana University Bloomington IN United States Center for Genomics and Bioinformatics Indiana University Bloomington IN United States Graduate Program in Neurobiology and Behavior University of Washington Seattle WA United States

Background: G protein coupled receptors (GPCRs) are one of the most widely studied gene superfamilies. Thousands of GPCR research studies have utilized heterologous expression systems such as human embryonic kidney cells (HEK293). Though often treated as 'blank slates', these cell lines nevertheless endogenously express GPCRs and related signaling proteins. The outcome of a given GPCR study can be profoundly influenced by this largely unknown complement of receptors and/or signaling proteins. Little easily accessible information exists that describes the expression profiles of the GPCRs in cell lines. What is accessible is often limited in scope - of the hundreds of GPCRs and related proteins, one is unlikely to find information on expression of more than a dozen proteins in a given cell line. Microarray technology has allowed rapid analysis of mRNA levels of thousands of candidate genes, but though often publicly available, the results can be difficult to efficiently access or even to ***: To bridge this gap, we have used microarrays to measure the mRNA levels of a comprehensive profile of non-chemosensory GPCRs and over a hundred GPCR signaling related gene products in four cell lines frequently used for GPCR research: HEK293, AtT20, BV2, and ***: This study provides researchers an easily accessible mRNA profile of the endogenous signaling repertoire that these four cell lines possess. This will assist in choosing the most appropriate cell line for studying GPCRs and related signaling proteins. It also provides a better understanding of the potential interactions between GPCRs and those signaling proteins. © 2011 Atwood et al;licensee BioMed Central Ltd.

关键词： HEK293 Cell AtT20 Cell GPCR Signaling Frizzle Receptor Related Signaling Protein

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Structural insights into TDP-43 in nucleic-acid binding and domain interactions

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Nucleic Acids Research 2009年第6期37卷 1799-1808页

作者： Kuo, Pan-Hsien Doudeva, Lyudmila G. Wang, Yi-Ting Shen, Che-Kun James Yuan, Hanna S. Institute of Bioinformatics and Structural Biology National Tsing Hua University Taipei Taiwan Institute of Molecular Biology National Taiwan University Taipei Taiwan Taiwan International Graduate Program Chemical Biology and Molecular Biophysics National Taiwan University Taipei Taiwan Graduate Institute of Biochemistry and Molecular Biology National Taiwan University Taipei Taiwan

TDP-43 is a pathogenic protein: its normal function in binding to UG-rich RNA is related to cystic fibrosis, and inclusion of its C-terminal fragments in brain cells is directly linked to frontotemporal lobar degeneration (FTLD) and amyotrophic lateral sclerosis (ALS). Here we report the 1.65 Å crystal structure of the C-terminal RRM2 domain of TDP-43 in complex with a single-stranded DNA. We show that TDP-43 is a dimeric protein with two RRM domains, both involved in DNA and RNA binding. The crystal structure reveals the basis of TDP-43's TG/ UG preference in nucleic acids binding. It also reveals that RRM2 domain has an atypical RRM-fold with an additional β-strand involved in making protein-protein interactions. This self association of RRM2 domains produced thermal-stable RRM2 assemblies with a melting point greater than 85°C as monitored by circular dichroism at physiological conditions. These studies thus characterize the recognition between TDP-43 and nucleic acids and the mode of RRM2 self association, and provide molecular models for understanding the role of TDP-43 in cystic fibrosis and the neurodegenerative diseases related to TDP-43 proteinopathy. © 2009 The Author(s).

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A Hydrodynamic Analysis of Water System in Dadahup Swamp Irrigation Area 1

A Hydrodynamic Analysis of Water System in Dadahup Swamp Irr...

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

作者： Purboseno, Sentot Suparyanto, Teddy Hidayat, Alam Ahmad Pardamean, Bens Institute of Agriculture STIPER Department of Agricultural Engineering Yogyakarta55283 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center BINUS Graduate Program-Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Dadahup Swamp Irrigation Area (DIR) in Kapuas Regency, Central Kalimantan is developed for agricultural activities to provide food security after the pandemic. The water system consists of various channels, gates, and a flood embankment structure, which is also connected to three different rivers: Barito River, Mangkatif River, and Kapuas Mumpung River. To quantify flood risk assessments in the area, we perform a hydrodynamic analysis using the unsteady flow method via HEC-RAS software version 4.1.0. We use geometrical profiles from topography measurements and the outer boundary of the Dadahup DIR as input for the hydrologic model. Our results estimate the maximum surface water level from both cross and longitudinal sections in the channels and find that several points near the flood embankment area are prone to flood events. Our modeling approach provides a preliminary assessment for the local government to formulate a flood mitigation plan and policy in the area. © 2021 IEEE.

关键词： Rivers

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Comparison of K-Nearest Neighbor and Support Vector Regression for Predicting Oil Palm Yield 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Pardamean, Bens Suparyanto, Teddy Sinaga, Gokma Sahat Tua Elwirehardja, Gregorius Natanael Firmansyah, Erick Ginting, Candra Mawandha, Hangger Gahara Putra, Dian Pratama Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program—Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Faculty of Agriculture Institute of Agriculture STIPER Yogyakarta55282 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

With the high demand for oil palm production, implementations of Machine Learning (ML) technologies to provide accurate predictions and recommendations to assist oil palm plantation management tasks have become beneficial, such as in predicting annual oil palm. However, different geographical and meteorological conditions may result in different scales of influence for each variable. In this research, K-Nearest Neighbors (KNN) and Support Vector Regression (SVR) were used in predicting oil palm yield based on data collected in Riau, Indonesia. Pearson’s correlation coefficient was also calculated in selecting the input features for the models, whereas normalization and standardization were used in scaling the data. By setting the minimum absolute correlation threshold to 0.1 and using standardization, both models managed to obtain more than 0.81 R2, with SVR obtaining the overall best performance with 0.8709 R2, 1.372 MAE, and 1.8025 RMSE without hyperparameter fine-tuning. It was also discovered that the oil palm yield in the previous year is the variable with the most influence in estimating oil palm yield in the current year, followed by the number of plants and soil types. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Machine learning

