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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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BraceNet: Graph-Embedded Neural Network For Brain Network Analysis

BraceNet: Graph-Embedded Neural Network For Brain Network An...

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2022 IEEE International Conference on Big data, Big data 2022

作者： Kan, Xuan Kong, Yunchuan Yu, Tianwei Guo, Ying Emory University Department of Computer Science GA United States Emory University Department of Biostatistics and Bioinformatics GA United States The Chinese University of HongKong School of Data Science Shenzhen China

ISBN: (纸本)9781665480451

Multimodal brain networks extracted from functional magnetic resonance imaging (fMRI) characterize complex connectivities among brain regions from both structural and functional, showing great potential for mental health analysis. Deep neural network models have led a tremendous success in various downstream tasks. However, common property in brain network data, the small number of samples compared with the huge amount of features, hinders the application of deep learning techniques for brain network analysis. This work presents a graph-embedding method by leveraging the unique characteristics of brain networks to unleash the power of deep neural networks and achieve outstanding prediction performance with proper explainability. Experiment results show clear advancements in our proposed braceNet on both real and synthetic datasets. © 2022 IEEE.

关键词： Graph neural networks

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AI-Based Video Analysis for Driver Fatigue Detection: A Literature Review on Underlying datasets, Labelling, and Alertness Level Classification 1

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

作者： Ariansyah, Dedy Rahutomo, Reza Elwirehardja, Gregorius Natanael Asadi, Faisal Pardamean, Bens Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Reduced alertness because of fatigue or drowsiness accounts for a major cause of road accidents globally. To minimize the likelihood of alertness reduction-related crashes, a video-based detection emerges as a non-intrusive method and can offer high accuracy for the countermeasure development. Many Artificial Intelligence (AI) techniques have been developed to aid in video analysis for fatigue detection. However, the issues in AI-based video analysis for fatigue countermeasure development are rarely discussed. This paper reviews current video-based countermeasure development in the literature and highlights some reliability issues of AI-based fatigue countermeasure development in terms of underlying dataset, labelling method, and alertness level classification. The result serves as insights and consideration to academics and industrialists in developing a high reliable AI-based video analysis for driver fatigue countermeasure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Classification (of information)

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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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Time-series Analysis of Correlation between Climatic Parameters and Dengue Fever in Indonesia 4

Time-series Analysis of Correlation between Climatic Paramet...

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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Nirwantono, Rudi Trinugroho, Joko Pebrianto Sudigyo, Digdo Hidayat, Alam Ahmad Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665473279

Dengue fever is mainly caused by female mosquitoes from the genus Aedes. It predominantly occurs in equatorial to sub-tropical regions. The weather factors play a significant role in the mosquito life cycle, so a study on the impact of climate parameters on dengue incidence is required. This study aimed to understand the statistical association between the annual fluctuation of dengue cases with climate factors in Indonesia. Climatic data, annual rainfall, and annual mean ambient temperature of Indonesia were obtained from *** from 2010 to 2020, while the data on the annual incidence of dengue fever in Indonesia were collected from the Indonesian Health Profile by the Ministry of Health of Indonesia from 2010 to 2020. The association between climatic factors with dengue cases was calculated with Spearman statistical test after the data was indicated as not normally distributed. In addition, we performed Poisson regression analysis to examine how the changes in climatic parameters affect the dengue cases. The rain precipitation was insignificantly correlated with the dengue incidence in Indonesia during the study time. In contrast, the ambient temperature statistically showed a strong association with the dengue cases. The association between climatic factors and the number of dengue cases suggests that the model would likely be prospectively applied to the dengue early warning system as a nationwide intervention to reduce the epidemic impact in Indonesia. © 2022 IEEE.

关键词： data mining

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Uncharted territory: the role of mitochondrial DNA variation in macrophage-mediated host susceptibility to tuberculosis

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Tuberculosis 2025年 153卷 102650-102650页

作者： Croock, Dayna Swart, Yolandi Sanko, Tomasz Janusz Mavumengwana, Vuyo Möller, Marlo Uren, Caitlin Petersen, Desiree C. South African Medical Research Council (SAMRC) Centre for Tuberculosis Research (CTR) Division of Molecular Biology and Human Genetics (MBHG) Department of Biomedical Sciences Faculty of Medicine and Health Sciences Stellenbosch University Cape Town South Africa Centre for Epidemic Response and Innovation (CERI) School of Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa Centre for Bioinformatics and Computational Biology Stellenbosch University Cape Town South Africa Genomics for Health in Africa (GHA) Africa-Europe Cluster of Research Excellence (CoRE) South Africa National Institute for Theoretical and Computational Sciences (NITheCS) South Africa South Africa

Mitochondria form an integral, yet frequently underappreciated, part of the immune response to Mycobacterium tuberculosis (***), particularly within macrophages. Despite growing recognition for their role in infection and immunity, studies investigating how mitochondrial DNA (mtDNA) variation influences host susceptibility to tuberculosis (TB) are limited. Notably, there are no studies in African-based populations, although Africans possess unparalleled human genetic diversity, including the earliest diverged mitochondrial haplogroups, and a high TB burden. This underrepresentation limits the discovery of novel ancestry-specific genetic loci associated with TB. In this review article, we describe the unique characteristics of mtDNA, highlight key mitochondrial functions relevant to macrophage responses during *** infection, and summarise published studies that investigate the role of host mtDNA variation in TB susceptibility. We further advocate for the inclusion of African populations in future studies to identify novel TB susceptibility genetic risk loci and expand the current knowledgebase on host TB susceptibility. © 2025 The Authors

