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Information Technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Faisal Asadi Gregorius Natanael Elwirehardja Joko Pebrianto Trinugroho Reza Rahutomo Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

The Ministry of Health of Indonesia has referred to pre-eclampsia as one of the most severe diseases affecting women. As an urgency, it is crucial to administrate pre-eclampsia cases for disease prevention as a long-term national healthcare strategy. Regarding health science, case data was significant in developing research and innovation. However, the main problem regarding pre-eclampsia case administration is data handling, recording, and management incompetence. Hence, this research proposed a conceptual design of a database for pre-eclampsia case administration. The proposed design covered conceptual, logical, and physical design. We elaborate the concept into three concepts of pre-eclampsia disease: pre-treatment, treatment, and post-treatment. This study proposed a solution to gain more data and study pre-eclampsia disease in Indonesia.

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Identification and evaluation of multi-antigenic epitopes of immunodominant protein from the selected Crimean–Congo hemorrhagic fever virus genome towards the development of diagnostic and vaccine candidates by reverse vaccinology approach

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Journal of Proteins and Proteomics 2024年第4期15卷 625-634页

作者： Jeyachandran, Akshay Muthuvel, Reshma Jagannathan, Selvaraj Pillai, Sarika Baburajan Rajendran, Vijayakumar Pachamuthu, Rahul Gandhi Balakrishnan, Ajithkumar Vedagiri, Hemamalini Channappa, Shivanandappa Kukkaler Premkumar, Ananda Arona Sakthivel, Sivakumar Mandal, Abhishek Department of Microbiology Pasteur Institute of India Coonoor The Nilgiris India Centre for Advanced Studies in Crystallography and Biophysics University of Madras Chennai India Computational and Data Sciences Indian Institute of Science Bangalore India The Centre for Nanoscience and Technology Pondicherry University Puducherry India Department of Bioinformatics Bharathiar University Coimbatore India Pasteur Institute of India Coonoor India Department of Biotechnology Pondicherry University Puducherry India

Crimean–Congo hemorrhagic fever (CCHF) is a severe zoonotic disease caused by the CCHF virus (CCHFV) belonging to the family of Bunyaviridae. It spreads through tick bites or direct contact with infected animals, affecting both humans and animals. Despite its high mortality rate, there is currently no vaccine available for the treatment of CCHF. To address this gap, we focused on the antigenic glycoprotein precursor (GPC) of CCHFV. This present study aims to identify potential T-cell epitopes that could serve as vaccine candidates. Using bioinformatics tools, epitopes were predicted for both major histocompatibility complex (MHC) classes relevant to the Indian population. These predicted epitopes were then evaluated for antigenicity, immunogenicity, toxicity, and conservancy. Ten epitopes with the highest binding scores were selected and modeled. These epitopes were subsequently docked with their respective HLA allele proteins. Notably, two epitopes—KLPPEIITL (MHC-1) and CKVIFYLLIIFGTLG (MHC-2)—demonstrated significant binding affinity with HLA alleles HLA-A*24:02 and HLA-DRB1*15:01, respectively. Furthermore, molecular dynamics simulations revealed that both epitopes exhibited higher stability when interacting with their respective HLA allele proteins. These findings suggest that these epitopes are promising candidates for developing an epitope-based vaccine against Crimean–Congo hemorrhagic fever (CCHF).

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Machine Learning Implementations in Childhood Stunting Research: A Systematic Literature Review

Machine Learning Implementations in Childhood Stunting Resea...

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International Conference on Information Management and Technology (ICIMTech)

作者： Reza Rahutomo Gregorius Natanael Elwirehardja Mahmud Isnan Faisal Asadi Bens Pardamean Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University 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

Childhood stunting is a condition anticipated to affect the growth potential of children under the age of five. With numerous stunting researches that have been conducted, stunting datasets are now widely available to facilitate stunting research. This provides an opportunity to implement machine learning (ML) principles to produce a broader insight or a novel technique in stunting prediction. A systematic literature review is necessary to discover the landscape of machine learning implementation in the application domain as a preliminary study for creating an effective research roadmap. This paper presents a systematic literature review (SLR) of 22 curated manuscripts that focuses on identifying the ML models applied in stunting research, as well as the datasets used in such studies that were published during 2017–2022. The SLR process found that ML principles have been applied in stunting research since 2017, and the diversity of ML implementation has become more varied in 2021–2022. In terms of ML models, XGBoost and Random Forest are recognized as the two most utilized models, and stunting prediction is the most common ML implementation. The majority of stunting research utilizing ML has been conducted in Indonesia. Although national survey data has been the most commonly utilized dataset in stunting research, researchers in Indonesia have shown a preference for utilizing data from regional or independent surveys. This study will be followed by developing a classifier model for stunted children using XGBoost and Random Forest algorithms. The model will be trained on a dataset generated from StuntingDB.