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A virtual positioning system for external beam radiotherapy

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Virtual Reality 2023年第3期27卷 2569-2582页

作者： Sung, Wen-Hsu Jiang, Ching-Fen Su, Tai-Sin Sun, Shuh-Ping Department of Physical Therapy and Assistive Technology National Yang Ming Chiao Tung University Taipei Taiwan Graduate Program of Smart Healthcare and Bioinformatics I-Shou University Kaohsiung Taiwan Department of Biomedical Engineering I-Shou University Kaohsiung Taiwan Department of Digital Media Design I-Shou University Kaohsiung Taiwan

In radiation therapy, positioning patients to ensure the accuracy of the setup of a procedure is a routine and labour-intensive process that substantially determines the efficacy of treatment. In this study, we propose a virtual positioning system that can simulate the positioning process with a visible beam path under the broad view of a life-like patient-positioning platform to obviate problems with excessively narrow view. This system integrates image processing, computer graphics, and virtual reality to encompass a 3D treatment target reconstructed from medical images of different modalities in a virtual scene. An innovative evaluation method is further proposed to verify the efficacy of the system using a full-scale 3D solid anthropometric model, and a treatment target showed an accuracy of 99% in calibration and a mean compatibility of 95% with an actual measurement. The proposed virtual positioning system provides a training and education platform for radiation therapy. The methodology through which the system was developed is also disclosed as a promising approach to improve the efficiency and safety of the position verification process for existing systems. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

关键词： Radiotherapy

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eXpanda: An integrated platform for network analysis and visualization

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In Silico Biology 2007年第2期7卷 141-144页

作者： Negishi, Yoshiteru Nakamura, Hiroyuki Yachie, Nozomu Saito, Rintaro Tomita, Masaru Institute for Advanced Biosciences Keio University Fujisawa 252-8520 Japan Bioinformatics Program Graduate School of Media and Governance Keio University Fujisawa 252-8520 Japan Department of Environmental Information Keio University Fujisawa 252-8520 Japan

Analysis and visualization of biological networks, such as protein-protein and protein-DNA interactions, are crucially important toward obtaining a thorough understanding of living systems. Here, we present an integrative software platform, eXpanda, which enables an analysis of a very broad range of biological networks, with a special focus on the extraction of characteristic topologies which potentially function as units in the networks. eXpanda is provided as a Perl library which gives full-automatic connections to various biological databases via a Perl programmable interface and can perform topological analysis based on graph theory. The results of these analyses are visualizable by vector graphics. eXpanda is under GNU General Public License. Software package, detailed documentations, source codes, and some sample scripts are downloadable at http://***/expanda/. © 2007 IOS Press. All rights reserved.

关键词： Interactome Network analysis Network visualization Perl library Scalable Vector Graphics (SVG)

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bioinformatics exploration into potential mechanisms of Calamus draco for colorectal cancer treatment 9th

Bioinformatics exploration into potential mechanisms of Cala...

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9th International Conference on Computer Science and Computational Intelligence, ICCSCI 2024

作者： Kenneth, Christian Nirwantono, Rudi Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

As one of the cancer types with the highest incidence rates, colorectal cancer (CRC) would benefit from treatments with fewer side effects and reduced treatment-resistant potential. One of the options is to harness the anti-CRC potential of natural products. Previous studies have shown that Calamus draco exudate, dragon's blood, has anticancer activity in liver cancer and acute myeloid leukemia, but its bioactivity has not been studied in CRC. Here we conduct a bioinformatics study based on network pharmacology to explore the anti-CRC potential and mechanism of C. draco-derived compounds. The bioinformatics pipeline is composed of compound and target collection, biological network evaluation, and enrichment analysis. We found that there are 43 bioactive compounds from C. draco targeting 91 CRC-related targets, of which most compounds target MEN1, PTGS2, and IDH1. Further analyses show that the targets of C. draco are involved in the cellular response to hypoxia. By inhibiting those targets, C. draco bioactive compounds can potentially hinder angiogenesis and increase treatment response efficacy. © 2024 The Authors.

关键词： Lung cancer

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Toward Learning Factory for Industry 4.0: Virtual Reality (VR) for Learning Human–Robot Collaboration 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Ariansyah, Dedy Colombo, Giorgio Pardamean, Bens Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta11480 Indonesia Department of Mechanical Engineering Politecnico di Milano Milan20156 Italy Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Industry 4.0 will bring not only transformation to the manufacturing technologies but also to the profile of the workforce. Education system should be revised to prepare the future graduates embracing the knowledge of the ongoing revolution. Initiatives on the modification of curriculum and tools to deliver the concepts of Industry 4.0 must be taken. This paper examines limitations of current engineering tools and proposes a virtual reality (VR)-based learning platform to support the teaching and learning activity for Industry 4.0, focusing on the design of Human–robot Collaboration (HRC). The development process began with the identification of the general Intended Learning Outcomes (ILOs) which were extracted from the review of the HRC design issues, followed by the evaluation of the current teaching tool. From these results, the new requirements of the tools to achieve ILOs are redefined. The implementation of the system is demonstrated to show the feasibility of the proposed learning platform. This workflow can serve as initial development of another learning platform for the other innovative concepts that form the building blocks of Industry 4.0. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Industry 4.0

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