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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Dominic, Nicholas Pardamean, Bens Bina Nusantara University Bioinformatics & Data Science Research Center Jakarta11480 Indonesia BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

In organizational knowledge management, Large Language Model (LLM) caches act as a semantic repository gathered from previous LLM responses. Due to intensive calls from multiple users, LLM may suffer from high inference latency. While there are many prior available approaches to solve this problem, most of them are inherently complex. This paper introduced a Knowledge Graph-enhanced Semantic Cache mechanism as an alternative, lightweight technique to boost retrieval for similar prompts. The latest state-of-the-art open-source LLM, named Google's Gemma-2B-it, was used to generate sample prompts and responses as a draft, while a knowledge graph (KG) was built from Wikipedia sentences. To create embeddings of prompts and KG, all-MiniLM-L6-v2 from SentenceTransformer was used. This new cache system resulted in up to 28% improvement over a standard model. In particular, reinforcement with KG cache embeddings yielded more than 85% semantic cache accuracy. To map the next trajectory of this pilot study, an overview of the extended framework for LLM knowledge management was also presented in this paper. The framework includes the new KG- enhanced cache system equipped with scalable security and fallback mechanisms that can promote green technology through substantial improvements in latency, throughput, and overall LLM costs. © 2024 IEEE.

关键词： Knowledge graph

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Variational quantum neural network with optimized ansatz for predicting malignant ventricular arrhythmias 9th

Variational quantum neural network with optimized ansatz for...

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

作者： Dominic, Nicholas Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Preventive strategies should be the utmost priority when dealing with diverse patients suffering from malignant ventricular arrhythmia (MVA) that can lead to sudden cardiac death (SCD). Electrocardiogram (ECG) data is commonly used as a predictor for MVA predictive models. In this study, all ECG signals from MIT-BIH databases were fragmented into five-minute durations with a frequency sampling of 128 Hz. To solve the absence of hybrid optimizations in Machine Learning (ML) models, a novel Variational Quantum Neural Network (VQNN) was invented. Empowered by deep learning capabilities and optimized quantum circuits design, VQNN achieved remarkable performances designated by an accuracy of up to 95.1%, a perfect 100% recall, and a 95.2% score of the area under the Receiver Operating Characteristic curve (AUC ROC) with Conjugate Gradient as an optimizer and EfficientSU2 as a quantum ansatz. Despite the susceptibility to quantum noise, this research settles a new trajectory of utilizing quantum variational algorithms to predict and expand its applicability for MVA cases. © 2024 The Authors.

关键词： Quantum computers

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Employing Hexahydroquinolines as PfCDPK4 Inhibitors to Combat Malaria Transmission: An Advanced Computational Approach

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Advances and Applications in bioinformatics and Chemistry 2024年 17卷 83-105页

作者： Oduselu, Gbolahan O Elebiju, Oluwadunni F. Ogunnupebi, Temitope A. Akash, Shopnil Ajani, Olayinka O. Adebiyi, Ezekiel Covenant University Bioinformatics Research (CUBRe) Covenant University OG Ota Nigeria Department of Chemistry Covenant University OG Ota Nigeria Department of Pharmacy Daffodil International University Dhaka Bangladesh African Center of Excellence in Bioinformatics & Data Intensive Science Makerere University Kampala Uganda Division of Applied Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany

Background: Existing antimalarial drugs primarily target blood-stage parasites, but there is a need for transmission-blocking drugs to combat malaria effectively. Plasmodium falciparum Calcium-dependent Protein Kinase 4 (CDPK4) is a promising target for such drugs. This study employed advanced in silico analyses of hexahydroquinolines (HHQ) derivatives to identify PfCDPK4 inhibitors capable of disrupting malaria transmission. Structure-based virtual screening (SBVS) was employed to discover HHQ derivatives with the highest binding affinities against the 3D structure of PfCDPK4 (PDB 1D: 4QOX). Methods: Interaction analysis of protein-ligand complexes utilized Discovery Studio Client, while druglikeness and ADMET properties were assessed using SwissADME and pkCSM web servers, respectively. Quantum mechanical calculations of the top hits were conducted using density functional theory (DFT), and GROMACS was employed to perform the molecular dynamics (MD) simulations. Binding free energy was predicted using the *** tool from the AMBER package. Results: SBVS identified ten best hits possessing docking scores within the range of −11.2 kcal/mol and −10.6 kcal/mol, surpassing the known inhibitor, BKI-1294 (−9.9 kcal/mol). Among these, 4-[4-(Furan-2-carbonyl)piperazin-1-yl]-1-(naphthalen-2-ylmethyl)- 2-oxo-4a,5,6,7,8,8a-hexahydroquinoline-3-carbonitrile (PubChem ID: 145784778) exhibited the highest binding affinity (−11.2 kcal/ mol) against PfCDPK4. Conclusion: Comparative analysis of this compound with BKI-1294 using advanced computational approaches demonstrated competitive potential. These findings suggest the potential of 4-[4-(Furan-2-carbonyl)piperazin-1-yl]-1-(naphthalen-2-ylmethyl)- 2-oxo-4a,5,6,7,8,8a-hexahydroquinoline-3-carbonitrile as a promising PfCDPK4 inhibitor for disrupting malaria transmission. However, further experimental studies are warranted to validate its efficacy and safety profile. © 2024 Oduselu et al.

