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SSRN 2024年

作者： Toopradab, Borwornlak Xie, Wanting Duan, Lian Hengphasatporn, Kowit Harada, Ryuhei Sinsulpsiri, Silpsiri Shigeta, Yasuteru Shi, Liyi Maitarad, Phornphimon Rungrotmongkol, Thanyada Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok10330 Thailand Center of Excellence in Biocatalyst and Sustainable Biotechnology Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok10330 Thailand Research Center of Nano Science and Technology Department of Chemistry College of Science Shanghai University Shanghai200444 China University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8577 Japan Graduate School of Pure and Applied Sciences University of Tsukuba 1-1-1 Tennodai Ibaraki305–8571 Japan Emerging Industries Institute Shanghai University Zhejiang Jiaxing314006 China

The global outbreak of the COVID-19 pandemic caused by the SARS-CoV-2 virus had led to profound respiratory health implications. This study focused on designing organoselenium-based inhibitors targeting the SARS-CoV-2 main protease (Mpro). The ligand-binding pathway sampling method based on parallel cascade selection molecular dynamics (LB-PaCS-MD) simulations was employed to elucidate plausible paths and conformations of ebselen, a synthetic organoselenium drug, within the Mpro catalytic site. Ebselen effectively engaged the active site, adopting proximity to H41 and interacting through the benzoisoselenazole ring in a π-π T-shaped arrangement, with an additional π-sulfur interaction with C145. In addition, the ligand-based drug design using the QSAR with GFA-MLR, RF, and ANN models were employed for biological activity prediction. The QSAR-ANN model showed robust statistical performance, with an R2training exceeding 0.98 and an RMSEtest of 0.21, indicating its suitability for predicting biological activities. Integration the ANN model with the LB-PaCS-MD insights enabled the rational design of novel compounds anchored in the ebselen core structure, identifying promising candidates with favorable predicted IC50 values. The designed compounds exhibited suitable drug-like characteristics and adopted an active conformation similar to ebselen, inhibiting Mpro function. These findings represent a synergistic approach merging ligand and structure-based drug design;with the potential to guide experimental synthesis and enzyme assay testing. © 2024, The Authors. All rights reserved.

关键词： Coronavirus

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Improving quantitation accuracy in isobaric-labeling mass spectrometry experiments with spectral library searching and feature-based peptide-spectrum match filter

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Scientific Reports 2023年第1期13卷 1-10页

作者： Kuo, Tzu-Yun Wang, Jen-Hung Huang, Yung-Wen Sung, Ting-Yi Chen, Ching-Tai Department of Biochemical Science and Technology National Taiwan University Taipei 10617 Taiwan Bioinformatics Program Taiwan International Graduate Program Institute of Statistical Science Academia Sinica Taipei 11529 Taiwan Institute of Information Science Academia Sinica Taipei 11529 Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei 11221 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei 10617 Taiwan Department of Bioinformatics and Biomedical Engineering Asia University Taichung 41354 Taiwan Center for Precision Health Research Asia University Taichung 41354 Taiwan

Isobaric labeling relative quantitation is one of the dominating proteomic quantitation technologies. Traditional quantitation pipelines for isobaric-labeled mass spectrometry data are based on sequence database searching. In this study, we present a novel quantitation pipeline that integrates sequence database searching, spectral library searching, and a feature-based peptide-spectrum-match (PSM) filter using various spectral features for filtering. The combined database and spectral library searching results in larger quantitation coverage, and the filter removes PSMs with larger quantitation errors, retaining those with higher quantitation accuracy. Quantitation results show that the proposed pipeline can improve the overall quantitation accuracy at the PSM and protein levels. To our knowledge, this is the first study that utilizes spectral library searching to improve isobaric labeling-based quantitation. For users to conveniently perform the proposed pipeline, we have implemented the feature-based filter being executable on both Windows and Linux platforms;its executable files, user manual, and sample data sets are freely available at https://***/comics/Software_*** . Furthermore, with the developed filter, the proposed pipeline is fully compatible with the Trans-Proteomic Pipeline. © 2023, Springer Nature Limited.

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Smart Dome 4.0: Low-Cost, Independent, Automated Energy System for Agricultural Purposes enabled by Machine Learning 2

Smart Dome 4.0: Low-Cost, Independent, Automated Energy Syst...

