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Data Mining for the Global Multiplex Weekly Average Income Analysis

Procedia Computer Science 2023年 219卷 52-59页

作者： Nicholas Dominic Gregorius Natanael Elwirehardja Bens Pardamean 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

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.

关键词： movie multiplex income data mining Business Intelligence dashboard

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Decision Theory and Risk Simulation Analysis for Optimizing Profit in PayLater Services

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Procedia Computer Science 2023年 219卷 60-67页

作者： Nicholas Dominic Bens Pardamean 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

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.

关键词： PayLater decision-making Bayesian analysis fault tree analysis critical path method Monte Carlo simulation

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Single-Cell RNA-Seq Data Clustering: Highlighting Computational Challenges and Considerations

Single-Cell RNA-Seq Data Clustering: Highlighting Computatio...

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2023 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2023

作者： Nossier, Mohamad Moussa, Sherin M. Badr, Nagwa Ain Shams University Faculty of Computer and Information Sciences Program of Bioinformatics Department of Information Systems Cairo11566 Egypt Cairo11837 Egypt Ain Shams University Faculty of Computer and Information Sciences Department of Information Systems Cairo11566 Egypt

ISBN: (纸本)9798350337488

Recent advancements in single-cell transcriptomics have revolutionized data generation, resulting in the production of vast amounts of single-cell RNA sequencing (scRNA-seq) data. The intricate biology of tissues and organs may be better understood through the analysis of such data, which aims to not only identify known cell types but also uncover novel ones. In this paper, we outline the standard single-cell RNA-seq data analytic process with particular focus on the critical role of unsupervised clustering, since it has a significant influence over subsequent analyses and the resulting biological insights. However, navigating the computational landscape for clustering single-cell transcriptomic data poses significant challenges. Thus, in this article, we provide a simplified exploration of key computational challenges and considerations within the realm of scRNA-seq data clustering, emphasizing the application and relevance of selected unsupervised methods in current research, while acknowledging the potential for further investigation of the biological aspects in this field. Furthermore, we present recommendations on how such challenges may be addressed. This work not only sheds light on the complexities of scRNA-seq data cluster analysis, but also provides insights into how to effectively address and surmount these challenges. © 2023 IEEE.

关键词： Clustering Data Analysis RNA-sequencing Single-cell Transcriptomics Unsupervised learning

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Understanding the binding of the same ligand to GPCRs of different families

Understanding the binding of the same ligand to GPCRs of dif...

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2021 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2021

作者： Dankwah, Kwabena Owusu Mohl, Jonathon E Begum, Khodeza Leung, Ming-Ying The University of Texas at El Paso Computational Science Program El Paso United States The University of Texas at El Paso Bioinformatics Program Computational Science Program Dept. of Mathematical Sciences El Paso United States The University of Texas at El Paso Bioinformatics Program Border Biomedical Research Center El Paso United States

ISBN: (纸本)9781665401265

G protein-coupled receptors (GPCRs) are the largest class of cell-surface receptor proteins with important functions in signal transduction and often serve as therapeutic drug targets. With the rapidly growing public data on three dimensional (3D) structures of GPCRs and GPCR-ligand interactions, computational prediction of GPCR ligand binding becomes a convincing option to high throughput screening and other experimental approaches during the beginning phases of ligand discovery. Such predictions are cost-efficient and can be important aides for planning wet lab experiments to help elucidate signaling pathways and expedite drug discovery. There are existing computational tools for GPCR ligand binding prediction using various sequence and structural derived features. However, these methods have been typically tested on specific families of GPCRs and none has focused on features that characterize binding of a single ligand to multiple GPCR families. In this work, we set out to uncover and understand the binding of the same ligand to multiple GPCRs of different families. As expected only a few GPCRs share a conserved sequence motif. We observed local 3D structural similarities and local sequence similarities. We also observed that the GPCRs within the same family share similar binding pockets for the ligand. Molecular docking revealed that a ligand can bind to GPCRs of different families with different poses but similar conformations. Finally, the ligands bind to similar pockets with similar electrostatic and solvation properties and share similar residues. These findings can be exploited to improve protein function inference, drug repurposing and drug toxicity prediction, and accelerate the development of new drugs. © 2021 IEEE.