关键词： antimalarial drugs drug design gametocidal activity malaria molecular dynamics

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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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Dynamic k-Anonymity for Electronic Health Records: A Topological Framework 19th

Dynamic k-Anonymity for Electronic Health Records: A Topolo...

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19th International Workshop on data Privacy Management, DPM 2024, 8th International Workshop on Cryptocurrencies and Blockchain Technology, CBT 2024 and 10th Workshop on the Security of Industrial Control Systems and of Cyber-Physical Systems, CyberICPS 2024 which were held in conjunction with the 29th European Symposium on Research in Computer Security, ESORICS 2024

作者： Swaminathan, Arjhun Akgün, Mete Medical Data Privacy and Privacy Preserving Machine Learning Department of Computer Science University of Tübingen Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen Germany

ISBN: (纸本)9783031823480

With the rapid digitization of Electronic Health Records (EHRs), fast and adaptive data anonymization methods have become increasingly important. While tools from topological data analysis (TDA) have been proposed to anonymize static datasets—allowing the creation of multiple generalizations for different anonymization needs from a single computation—the application to dynamic datasets remains unexplored. To address this, our work adapts existing methodologies to the dynamic setting. We develop an improved version of weighted persistence barcodes that track higher-dimensional holes in data, allowing us to edit persistence information on the fly. Additionally, we introduce filtration trimming, a novel technique designed to update persistence data quickly with minimal computing effort when data is added. Our work represents a significant advancement in healthcare data privacy, offering a refined approach to protecting highly sensitive and evolving patient data through dynamic k-anonymity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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On the Measurement of the Inductive Bias Impact in Convolutional Neural Networks Training for Single Image Super-Resolution 8

On the Measurement of the Inductive Bias Impact in Convoluti...

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

作者： Kwantawijaya, Aldrian Najoan, Daniel Stephanus Tamimmanar Cenggoro, Tjeng Wawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

The performance of SISR models is currently growing at a rapid pace since the adoption of Convolutional Neural Networks (CNN) in the domain. Despite that, the understanding of the underlying factors of this rapid growth is still low. In this study, we proposed a study design to measure the impact of the inductive bias in CNN on its training performance, which is one of the factors of the excellent performance of CNN in SISR. With this study design, we observed that not all CNN has a supportive inductive bias to the training performance. This result can provide guidance to future research in choosing a suitable CNN architecture for optimal SISR performance. © 2023 The Authors. Published by Elsevier B.V.

关键词： Convolutional Neural Networks Deep Learning Inductive Bias Single-Image Super Resolution

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Cancer Prediction using Clinical and Genomic data Fusion: A Systematic Review 6

Cancer Prediction using Clinical and Genomic Data Fusion: A ...

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

作者： Julian, Kelvin Nugroho, Kuncahyo Setyo Nirwantono, Rudi Pardamean, Bens Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

This systematic review provides a comprehensive overview of the methods used to integrate genomic and clinical data in cancer prediction. The review includes 19 studies across various cancers, including breast, colorectal, melanoma, lung, pancreatic, and thyroid. The studies employed different methods to combine genomic and clinical data, including weighted polygenic risk scores, genetic and non-genetic risk scores, and different machine learning algorithms. The results show significant improvements in model prediction performance accuracy across multiple studies. The review highlights the potential benefits of integrating genetic and phenotypic information to improve disease risk prediction models and inform personalized healthcare strategies. © 2024 IEEE.