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2021 2nd International Symposium on Automation, Information and Computing, ISAIC 2021

作者： Budiman, Arief S Gunawan, Fergyanto Djuana, Endang Pardamean, Bens Romeli, Sugiarto Putri, Dianing Nn Aji, Daisman Pb Rahardjo, Kuat Stevanus Ilham Wibowo, M. Daffa, Nadiev Owen, Raymond Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia PT. Impack Pratama Industri Tbk Jakarta Indonesia Mechanical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

Many economically essential crops in Indonesia (such as coffee, tea, chocolate, or copra) require storage or drying under certain environmental conditions, especially temperature and humidity. The solar dryer dome, typically used for agricultural purposes in Indonesia, produces a sufficient amount of heat to increase the evaporation rate inside the dome and reduce the moisture content of the commodity. A hybrid solar dryer accompanied by a photovoltaic panel, fan, and ventilation system is generally suitable. The system can provide an optimum environment with minimum control. However, as the outdoor temperature and humidity change dramatically, such as at night time, more control is required. Based on Industry 4.0 technologies, we have developed a new kind of hybrid solar dryer that provides an optimum environment 24/7. The system, called Smart Dome 4.0, is an intelligent, low-cost, self-sufficient drying and storage system to support Indonesia Agriculture 4.0. The system has a local power generation unit to self-sustain the required energy and operate without connecting to the electricity grid. The system utilizes a machine-learning algorithm to predict the environmental condition and optimally uses self-generated electric power. The developed Smart Dome 4.0 technology is critical to producing a sustainable solar dome under drastic environmental dynamics. © Published under licence by IOP Publishing Ltd.

关键词： Learning algorithms

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Predicting regional somatic mutation rates using DNA motifs

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PLoS Computational Biology 2023年第10 October期19卷 e1011536-e1011536页

作者： Liu, Cong Wang, Zengmiao Wang, Jun Liu, Chengyu Wang, Mengchi Ngo, Vu Wang, Wei Department of Chemistry and Biochemistry University of California San Diego La Jolla CA United States State Key Laboratory of Remote Sensing Science Center for Global Change and Public Health Faculty of Geographical Science Beijing Normal University Beijing China Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA United States Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA United States

How the locus-specificity of epigenetic modifications is regulated remains an unanswered question. A contributing mechanism is that epigenetic enzymes are recruited to specific loci by DNA binding factors recognizing particular sequence motifs (referred to as epi-motifs). Using these motifs to predict biological outputs depending on local epigenetic state such as somatic mutation rates would confirm their functionality. Here, we used DNA motifs including known TF motifs and epi-motifs as a surrogate of epigenetic signals to predict somatic mutation rates in 13 cancers at an average 23kbp resolution. We implemented an interpretable neural network model, called contextual regression, to successfully learn the universal relationship between mutations and DNA motifs, and uncovered motifs that are most impactful on the regional mutation rates such as TP53 and epi-motifs associated with H3K9me3. Furthermore, we identified genomic regions with significantly higher mutation rates than the expected values in each individual tumor and demonstrated that such cancer-related regions can accurately predict cancer types. Interestingly, we found that the same mutation signatures often have different contributions to cancer-related and cancer-independent regions, and we also identified the motifs with the most contribution to each mutation signature. © 2023 Liu et al.

关键词： Neural network models

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Structural insights into the role of N-terminal integrity in PhoSL for core-fucosylated N-glycan recognition

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International Journal of Biological Macromolecules 2024年 255卷 128309页

作者： Lou, Yuan-Chao Tu, Cheng-Fen Chou, Chun-Chi Yeh, Hsin-Hong Chien, Chia-Yu Sadotra, Sushant Chen, Chinpan Yang, Ruey-Bing Hsu, Chun-Hua Institute of Biomedical Sciences Academia Sinica Taipei115 Taiwan Department of Agricultural Chemistry National Taiwan University Taipei106 Taiwan Chemical Biology and Molecular Biophysics Taiwan International Graduate Program Academia Sinica Taipei115 Taiwan Institute of Bioinformatics and Structural Biology National Tsing Hua University Hsinchu300 Taiwan Biomedical Translation Research Center Academia Sinica Taipei115 Taiwan Ph.D. Program in Drug Discovery and Development Industry College of Pharmacy Taipei Medical University Taipei110 Taiwan Genome and Systems Biology Degree Program National Taiwan University and Academia Sinica Taipei106 Taiwan Institute of Biochemical Sciences National Taiwan University Taipei106 Taiwan Center for Computational and Systems Biology National Taiwan University Taipei106 Taiwan

