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6th International Conference on Software Engineering and Information Management, ICSIM 2023

作者： Walker, Logan Alexander Li, Ye McGlothlin, Maggie Cai, Dawen University of Michigan Biophysics Program Department of Computational Medicine and Bioinformatics United States University of Michigan Cell and Developmental Biology United States University of Michigan Cell and Developmental Biology Biophysics Program Neuroscience United States

ISBN: (纸本)9781450398237

Modern high-throughput microscopy methods such as light-sheet imaging and electron microscopy can produce petabytes of data inside of a single experiment. Storage of these large images, however, is challenging because of the difficulty of moving, storing, and analyzing such vast amounts of data, which is often collected at very high data rates (>1GBps). In this report, we provide a comparison of the performance of several compression algorithms using a collection of published and unpublished datasets including confocal, fMOST, and pathology images. We also use simulated data to demonstrate the efficiency of each algorithm as image content or entropy increases. As a result of this work, we recommend the use of the BLOSC algorithm combined with ZSTD for various microscopy applications, as it produces the best compression ratio over a collection of conditions. © 2023 ACM.

关键词： Information management

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Data mining for the global multiplex weekly average income analysis

Data mining for the global multiplex weekly average income a...

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2022 International Conference on ENTERprise Information Systems, CENTERIS 2022 - International Conference on Project MANagement, ProjMAN 2022 and International Conference on Health and Social Care Information Systems and Technologies, HCist 2022

作者： Dominic, Nicholas Elwirehardja, Gregorius Natanael Pardamean, Bens 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

The box office (BO) income had significantly declined up to 80% in 2020, as the COVID-19 pandemic emerged. To minimize further financial risks, multiplex (multiple cinema complexes) owners need to analyze their potential income for each movie, each week. Therefore, we developed a proper data mining strategy that allows multiplex owners to analyze and discover insights on how successfully produced movies could be. The methodology comprises (1) data loading and exploration, (2) data cleaning, (3) data selection, integration, and transformation using Pentaho, (4) data mining in which the results were stored in the MySQL database, and (5) pattern evaluation and presentation using Qlik Sense as the Business Intelligence (BI) dashboard. Based on our data mining methodology, we revealed that drama, comedy, action, and thriller are favorite genres. We also found that DreamWorks Animation and Pixar Animation Studios are both the most popular production houses, even Apatow Productions and Escape Artists still have the biggest revenue on average. © 2022 The Authors. Published by Elsevier B.V.

关键词： Animation

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Decision theory and risk simulation analysis for optimizing profit in paylater services

Decision theory and risk simulation analysis for optimizing ...

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作者： Dominic, Nicholas Pardamean, Bens 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

The projected increase in PayLater utilization reaches up to five million people by 2025. To optimize the yearly profit from their PayLater service, fintech companies must examine all possible risks before a unanimous decision is taken. Therefore, we proposed a unified decision framework derived from decision theory and the Monte Carlo simulation technique. Two schemes were coined: (1) a decision-making scheme, and (2) a risk simulation scheme. Throughout experiments, the framework was able to estimate several alternative decisions and their impacts, analyze the causes of failure and delays in the development of the PayLater service, and execute Monte Carlo simulations in up to 10,000 trials. Outputs of this study will benefit decision-makers in the fintech initiative before launching their PayLater products. © 2023 Elsevier B.V.. All rights reserved.

关键词： Fault tree analysis

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A Hybrid CNN-SVM Approach for Malaria in Blood Cell Detection 2

A Hybrid CNN-SVM Approach for Malaria in Blood Cell Detectio...

