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3rd International Conference on Eco Engineering Development, ICEED 2019

作者： Darmalim, Usin Darmalim, Ferdinan Darmalim, Sutristo Hidayat, Alam Ahmad Budiarto, Arif Mahesworo, Bharuno Pardamean, Bens Mega Medica Pharmaceuticals Jakarta11840 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Channa striata or the striped snakehead fish is one of snakehead fish species which inhabits all types of freshwater bodies distributed across Asian countries. Because this fish is known to have higher albumin fraction (64.61%) of protein and other economic values, domestication, and cultivation of this fish has been done in many Asian countries such as Indonesia, China, Malaysia, Thailand, Bangladesh, and India. Environmental factors such as temperature, water pH, dissolved oxygen, total dissolved solids, and turbidity are important parameters must be considered inbreeding and growing this type of fish. The aim of this paper is to propose an IoT solution to automatically monitor these environmental factors. It is designed with affordable and open-source electrical components to provide a cost-efficient solution for farmers. Five sensors are used to measure each parameter. A web application prototype is also presented as a companion application for the users to get useful information from the IoT device. It is developed using a Pythonframework. By accessing this web application, the users can immediately detect any abnormal conditions of the pond. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Fish

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Translation dysregulation in cancer as a source for targetable antigens

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Cancer Cell 2025年第5期43卷 823-840.e18,20页

作者： Weller, Chen Bartok, Osnat McGinnis, Christopher S. Palashati, Heyilimu Chang, Tian-Gen Malko, Dmitry Shmueli, Merav D. Nagao, Asuteka Hayoun, Deborah Murayama, Ayaka Sakaguchi, Yuriko Poulis, Panagiotis Khatib, Aseel Erlanger Avigdor, Bracha Gordon, Sagi Cohen Shvefel, Sapir Zemanek, Marie J. Nielsen, Morten M. Boura-Halfon, Sigalit Sagie, Shira Gumpert, Nofar Yang, Weiwen Alexeev, Dmitry Kyriakidou, Pelgia Yao, Winnie Zerbib, Mirie Greenberg, Polina Benedek, Gil Litchfield, Kevin Petrovich-Kopitman, Ekaterina Nagler, Adi Oren, Roni Ben-Dor, Shifra Levin, Yishai Pilpel, Yitzhak Rodnina, Marina Cox, Jürgen Merbl, Yifat Satpathy, Ansuman T. Carmi, Yaron Erhard, Florian Suzuki, Tsutomu Buskirk, Allen R. Olweus, Johanna Ruppin, Eytan Schlosser, Andreas Samuels, Yardena Department of Molecular Cell Biology Weizmann Institute of Science Rehovot 7610001 Israel Department of Pathology Stanford University Stanford 94305 CA United States Parker Institute for Cancer Immunotherapy San Francisco 94129 CA United States Department of Cancer Immunology Institute for Cancer Research Oslo University Hospital Oslo 0379 Norway Precision Immunotherapy Alliance University of Oslo Oslo Norway Cancer Data Science Laboratory Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda 20892 MD United States Department of Systems Immunology Weizmann Institute of Science Rehovot 7610001 Israel Department of Chemistry and Biotechnology of Engineering The University of Tokyo Tokyo 113-8656 Japan Department of Physical Biochemistry Max Planck Institute for Multidisciplinary Sciences Göttingen 37077 Germany Department of Pathology Sackler School of Medicine Tel Aviv University Tel Aviv 69978 Israel Department of Immunology and Regenerative Biology Weizmann Institute of Science Rehovot 7610001 Israel Computational Systems Biochemistry Research Group Max-Planck Institute of Biochemistry Martinsried 82152 Germany Department of Veterinary Resources Weizmann Institute of Science Rehovot 7610001 Israel Tissue Typing and Immunogenetics Unit Hadassah Hebrew University Hospital Jerusalem 9112102 Israel CRUK Lung Cancer Centre of Excellence University College London Cancer Institute London WC1E 6DD United Kingdom Tumour Immunogenomics and Immunosurveillance Laboratory University College London Cancer Institute London WC1E 6DD United Kingdom Department of Life Science Core Facilities Weizmann Institute of Science Rehovot 7610001 Israel Bioinformatics Unit Department of Life Sciences Core Facilities Weizmann Institute of Science Rehovot 7610001 Israel de Botton Institute for Protein Profiling the Nancy and Stephen Grand Israel National Center for Personalized Medicine Weizmann Institute of Science Rehovot