关键词： Mitochondria Mitochondrial DNA (mtDNA) variation Mycobacterium tuberculosis (M.tb) Tuberculosis (TB) susceptibility

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Reply to Žárský and Eliáš

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Current biology : CB 2025年第5期35卷 R173-R174页

作者： Maaike J Bierenbroodspot Tatyana Darienko Sophie de Vries Thomas Pröschold Iker Irisarri Jan de Vries University of Goettingen Institute for Microbiology and Genetics Department of Applied Bioinformatics Goettingen Germany. University of Goettingen Institute for Microbiology and Genetics Department of Applied Bioinformatics Goettingen Germany University of Goettingen Albrecht-von-Haller-Institute for Plant Sciences Department of Experimental Phycology and Culture Collection of Algae Goettingen Germany. Research Department for Limnology University of Innsbruck Mondsee Austria. Department of Biodiversity and Evolutionary Biology Museo Nacional de Ciencias Naturales Madrid Spain Campus Institute Data Science University of Goettingen Goettingen Germany. Electronic address: Iker.Irisarri@mncn.csic.es. Campus Institute Data Science University of Goettingen Goettingen Germany Goettingen Center for Molecular Biosciences (GZMB) Department of Applied Bioinformatics University of Goettingen Goettingen Germany. Electronic address: devries.jan@uni-goettingen.de.

The more we learn about the algal relatives of embryophytes (land plants), the more complex and fascinating the evolution of key streptophyte traits becomes. With the sequencing of the Klebsormidium nitens genome, a range of molecular traits shared with land plants were found - despite ca. 800 million years of divergence. A few years ago, Streptofilum was described, superficially resembling members of Klebsormidiophyceae but showing divergence in traits. In our recent study, we reconstructed a phylogenomic backbone for Klebsormidiophyceae, and Streptofilum was nested within, although we pointed out the difficulties in placing Streptofilum. Now, Žárský and Eliáš have commented on this placement, building on a different approach to analyze the data.

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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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Semantic Answer Type Prediction by Using BERT classifier and Rule-based Ranking Strategies 2

Semantic Answer Type Prediction by Using BERT classifier and...

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2nd SeMantic Answer Type and Relation Prediction Task at ISWC Semantic Web Challenge, SMART 2021

作者： Ning, Xiao Ammar, Ammar Yilmaz, Arif Mehryar, Shervin Celebi, Remzi Institute of Data Science Maastricht University Maastricht Netherlands Department of Bioinformatics Maastricht University Maastricht Netherlands School of Biological Science and Medical Engineering Southeast University China University of Toronto Toronto Canada

A key task in Question Answering (QA) is answer type prediction in which the type of the answer expected to a question expressed in natural language is predicted in order to improve overall search and retrieval performance. Answers might be of many different types as natural language is ambiguous and a question might correspond to different relevant queries. The task of predicting granular answer types for a question from a big ontology is a greater challenge due to many possible classes. In this paper, we focus on semantic answer type prediction where the candidate types come from a general-purpose ontology. We propose a model that is trained on the datasets provided for the International Semantic Web Conference (ISWC 2021) SMART Task Challenge. We model the problem as a two-stage pipeline of sequence classification tasks (answer category prediction, answer resource type prediction), each one making use of a fine-tuned BERT classifier. To cope with the highly skewed distribution of answer types in the resource category, the BERT classifier is enhanced with a rule-based ranking strategy. On the DBpedia dataset, we obtain an accuracy score of 0.985 for the answer category prediction, 0.737 of NDCG@5 and 0.702 of NDCG@10 for the answer type prediction. © 2022 CEUR-WS. All rights reserved.

关键词： Ontology

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Systems Biology of Streptophyte Cell Evolution

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Annual review of plant biology 2025年 2025 Jan 16页

作者： Elisa S Goldbecker Jan de Vries 1Department of Applied Bioinformatics Institute for Microbiology and Genetics University of Göttingen Göttingen Germany email: elisa.goldbecker@uni-goettingen.de devries.jan@uni-goettingen.de. 2Campus Institute Data Science (CIDAS) University of Göttingen Göttingen Germany. 3Department of Applied Bioinformatics Goettingen Center for Molecular Biosciences (GZMB) University of Göttingen Göttingen Germany.

More than 500 million years ago, a streptophyte algal population established a foothold on land and started terraforming Earth through an unprecedented radiation. This event is called plant terrestrialization and yielded the Embryophyta. Recent advancements in the field of plant evolutionary developmental biology (evo-devo) have propelled our knowledge of the closest algal relatives of land plants, the zygnematophytes, highlighting that several aspects of plant cell biology are shared between embryophytes and their sister lineage. High-throughput exploration determined that routes of signaling cascades, biosynthetic pathways, and molecular physiology predate plant terrestrialization. But how do they assemble into biological programs, and what do these programs tell us about the principal functions of the streptophyte cell? Here, we make the case that streptophyte algae are unique organisms for understanding the systems biology of the streptophyte cell, informing on not only the origin of embryophytes but also their fundamental biology.

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