PhoSL (Pholiota squarrosa Lectin) has an exceptional binding affinity for biomolecules with core-fucosylated N-glycans. This modification involves the addition of fucose to the inner N-acetylglucosamine within the N-glycan structure and is known to influence many physiological processes. Nevertheless, the molecular interactions underlying high-affinity binding of native PhoSL to core-fucosylated N-glycans remain largely unknown. In this study, we devised a strategy to produce PhoSL with the essential structural characteristics of the native protein (n-PhoSL). To do so, a fusion protein was expressed in E. coli and purified. Then, enzymatic cleavage and incubation with glutathione were utilized to recapitulate the native primary structure and disulfide bonding pattern. Subsequently, we identified the residues crucial for n-PhoSL binding to core-fucosylated chitobiose (N2F) via NMR spectroscopy. Additionally, crystal structures were solved for both apo n-PhoSL and its N2F complex. These analyses suggested a pivotal role of the N-terminal amine in maintaining the integrity of the binding pocket and actively contributing to core-fucose recognition. In support of this idea, the inclusion of additional residues at the N-terminus considerably reduced binding affinity and PhoSL cytotoxicity toward breast cancer cells. Taken together, these findings can facilitate the utilization of PhoSL in basic research, diagnostics and therapeutic strategies. © 2023 Elsevier B.V.

关键词： Proteins

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Web Development of Direct-to-consumer Genetics Testing

Web Development of Direct-to-consumer Genetics Testing

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Kians Azizatikarna Deby Erina Parung Dian Amirullah Alam Ahmad Hidayat Tjeng Wawan Cenggoro Arif Budiarto Simon Bens Pardamean Genetics Indonesia Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665453967

As the cost of genetic testing becomes more affordable each year, direct-to-customer (DTC) genetic testing services witness rapid market growth. This has encouraged the development of an easy-to-use website application to optimize potential customers to obtain informed choice regarding the offered DTC genetic testing as well as purchasing information. We present a wireframing process as a part of the agile software development process to build a web portal prototype for an Indonesia-based genetic testing service called DNAku. The approach in building the prototype used in this case is a part of the Agile Software Development Method. In brief, the wireframing stage resulted in this work consists of three main important outcomes. The first one is the use case diagram as a blueprint of this web-based system. A site map is then proposed as an extended version of the use case diagram which visualizes the whole pages with the links. Finally, based on this site map a series of page mock-ups are designed using Hyper Text Marking Language (HTML) and Cascading Style Sheets (CSS). The result of the prototype can be used as the basis for the next development stage, which is the coding stage based on the collected feedback from users. Considering the continuous application development via the wireframing method by taking into account the cycle of feedbacks from involved parties, this approach can strengthen the infrastructure to sustain DTC genetic business model, which has a niche market in Indonesia.

关键词： Visualization Costs Buildings Prototypes Agile software development Genetics Encoding

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Virtual Lung Screening Trial (VLST): An In Silico Replica of the National Lung Screening Trial for Lung Cancer Detection

arXiv

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arXiv 2024年

作者： Tushar, Fakrul Islam Vancoillie, Liesbeth McCabe, Cindy Kavuri, Amareswararao Dahal, Lavsen Harrawood, Brian Fryling, Milo Zarei, Mojtaba Sotoudeh-Paima, Saman Ho, Fong Chi Ghosh, Dhrubajyoti Harowicz, Michael R. Tailor, Tina D. Luo, Sheng Segars, W. Paul Abadi, Ehsan Lafata, Kyle J. Lo, Joseph Y. Samei, Ehsan Center for Virtual Imaging Trials Carl E. Ravin Advanced Imaging Laboratories Dept. of Radiology Duke University School of Medicine United States Dept. of Electrical & Computer Engineering Pratt School of Engineering Duke University United States Medical Physics Graduate Program Duke University United States Dept. of Biostatistics and Bioinformatics Duke University School of Medicine United States Dept. of Radiation Oncology Duke University School of Medicine United States

Importance: Clinical imaging trials are crucial for evaluation of medical innovations, but the process is inefficient, expensive, and ethically-constrained. Virtual imaging trial (VIT) approach addresses these limitations by emulating the components of a clinical trial. An in silico rendition of the National Lung Screening Trial (NCLS) via Virtual Lung Screening Trial (VLST) demonstrates the promise of VITs to expedite clinical trials, reduce risks to subjects, and facilitate the optimal use of imaging technologies in clinical settings. Objectives: To demonstrate that a virtual imaging trial platform can accurately emulate a major clinical trial, specifically the National Lung Screening Trial (NLST) that compared computed tomography (CT) and chest radiography (CXR) imaging for lung cancer screening. Design, Setting, and Participants: A virtual patient population of 294 subjects was created from human models (XCAT) emulating the NLST, with two types of simulated cancerous lung nodules. Each virtual patient in the cohort was assessed using simulated CT and CXR systems to generate images reflecting the NLST imaging technologies. Deep learning models trained for lesion detection, AI CT-Reader, and AI CXR-Reader served as virtual readers. Main Outcomes and Measures: The primary outcome was the difference in the Receiver Operating Characteristic Area Under the Curve (AUC) for CT and CXR modalities. Results: The study analyzed paired CT and CXR simulated images from 294 virtual patients. The AI CT-Reader outperformed the AI CXR-Reader across all levels of analysis. At the patient level, CT demonstrated superior diagnostic performance with an AUC of 0.92 (95% CI: 0.90-0.95), compared to CXR’s AUC of 0.72 (0.67-0.77). Subgroup analyses of lesion types revealed CT had significantly better detection of homogeneous lesions (AUC 0.97, 95% CI: 0.95-0.98) compared to heterogeneous lesions (0.89;0.86-0.93). Furthermore, when the specificity of the AI CT-Reader was adjusted to match