Aberrant peptides presented by major histocompatibility complex (MHC) molecules are targets for tumor eradication, as these peptides can be recognized as foreign by T cells. Protein synthesis in malignant cells is dysregulated, which may result in the generation and presentation of aberrant peptides that can be exploited for T cell-based therapies. To investigate the role of translational dysregulation in immunological tumor control, we disrupt translation fidelity by deleting tRNA wybutosine (yW)-synthesizing protein 2 (TYW2) in tumor cells and characterize the downstream impact on translation fidelity and immunogenicity using immunopeptidomics, genomics, and functional assays. These analyses reveal that TYW2 knockout (KO) cells generate immunogenic out-of-frame peptides. Furthermore, Tyw2 loss increases tumor immunogenicity and leads to anti-programmed cell death 1 (PD-1) checkpoint blockade sensitivity in vivo. Importantly, reduced TYW2 expression is associated with increased response to checkpoint blockade in patients. Together, we demonstrate that defects in translation fidelity drive tumor immunogenicity and may be leveraged for cancer immunotherapy. © 2025 The Author(s)

关键词： cancer immunopeptidome immune checkpoint blockade neoantigens non-canonical peptides translation fidelity

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database Management System Design Improvement for Child Stunting data Collection in Multiple Observation Areas 7

Database Management System Design Improvement for Child Stun...

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7th International Conference on Information Management and Technology, ICIMTech 2022

作者： Rahutomo, Reza Elwirehardja, Gregorius Natanael Dominic, Nicholas Caesario, Benedict Pardamean, Bens School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

The launch of StuntingDB is believed to enliven the role of database management systems (DBMS) in Indonesia's stunting research. However, a novelty in stunting data management that enables parallel project activation in multiple observation areas is necessary. Several improved functionalities in StuntingDB must be designed to prevent the high demand for accessibility in the middle of stunting research. Connolly and Begg's principle of database design is adopted along with Software Development Life Cycle's Waterfall model for creating a continuous development of the system. This research produces a new database design in the form of Entity Relationship Diagram (ERD) and Use Case Diagram to help system developers realizing a new version of StuntingDB for stunting research in several observation areas. © 2022 IEEE.

关键词： database systems

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Performance Evaluation of Coffee Bean Binary Classification Through Deep Learning Techniques 1

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

作者： Islamy, Fajrul Muchtar, Kahlil Arnia, Fitri Dawood, Rahmad Febriana, Alifya Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Master of Computer Science Program BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Coffee beans are one of the high-value commodities in Indonesia, but the sorting method for the quality of coffee beans still uses visual methods and sieves with mechanical machines. This study aims to provide an alternative to classifying coffee beans using Deep Learning algorithms, such as the ResNet-18 and MobileNetV2. The dataset used is USK-COFFEE which consists of two classes, the normal coffee bean class, and the defect coffee bean class. The models were trained using the balanced and imbalanced datasets, meaning that two scenarios were performed. From the results of this study, the accuracy value for the ResNet-18 model was 91.50% when trained on the balanced dataset and 90.87% on the imbalanced dataset. It outperformed the MobileNetV2 architecture, which only achieved 0.8125 and 0.8112 accuracy scores when trained on the balanced and imbalanced datasets, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Computer vision

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1997-2017 Leading Causes of Death Information Due to Diabetes, Neoplasms, and Diseases of the Circulatory System, Issues Cautionary Weight-Related Lesson to the US Population at Large

1997-2017 Leading Causes of Death Information Due to Diabete...

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2019 International Conference on Engineering, science, and Industrial Applications, ICESI 2019

作者： D'Souza, Malcolm J. Li, Riza C. Gannon, Morgan L. Wentzien, Derald E. Mathematics Data Science Wesley College DoverDE19901 United States Center for Bioinformatics and Computational Biology University of Delaware NewarkDE19711 United States Advanced Plastic Surgery Center DE19711 United States

ISBN: (纸本)9781728121741

In the US, cardiovascular disease, cancer, and diabetes are in the top ten leading causes of death categories. The diseases compromise US life-expectancy and account for significant US health-care costs. This observational study investigates the US population's 1997-2017 Centers for Disease Control and Prevention (CDC) WONDER ICD-10 mortality records to extract the prevalence rates for leading causes of death by diabetes, neoplasms (cancers), and diseases of the circulatory system. The variables of race and age are examined for each disease in order to evaluate demographic and age-group risks. To document the public health burden from these three chronic conditions, mortality data from CDC WONDER was analyzed using MS-Excel and Statistical Analysis System (SAS) software. The general trend of deaths by diabetes, neoplasms, and diseases of the circulatory system has been progressively decreasing nationally;however, a significantly higher trend in mortality rates is observed for the Black or African American populations. Furthermore, over the 1997-2017 observational period, the crude mortality rates for the 45-54 (middle-age) and lower age-groups are below national mortality rate averages but are troublingly increasing for diabetes and notably, for the diseases of the circulatory system, the (younger) 25-34 age-group had a crude mortality rate increase of 6.78%. © 2019 IEEE.