关键词： Ligands

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VirusT5: Harnessing Large Language Models to Predicting SARS-CoV-2 Evolution

arXiv

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arXiv 2024年

作者： Marathe, Vishwajeet Bajracharya, Deewan Yan, Changhui The Department of Computer Science Department of North Dakota State University FargoND58102 United States The Genomics Phenomics and Bioinformatics Program of North Dakota State University FargoND58102 United States The Department of Computer Science and the Genomics Phenomics and Bioinformatics Program of North Dakota State University FargoND58102 United States

During a virus's evolution, various regions of the genome are subjected to distinct levels of functional constraints. Combined with factors like codon bias and DNA repair efficiency, these constraints contribute to unique mutation patterns within the genome or a specific gene. In this project, we harnessed the power of Large Language Models (LLMs) to predict the evolution of SARS-CoV-2. By treating the mutation process from one generation to the next as a translation task, we trained a transformer model, called VirusT5, to capture the mutation patterns underlying SARS-CoV-2's evolution. We evaluated the VirusT5's ability to detect these mutation patterns including its ability to identify mutation hotspots and explored the potential of using VirusT5 to predict future virus strains. Our findings demonstrate the feasibility of using a large language model to model viral evolution as a translation process. This study establishes the groundbreaking concept of "mutation-as-translation," paving the way for new methodologies and tools in combating viral threats. © 2024, CC BY.

关键词： Genome

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Literature study of stunting supplementation in Indonesian utilizing text mining approach 7

Literature study of stunting supplementation in Indonesian u...

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7th International Conference on Computer Science and Computational Intelligence, CSCI 2022

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

Numerous research on stunting supplementation interventions in Indonesia have been published. The information can be extracted through data mining, especially from academic research databases. In this paper, we presented a text mining-based literature review strategy to create a pipeline that researchers can use to accelerate the development of stunting supplementation intervention research in Indonesia. Utilizing various NLP (Natural Language Processing) techniques, data were crawled, processed, and visualized using Python. The crawling dataset used a module from the Pubmed API (Application programming Interface) to collect literature papers. The NLTK (Natural Language Toolkit) module and itertools were used to process text data. The n-grams model was applied to process tokens into bigrams and trigrams. Text information was visualized using Matplotlib and Word cloud packages. There is an increasing number of publication in stunting supplementation intervention according to our result, which was observed from 2015 to 2021. West Java was the province where most of the stunting research has been conducted, as determined by research abstracts. Top occurrences obtained from the bigram and trigrams models calculation produced different terms. The word pairings that occurred the most frequently in the bigram and trigram model analyses were "child-aged" and "iron-folic-acid," respectively. The findings of this study are expected to help researchers to obtain the latest research topics related to stunting supplementation interventions in Indonesia. © 2022 Elsevier B.V.. All rights reserved.

关键词： Data mining

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Towards a Healthy AI Tradition: Lessons from Biology and Biomedical Science

arXiv

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arXiv 2024年

作者： Kasif, Simon Department of Biomedical Engineering Bioinformatics Graduate Program Department of Computer Science Boston University United States Department of Computer Science Tel Aviv University

AI is a magnificent field that directly and profoundly touches on numerous disciplines ranging from philosophy, computer science, engineering, mathematics, decision and data science and economics, to cognitive science, neuroscience and more. The number of applications and impact of AI is second to none and the potential of AI to broadly impact future science developments is particularly thrilling. While attempts to understand knowledge, reasoning, cognition and learning go back centuries, AI remains a relatively new field. In part due to the fact it has so many wide-ranging overlaps with other disparate fields it appears to have trouble developing a robust identity and culture. Here we suggest that contrasting the fast-moving AI culture to biological and biomedical sciences is both insightful and useful way to inaugurate a healthy tradition needed to envision and manage our ascent to AGI and beyond (independent of the AI Platforms used). After all, the human brain is a biological organ produced by evolution and human intelligence is a remarkable bi-product of nature and nurture and their complex interaction. In this perspective, we focus on traditions and culture, namely the commonly observed practices of evaluating, recognizing applauding, critiquing, debating and managing all progress including useful advances and discovery of challenging limitations. We are not discussing specific scientific exchanges between AI and Biology that include interdisciplinary cross fertilization of scientific methods, technology, ideas and applications that have been amply demonstrated and will continue to be transformative in the future. In a previous perspective, we suggested that biomedical laboratories or centers can usefully embrace logistic traditions in AI labs that will allow them to be highly collaborative, improve the reproducibility of research, reduce risk aversion and produce faster mentorship pathways for PhDs and fellows. This perspective focuses on the benefits of AI a

关键词： Biological organs

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LOCOS: A cosine based local gene expression pattern finding algorithm on time-series data

LOCOS: A cosine based local gene expression pattern finding ...