关键词： Lung cancer

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Theoretical Insights into Co2 Electroreduction on Single and Dual Heteroatom-Doped Diamonds

SSRN

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SSRN 2023年

作者： Injongkol, Yuwanda Zhang, Rui-Qin Montoya, Alejandro Rungrotmongkol, Thanyada Jungsuttiwong, Siriporn Center of Excellence in Biocatalyst and Sustainable Biotechnology Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok10330 Thailand City University of Hong Kong Hong Kong School of Chemical and Biomolecular Engineering The University of Sydney SydneyNSW2006 Australia Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok10330 Thailand Department of Chemistry Faculty of Science Ubon Ratchathani University Ubon Ratchathani34190 Thailand

We employed Periodic Density Functional Theory (DFT) to investigate the catalytic activity of single and double dopant of boron (B), nitrogen (N), and phosphorous (P) anchored on diamond surfaces for the CO2 reduction reaction (CO2RR) by analyzing reaction energy profiles. Our findings show that the double-doped catalyst exhibits lower energy barriers in CO2RR compared to the single dopants. Specifically, when considering BB and NN configurations, there is a thermodynamic preference towards the formation of formic acid (HCOOH) with overpotentials of 0.40 and 0.09 V, respectively. The co-doped catalyst comprising B and N (BN) demonstrates a tendency towards the formation of HCOOH without requiring any applied overpotentials. Notably, BN outperforms other catalysts, occupying the top position on the volcano plot, indicating the lowest limiting potential (UL), remarkable thermal stability, and the ability to suppress the competing hydrogen evolution reaction (HER). This research provides valuable insights into the product differentiation in the electroreduction of CO2. © 2023, The Authors. All rights reserved.

关键词： Density functional theory

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A Design of Polygenic Risk Model with Deep Learning for Colorectal Cancer in Multiethnic Indonesians

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Procedia Computer Science 2021年 179卷 632-639页

作者： Steven Amadeus Tjeng Wawan Cenggoro Arif Budiarto Bens Pardamean Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 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

Recently, health management is emerging and attract attention to how to provide better prognostication and health management systems. The challenges in the prognostication are how to develop a model that can self-learn the prognostication features and how to get a high accuracy prediction. Prognostication in health disease involves SNPs which is a genetic marker. In this paper, we propose a polygenic risk model using deep learning: Transformer with self-attention mechanism and DeepLIFT. The use of these deep learning model allows us to predict the risk of colorectal cancer and see the correlation between SNPs.

关键词： Prognostication Polygenic Risk Model Transformer Model DeepLIFT Colorectal Cancer

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Deep Learning as a Vector Embedding Model for Customer Churn

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Procedia Computer Science 2021年 179卷 624-631页

作者： Tjeng Wawan Cenggoro Raditya Ayu Wirastari Edy Rudianto Mochamad Ilham Mohadi Dinne Ratj Bens Pardamean 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

To face the tight competition in the telecommunication industry, it is important to minimize the rate of customers stopping their service subscription, which is known as customer churn. For that goal, an explainable predictive customer churn model is an essential tool to be owned by a telecommunication provider. In this paper, we developed the explainable model by utilizing the concept of vector embedding in Deep Learning. We show that the model can reveal churning customers that can potentially be converted back to use the previous telecommunication service. The generated vectors are also highly discriminative between the churning and loyal customers, which enable the developed models to be highly predictive for determining whether a customer would cease his/her service subscription or not. The best model in our experiment achieved a predictive performance of 81.16%, measured by the F1 Score. Further analysis on the clusters similarity and t-SNE plot also confirmed that the generated vectors are discriminative for churn prediction.

关键词： customer churn customer behavior deep learning vector embedding representation learning

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