关键词： Lung cancer

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Comparative Analysis of CNN Models for Multiclass Detection on Colon Disease 9

Comparative Analysis of CNN Models for Multiclass Detection ...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Sulistyawan, Jason Sebastian Nugroho, Kuncahyo Setyo Isnan, Mahmud Pardamean, Bens Binus Graduate Program Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331510732

Gastrointestinal diseases are significant health concerns that mostly affect the digestive and biliary tracts. These can only be observed internally through endoscopy, or a modern approach named Wireless Capsule Endoscopy. Though the approach has proven to work, the process relies heavily on medical experts, which is prone to error when given a considerably large load. The use of deep learning in detecting diseases with images has been proven to work in various cases and can be implemented for gastrointestinal diseases. This study compared four deep learning models based on Convolutional Neural Network architectures consisting of the VGG16, ResNet152V2, InceptionV3, and Xception for classifying colon disease images. The experiment showed that the ResNet152V2 performed the best, compared to the other three with a testing accuracy score of 0.9837. On the other hand, the VGG16 had the lowest performance with around accuracy score of 0.89 while the Xception and InceptionV3models yielded similar accuracy scores of 0.9587 and 0.9549, respectively. The results highlight the effectiveness of ResNet152V2 in handling the complexity of colon disease detection © 2024 IEEE.

关键词： Convolutional neural networks

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TCRosetta:An Integrated Analysis and Annotation Platform for T-cell Receptor Sequences

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Genomics, Proteomics & bioinformatics 2024年第4期22卷 181-190页

作者： Tao Yue Si-Yi Chen Wen-Kang Shen Zhan-Ye Zhang Liming Cheng An-Yuan Guo Center for Artificial Intelligence Biology Hubei Bioinformatics&Molecular Imaging Key LaboratoryCollege of Life Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Department of Laboratory Medicine Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Department of Thoracic Surgery West China Biomedical Big Data CenterWest China HospitalSichuan UniversityChengdu 610041China

T cells and T-cell receptors(TCRs)are essential components of the adaptive immune *** of the TCR repertoire offers a promising and highly informative source for understanding the functions of T cells in the immune response and *** TCR repertoire studies have attracted much attention,there are few online servers available for TCR repertoire analysis,especially for TCR sequence annotation or advanced ***,we developed TCRosetta,a comprehensive online server that integrates analytical methods for TCR repertoire analysis and *** combines general feature analysis,large-scale sequence clustering,network construction,peptide–TCR binding prediction,generation probability calculation,and k-mer motif analysis for TCR sequences,making TCR data analysis as simple as *** TCRosetta server accepts multiple input data formats and can analyze�20,000 TCR sequences in less than 3 *** is the most comprehensive web server available for TCR repertoire analysis and is freely available at https://***/TCRosetta/.

关键词： T-cell receptor TCR repertoire analysis Batch search Annotation Web server.

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Automatic Multiple Choice Question Generation: A Systematic Literature Review 9

Automatic Multiple Choice Question Generation: A Systematic ...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Henry, Matthew Martianus Heryanto, Nur Adhianti Isnan, Mahmud Nugroho, Kuncahyo Setyo Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331510732

Despite their drawbacks, multiple-choice questions (MCQ) have been widely used to assess the students' understanding of lectures through examinations. The development of automatic MCQ generation is beneficial, especially for educators. As a starting point for further development, a Systematic Literature Review (SLR) is conducted to uncover current trends, future challenges, and opportunities in automatic MCQ generation. Previously, an SLR was conducted, but it lacks coverage of the utilization of transformer-based models. This SLR covers the development of automatic MCQ generation using either traditional or advanced approaches such as Transformers. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework was used to gather the data from Scopus, IEEE Xplore, SpringerLink, arXiv, and Semantic Scholar. The included articles must be open-access computer science conference papers or journal articles and written in English less than five years ago. Four independent reviewers analyzed the research workflow, evaluation metric, and dataset used in each study. There are 18 included studies, where 17% (n = 3) studies are from 2024, 33% (n = 6) studies are from 2023, 22% (n = 4) studies are from 2022, 11% (n = 2) studies are from 2021, and 17% (n = 3) studies are from 2020. There are 33% (n = 6) of the studies used the traditional feature-based engineering approach, 39% (n = 7) of the studies used the Transformer-based model fine-tuning approach, and the remaining used novel approaches. The study found that BERT variants are the most utilized Transformer-based model in automatic MCQ. The research notes some challenges, but also open various opportunities for further research, including Large Language Model (LLM) utilization for automatic MCQ generation, the utilization BERT-based models for standardized machine-learned evaluation metrics, and the initiative for the creation of an MCQ dataset benchmark. © 2024 IEEE.

关键词： Semantics

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