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Expert system for oil palm leaves deficiency to support precision agriculture 6

Expert system for oil palm leaves deficiency to support prec...

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

作者： Putra, Dian Pratama Bimantio, Prasanto Suparyanto, Teddy Pardamean, Bens Institute of Agriculture STIPER Agrotechnology Department Yogyakarta55281 Indonesia Institute of Agriculture STIPER Agricultural Engineering Department Yogyakarta55281 Indonesia Research Center Bina Nusantara University Bioinformatics and Data Science Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Expert systems are an option in the implementation of agricultural cultivation, which can provide comprehensive information to make crop cultivation more precise. The expert program currently used in oil palm plants is still unable to provide accurate information on actual deficiencies. Thus, there is a need for an expert system that can provide valid information. This research creates an expert system in agriculture that can help farmers to make decisions by knowing the types of nutrient deficiencies in oil palm. Besides that, it can help the process of disseminating information and knowledge through applications that can be accessed at any time by farmers to save time and money and support their existence. Edge detection using the Sobel method and the Robert method using the High Pass Filter (HPF) is the basis for this expert system creation method. This expert system can be used for any type of mature oil palm plant where yellow spots are found, due to a lack of nutrients or deficiency. Diseases that arise due to deficiencies depend on each nutrient, in this study all deficiencies are specific to the upper part of the plant, namely leaves. Tests carried out 15 times resulted in the deficiency most often raised by oil palm trees due to the lack of primary essential nutrients, namely nitrogen nutrients. The validation assessment is then carried out by an expert (human) directly and assesses the deficiency directly, then from the results of the test and assessment, there are 11 images from 15 images that match the assessment by the expert. © 2021 IEEE.

关键词： High pass filters

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Systematic Literature Review On Machine Learning Predictive Models For Indoor Climate In Smart Solar Dryer Dome

Systematic Literature Review On Machine Learning Predictive ...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Karli Eka Setiawan Gregorius Natanael Elwirehardja Bens Pardamean Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665453967

Precise predictions of indoor climate conditions are required in the implementation of Smart Solar Dryer Dome (SDD). Trend development of prediction models is discussed in this review from 15 selected research papers (2018–2022) on indoor climate prediction which was obtained from research paper databases The output shows that the most used model for predicting indoor climate is Artificial Neural Network (ANN), especially Recurrent Neural Network (RNN) such as LSTM and GRU. However, there are some potential methods such as Transformer, Combined Support Vector Machines (SVM)-Deep Learning, and sequence-to-sequence which could outperform other commonly used models. Based on findings various opportunities exist to improve the precision of indoor climate prediction, which can bring power consumption efficiency and others benefit to Smart SDD users. Such studies may further be explored to produce more accurate machine learning models.

关键词： Support vector machines Systematics Recurrent neural networks Power demand Bibliographies Artificial neural networks Predictive models

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

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

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

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

ISBN: (纸本)9781665473286

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.

关键词： Epidemics Temperature distribution Rain Meteorological factors Multimedia systems Time series analysis Weather forecasting

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Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare

arXiv

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arXiv 2024年

作者： Liu, Mingxuan Ning, Yilin Ke, Yuhe Shang, Yuqing Chakraborty, Bibhas Ong, Marcus Eng Hock Vaughan, Roger Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Department of Anaesthesiology and Perioperative Medicine Singapore General Hospital Singapore Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Department of Statistics and Data Science National University of Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Department of Emergency Medicine Singapore General Hospital Singapore Singapore Institute of Data Science National University of Singapore Singapore Singapore

Background The escalating integration of machine learning in high-stakes fields such as healthcare raises substantial concerns about model fairness and interpretability. Conventional bias-mitigation techniques often sacrifice model performance (e.g., accuracy), limiting their impact on aiding decision-making. This study introduces an interpretable bias mitigation framework, Fairness-Aware Interpretable Modeling (FAIM), aiming to balance model performance and fairness. Methods We propose the FAIM framework, which prioritizes fairness in predictive models that are nearly optimal in performance. Initiating with a performance-optimized model yet without explicit fairness considerations (i.e., "fairness-unaware"), we construct a set of nearly-optimal models (i.e., Integral Rashomon Set) to select a fairer alternative (i.e., "fairness-aware"). To identify this fairness-aware model, we introduce a fairness ranking index (FRI) to comprehensively rank model fairness, which encompasses multiple fairness metrics including equalized odds, equal opportunity, and equality of balanced error rate. This index is complemented by an interactive graphical interface that facilitates the incorporation of clinical expertise to guide model selection with contextualized fairness. Moreover, we integrate SHapley Additive exPlanations (SHAP) within FAIM to compare variable contributions between the fairness-unaware and fairness-aware models, thereby uncovering model modifications made to address fairness. We demonstrated the value of FAIM by predicting hospital admission using two real-world databases, MIMIC-IV-ED and SGH-ED, to test its performance in bias mitigation, particularly for sex and race. We gauged model performance via AUC, sensitivity and specificity, and evaluated model fairness via the aforementioned fairness metrics. Results For both datasets, the fairness-aware models yielded by FAIM automatically excluded race and reduced the contribution of sex and other bias-related variabl