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IEEE International Conference on Big Data

作者： Youjeong Suk Jaemin Jeon Inuk Jung School of Computer Science and Engineering Kyungpook National University Daegu Republic of Korea Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

In gene expression analysis, understanding a biological event that is observed at some time instance often requires capturing genes whose expression levels modulate before and after the event. Such genes are expected to be the responders to the event and will exhibit similar expression patterns during the event. However, after the effect of the event fades, their expression levels may loose their correlation. Hence, it is a non-trivial task to identify genes that share highly similar local expression patterns nearby the event and also allowing some level of divergent expression patterns further away from the event’s time point. Here, we propose LOCOS (LOcal COSine), a novel time-course clustering algorithm tailored to cluster genes exhibiting similar expression patterns within a specified time interval. LOCOS is an extension of the traditional Non-negative Matrix Factorization (NMF), which incorporates the cosine similarity and Euclidean distances in its update procedure. LOCOS maximizes the cosine similarity within an interval of interest, while allowing a relaxed minimization of Euclidean distance outside it. Using synthetic and non-biological time-course data, we showed that LOCOS was able to correctly detect the known local patterns within a specified interval. Furthermore, we used longitudinal single-cell RNA-seq samples from four patients showing deteriorating and recovering health conditions to identify genes related to each of the phenotype. As a result, LOCOS was able to capture gene clusters with distinct expression patterns that aligned with the intervals embedding the clinical deterioration and recovery events.

关键词： Phenotypes Correlation Clustering algorithms Euclidean distance Big Data Minimization Biology Gene expression

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Methacrylic acid/Tween 80 engineered amorphous calcium phosphate as an effective bioactive reinforcing nanofillers in dental adhesive

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Dental Materials Journal 2025年第2期44卷 190-200页

作者： Maimansomsuk, Siripatsorn Teangrom, Paremai Teanchai, Chayada Sinthuvanich, Chomdao Ruengket, Pakorn Attarataya, Jakrada Akkarachaneeyakorn, Khrongkhwan Department of Chemistry Faculty of Science Kasetsart University Bangkok10900 Thailand Center of Excellence for Innovation in Chemistry Faculty of Science Kasetsart University Bangkok10900 Thailand Research Center Faculty of Dentistry Mahidol University Bangkok10400 Thailand Department of Biochemistry Faculty of Science Kasetsart University Bangkok Thailand Faculty of Science Kasetsart University Bangkok Thailand Genetic Engineering and Bioinformatics Program Graduate School Kasetsart University Bangkok Thailand Nakhon Ratchasima30000 Thailand

Bioactive fillers in dental adhesives are designed to release beneficial ions, such as calcium and phosphate, to help in remineralization process, and preventing secondary caries. Their incorporation also aims to improve the longevity of dental restorations. Herein, CaP nanoparticles were synthesized through a water-in-oil emulsion method using Tween80 as a surfactant and methacrylic acid (MAA). MAA officiated as a co-surfactant and reduced the size of CaP nanoparticle, including improved compatibility with the components of dental adhesive. The incorporation of 2% T80/CaP/MAA nanofillers into dental adhesive demonstrated increased efficiency, as evidenced by a higher degree of conversion (%DC) and greater micro-tensile bond strength (μTBS). More importantly, T80/CaP/ MAA nanofillers were able to induce remineralization process in the dental structure, which was confirmed by EDX mapping and IR imaging. Finally, a cytotoxicity assessment of the dental adhesive containing T80/CaP/MAA showed no significant adverse effects on human fibroblast cells. © 2025, Japanese Society for Dental Materials and Devices. All rights reserved.

关键词： Stem cells

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Utilizing Semi-supervised Method in Predicting BRCA1 Pathogenicity Variants 8

Utilizing Semi-supervised Method in Predicting BRCA1 Pathoge...

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

作者： Hidayat, Alam Ahmad Trinugroho, Joko Pebrianto Nirwantono, Rudi Sudigyo, Digdo Pardamean, Bens Department of Mathematics School of Computer Science Bina Nusantara University 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

Quantifying the effect of mutations in the BRCA1 gene is useful for understanding their clinical consequences on breast cancer. Machine learning models can be applied to predict the landscape of protein variant effects that might not be always accessible by experiments. In this work, we propose a simple semi-supervised learning method using a Gaussian mixture model to predict ~90% of the unlabeled missense variants of the BRCA1 gene collected from the ClinVar database. High-quality embeddings are used as a feature of the protein sequences, extracted using the latest pre-trained transformer-based language protein model. A statistical test show that the protein embeddings are effective and robust for predicting pathogenicity. Further, the lower representations of the features are then fed into the semi-supervised model. The prediction performance of the model only for the labeled testing data achieves an AUC score and an accuracy of 79.27% and 71.58%, respectively. Using our defined pathogenic probability score, we find that ~94% of variants in our unlabeled dataset are well-separated into either benign or pathogenic classes according to that scoring. Our scores obtain a moderate Spearman rank correlation with the results of established unsupervised variant effect models. Finally, our approach can potentially be developed for more accurate and biologically reliable predictions of the variant effects. © 2023 The Authors. Published by Elsevier B.V.

关键词： embeddings language model mutation effect protein sequence semi-supervised learning

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