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2nd International Conference on Software Engineering and Information Technology, ICoSEIT 2024

作者： Karnyoto, Andrea Stevens Isnan, Mahmud Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350317503

There is still a severe malaria problem worldwide, particularly in regions with limited access to diagnostic tools. It is crucial to develop a system for detecting malaria in blood cells. This paper presents a hybrid Convolutional Neural Network (CNN), and as a classifier, we use a Support Vector Machine (SVM) framework for the automated detection of malaria parasites in blood cell images. The proposed system leverages the strengths of CNNs in feature extraction and representation learning from images, combined with the discriminative power of SVMs for classification. Initially, CNN extracts intricate features from blood cell images, capturing essential patterns indicative of malaria infection. Subsequently, the extracted features are used to train an SVM classifier, enabling accurate discrimination between parasitized and uninfected blood cells. Experimental dataset evaluations were obtained from the Lister Hill National Center for Biomedical Communications website of the National Library of Medicine. The proposed model achieves a better f1-score, outperforming individual CNN or SVM models, around 0.015 compared to individual CNN models and 0.27 compared to individual SVM models. This hybrid CNN-SVM methodology offers a promising solution for accurately and efficiently detecting malaria parasites in blood cell images. © 2024 IEEE.

关键词： Support vector machines

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Implementation of Fine-Tuned BERT for Enzyme Classification Based on Gene Ontology 11

Implementation of Fine-Tuned BERT for Enzyme Classification ...

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11th International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2024

作者： Henry, Matthew Martianus Kenneth, Christian Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331520311

Enzymes are biocatalysts with vital roles in biological functions and many industrial applications. Diverse enzymes are classified using Enzyme Commission (EC) nomenclature, making differentiation challenging. On the other hand, another biological information, gene ontology (GO), can describe the biological aspects of enzymes, covering related biological processes (BP), molecular functions (MF), and their locations within cells (CC). This study proposes a novel EC class and subclass classification of enzymes within the ontology subclass based on their GO semantics using a Bidirectional Encoder Representation of Transformer (BERT). The BERT model is first fine-tuned using the preprocessed GO term name and definition, with the enzymes in each ontology class (BP, MF, or CC) are also divided based on how the GO assigned, either through manual annotation (NONIEA) or electronically inferred (IEA). BERT successfully obtained 0.93, 0.60, 0.99, 0.90, 0.40, and 0.35 F1 scores during fine-tuning for BP IEA, BP NONIEA, MF IEA, MF NONIEA, CC IEA, and CC NONIEA, respectively. On the test set, the fine-tuned BERT significantly outperformed GOntoSim, a framework to calculate semantic similarity based on classical information theory, in EC class classification across all metrics with less inference time in all ontology subclass. Expanded further to the EC subclass, BERT can classify the enzyme on the EC subclass level in BP IEA and MF IEA ontology subclass. However, longer epochs are needed in fine-tuning. This result shows that the names and definitions of GO terms are distinguishable features in classifying enzymes as an alternative to the information content approach. © 2024 IEEE.

关键词： Gene Ontology

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Aggregating Structural Features for Computational Analysis of Mutations in BRCT Domains of BRCA1 Protein

Aggregating Structural Features for Computational Analysis o...

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

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

ISBN: (纸本)9798350307306

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features. © 2023 IEEE.

关键词： Proteins

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Multiplexed Nanoscopy via Buffer Exchange

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ACS Nano 2024年第34期18卷 23445-23456页

作者： Chang, Ting-Jui Ben Yang, T. Tony Department of Electrical Engineering National Taiwan University Taipei10617 Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei10617 Taiwan Department of Physics National Taiwan University Taipei10617 Taiwan Nano Science and Technology Program Taiwan International Graduate Program Academia Sinica and National Taiwan University Taipei10617 Taiwan

Understanding cellular functions, particularly in their intricate complexity, can greatly benefit from the spatial mapping of diverse molecules through multitarget single-molecule localization microscopy (SMLM). Existing methodologies, primarily restricting the encoding dimensions to color and lifetime or requiring cyclic staining, often involve broad chromatic detection, specialized optical configurations, or sophisticated labeling techniques. Here, we propose a simple approach called buffer-exchange stochastic optical reconstruction microscopy (beSTORM), which introduces an additional dimension to differentiate between single molecules irrespective of their spectral properties. This method leverages the distinguishable photoblinking responses to distinct buffer conditions, offering a straightforward yet effective means of fluorophore discrimination. Through buffer exchanges, beSTORM achieves multitarget SMLM imaging with minimal crosstalk. Direct integration with expansion microscopy (ExM) demonstrates its capability to resolve up to six proteins at the molecular level within a single emission color without chromatic aberration. Overall, beSTORM presents a highly compatible imaging platform, promising significant advancements in highly multiplexed nanoscopy for exploring multiple targets in biological systems with nanoscale precision. © 2024 The Authors. Published by American Chemical Society.