关键词： Diseases

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South jakarta's air quality using PM 2.5 data at the beginning of COVID-19 Restriction 6

South jakarta's air quality using PM 2.5 data at the beginni...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Rahutomo, Reza Purwandari, Kartika Hidayat, Alam Ahmad Pardamean, Bens Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

As soon as the lockdown was established in Jakarta, Indonesia, people experienced a change in Jakarta's air quality. This research was conducted to answer several challenges such as: where to find Indonesia's air quality open data platforms, how to handle the acquired data with SQL technology, and how to validate whether the air quality of Jakarta was significantly changed from a period to another or not. With particulate matter 2.5 (PM 2.5) data acquired by The United States Diplomatic Post in South Jakarta, a data visualization was generated to describe the fluctuation of PM 2.5 from February 29th to June 29th. By conducting a two-sample t-test, a significant change in South Jakarta's air quality was revealed. The test concluded that South Jakarta's air quality got worse even after a lockdown was established. © 2021 IEEE.

关键词： COVID-19

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ConvNeXt Model for Breast Cancer Image Classification 6

ConvNeXt Model for Breast Cancer Image Classification

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Setiawan, Devin Karnyoto, Andrea Stevens Intan, Indo Pardamean, Bens Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83. © 2024 IEEE.

关键词： Lung cancer

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Screening of FDA-approved compound library identifies potential small-molecule inhibitors of SARS-CoV-2 non-structural proteins NSP1, NSP4, NSP6 and NSP13: molecular modeling and molecular dynamics studies

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Journal of proteins and proteomics 2021年第3期12卷 161-175页

作者： Shobana Sundar Lokesh Thangamani Shanmughavel Piramanayagam Chandrasekar Narayanan Rahul Natarajan Aiswarya Kanagaraj Sekar Jeyakumar Natarajan Computational Biology Lab Department of Bioinformatics Bharathiar University Coimbatore India. Department of Computational and Data Sciences Indian Institute of Science Bangalore Karnataka India. Data Mining and Text Mining Laboratory Department of Bioinformatics Bharathiar University Coimbatore Tamil Nadu India.

COVID-19, the current global pandemic has caused immense damage to human lives and the global economy. It is instigated by the SARS-CoV-2 virus and there is an immediate need for the identification of effective drugs against this deadly virus. SARS-CoV-2 genome codes for four structural proteins, sixteen non-structural proteins (NSPs) and several accessory proteins for its survival inside the host cells. In the present study, through in silico approaches, we aim to identify compounds that are effective against the four NSPs namely, NSP1, NSP4, NSP6 and NSP13 of SARS-CoV-2. The selection criteria of these four NSP proteins are they are least explored and potential targets. First, we have modeled the 3D structures of these proteins using homology modeling methods. Further, through molecular docking studies, we have screened the FDA-approved compounds against these modeled proteins and reported their docking scores. To gain dynamic insights, molecular dynamics studies have also been carried out for the best scored ligand against the NSPs. This study can further pave way for exposing more number of compounds against these proteins and enhance COVID-19 treatment.

关键词： COVID-19 FDA-approved compounds Molecular docking Molecular dynamics NSPs

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The Technology behind Genomic database 4

The Technology behind Genomic Database

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4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Trinugroho, Joko Pebrianto Perbangsa, Anzaludin Samsinga Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665453950

In this digital era, we are exposed to a large amount of data. This includes biological data, which stores information about living organisms, including Deoxyribonucleic acid (DNA), genes, and proteins. With the development of information technology and information system, most of available biological data are stored in an online public database. Many of the databases are free-access and easily used, which helps the users, especially researchers, to make use of the data. Among the known public biological databases are the University of California Santa Cruz (UCSC) Genome Browser database and the Rat Genome database (RGD). These two databases provide access to the biological data from different organisms. This paper aims to describe the technology of public biological databases. Also elucidated in this paper are the differences features between UCSC Genome Browser database and the RGD. Our results showed that the UCSC contains much more biological data and features than the RGD. However, the genome browser of UCSC has a complex display, while the RGD has a simple display. Overall, both databases give the users the option to choose the most suitable database for them. © 2022 IEEE.

关键词： Rats

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A mixture language model for class-attribute mining from biomedical literature digital library

A mixture language model for class-attribute mining from bio...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Xiaohua Zhou Xiaohua Hu Xiaodan Zhang Daniel D. Wu Data Mining and Bioinformatics Lab College of Information Science & Technology Drexel University Philadelphia PA USA

We define and study a novel text mining problem for biomedical literature digital library, referred to as the class-attribute mining. Given a collection of biomedical literature from a digital library addressing a set of objects (e.g., proteins) and their descriptions (e.g., protein functions), the tasks of class-attribute mining include: (1) to identify and summarize latent classes in the space of objects, (2) to discover latent attribute themes in the space of object descriptions, and (3) to summarize the commonalities and differences among identified classes along each attribute theme. We approach this mining problem through a mixture language model and estimate the parameters of the model using the EM algorithm. We demonstrate the effectiveness of the model with an application called protein community identification and annotation from Medline, the largest biomedical literature digital library with more than 16 millions abstracts.

关键词： Software libraries Proteins data mining Text mining Context modeling bioinformatics Abstracts Educational institutions Information science Parameter estimation

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