关键词： Machine learning

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Determining Variables Associated with Annual Oil Palm Yield: An Explainable Gradient Boosting Approach

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Procedia Computer science 2023年 227卷 262-271页

作者： Gregorius Natanael Elwirehardja Teddy Suparyanto Miftakhurrokhmat Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia 55282 Faculty of Industrial Technology Universitas Islam Indonesia Yogyakarta Indonesia 55584 Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

With the growing demands for Precision Agriculture (PA) in Indonesia, researchers have evaluated the utilization of Machine Learning for predicting oil palm yields and determining variables affecting them. Previous studies showed that meteorological variables, including rainfalls and wind speed, are associated with oil palm yields. In this research, the Extreme Gradient Boosting (XGBoost) model and the Shapley Additive exPlanations (SHAP) were deployed for analyzing the importance of 15 agrometeorological variables in predicting oil palm yield. The best model attained 1.911 RMSE and 0.855 R2. By analyzing the weights and gains of the XGBoost model along with the SHAP values, it was found that the yield in the previous year, the age and number of plants, the area of peat lands, and meteorological parameters such as rainfall rates and the number of rainy days in the previous three years were considered important. The previous year's yield in particular possesses the greatest weight and gain values according to the model, and the highest SHAP value among all input variables. However, the meteorological data used in this research are only limited to rainfall rates and the number of rainy days. In the future, more diverse variables can be analyzed.

关键词： Oil Palm Yield Machine Learning XGBoost XAI SHAP

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 6

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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6th International Conference on Advances in Artificial Intelligence, ICAAI 2022

作者： Dzulkalnine, Mohamad Faiz Mohamad, Mohd Saberi Choon, Yee Wen Remli, Muhammad Akmal Alashwal, Hany Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Johor81310 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Big Data Analytics Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Data Science Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al-Ain United Arab Emirates

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms' cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy. © 2022 Association for Computing Machinery.

关键词： Ethanol

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Docking of Human Band 3 Anion Transporter Proteins with Their Plasmodium falciparum Interactors Based on Short Linear Motifs

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American Journal of Molecular Biology 2024年第4期14卷 187-200页

作者： Fatoumata Gniné Fofana Ayoub Ksouri Cheickna Cisse Oussema Souiai Alia Benkahla Hedmon Okella Mamadou Sangare Jeffrey G. Shaffer Seydou Doumbia Mamadou Wele African Center of Excellence in Bioinformatics University of Sciences Techniques and Technologies of Bamako (USTTB) Bamako Mali Laboratory of BioInformatics BioMathematics and BioStatistics (BIMS) Institut Pasteur de Tunis University of Tunis El Manar Tunis Tunisia School of Veterinary Medicine University of California Davis Tulare USA Department of Biostatistics and Data Science Tulane University School of Public Health and Tropical Medicine New Orleans USA University Clinical Research Centre (UCRC) USTTB Bamako Mali

Plasmodium (P.) falciparum is a pathogen that causes severe forms of malaria. Protein interactions have been shown to occur between P. falciparum and human erythrocytes in human blood. The Band 3 Anion Transporter (B3AT) protein is considered the main invasive pathway for the parasite in erythrocytes that causes clinical symptoms for malaria in humans. The interactions between P. falciparum parasites and erythrocytes along this receptor have previously been explored. Short linear motifs (SLIMs) are short linear mediator sequences that involve several biological processes, acting as mediators of protein interactions identifiable by computational tools such as SLiMFinder. For a given protein, the identification of SLIMs allows predicting its interactors. Using the SLIMs approach, protein-protein interaction network analyses between P. falciparum and its human host, were used to identify a tryptophan-rich protein, A5K5E5_PLAVS as an essential interactor of B3AT. To better understand the interaction mechanism, a guided protein-protein docking approach based on SLIM motifs was performed for human B3AT and A5K5E5_PLAVS. The highlights of this important interaction between P. falciparum and its human host have the potential to pave the way to identify new therapeutic candidates.

关键词： Malaria Protein-Protein Docking Protein-Protein Interaction Short Linear Motifs

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