关键词： Stochastic systems

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miR-let-7a-2, miR103a-2 and CREB1-TF as therapeutic targets to regulate the transcription of DISC1 and PDE4D in the transcriptional regulation pathway by DISC1/ATF4 complex 4

miR-let-7a-2, miR103a-2 and CREB1-TF as therapeutic targets ...

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4th IEEE International Conference on BioInspired Processing, BIP 2022

作者： Rodriguez, Esteban Jose Rodriguez Mora-Rodriguez, Rodrigo San José Costa Rica University of Costa Rica Master Program of Bioinformatics and System Biology Posgraduate Program Sep San José Costa Rica

ISBN: (纸本)9781665470483

Disrupted In Schizophrenia 1 (DISC1) is considered a multifunctional protein implicated in various signaling pathways with neurological relevant outcomes, with the Disrupted In Schizophrenia 1/Activating Transcription Factor 4 (DISC1/ATF4) transcriptional regulation pathway being a pathway of interest since it regulates Phosphodiesterase 4D (PDE4D) transcription. Alterations in the transcription levels of DISC1 and PDE4D have been implicated in neurodegenerative processes and the identification of therapeutic targets that regulate these processes can be of great help to promote neuroprotective processes. By using a System Biology approach, we created a mathematical model of the transcriptional regulation of the DISC1/ATF4 pathway and using this model we conducted in-silico experiments to identify potential therapeutic targets with robust control on the network. As a result, miR-let-7a-2 is reported as a transcriptional regulator of DISC1 as well as miR103-a2 and CREB1-TF as transcriptional regulators of the PDE4D. These three regulators are identified as in-silico therapeutic targets to promote neuroprotective processes against neurodegenerative mental disorders. © 2022 IEEE.

关键词： Robust control

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Integrating immunoinformatics and computational epitope prediction for a vaccine candidate against respiratory syncytial virus

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Infectious Disease Modelling 2024年第3期9卷 763-774页

作者： Truc Ly Nguyen Heebal Kim Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Sciences Seoul National UniversitySeoul08826Republic of Korea Interdisciplinary Program in Bioinformatics Seoul National UniversitySeoul08826Republic of Korea eGnome Inc.Seoul05836Republic of Korea

Respiratory syncytial virus(RSV)poses a significant global health threat,especially affecting infants and the *** this,the present study proposes an innovative approach to vaccine design,utilizing immunoinformatics and computational *** analyzed RSV's structural proteins across both subtypes A and B,identifying potential helper T lymphocyte,cytotoxic T lymphocyte,and linear B lymphocyte *** such as antigenicity,allergenicity,toxicity,and cytokine-inducing potential were rigorously ***,we evaluated the conservancy of these epitopes and their population coverage across various RSV *** comprehensive analysis identified six major histocompatibility complex class I(MHC-I)binding,five MHC-II binding,and three B-cell *** were integrated with suitable linkers and adjuvants to form the ***,molecular docking and molecular dynamics simulations demonstrated stable interactions between the vaccine candidate and human Toll-like receptors(TLR4 and TLR5),with a notable preference for *** simulation analysis underscored the vaccine's potential to elicit a strong immune *** study presents a promising RSV vaccine candidate and offers theoretical support,marking a significant advancement in vaccine development ***,the promising in silico findings need to be further validated through additional in vivo studies.

关键词： Respiratory syncytial virus Multiepitope vaccine Immunoinformatics Docking molecular Molecular dynamics simulation Immune simulation

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Knowledge Graph-Enhanced Semantic Cache for Low-Latency and Cost-Effective Inference in Large Language Models 9

Knowledge Graph-Enhanced Semantic Cache for Low-Latency and ...

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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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