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

作者： Purwandari, Kartika Perdana, Reza Bayu Sigalingging, Join W.C. Rahutomo, Reza Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta10720 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Although Twitter is a popular platform for social interaction analysis and text data mining, it faces challenges with geolocation automation. To address this problem, the researchers propose the utilization of a Support Vector Machine (SVM) model to develop an automated Twitter crawling system. The system aims to collect data related to weather in Indonesia by employing Twin, a Python-based Twitter scraping software. To overcome null geolocations, the study incorporates aliases created based on the common practice of Indonesian users mentioning the country's location in tweets. The results demonstrate that the SVM model, combined with automated smart crawling, achieves an 85% accuracy rate. © 2023 The Authors. Published by Elsevier B.V.

关键词： smart crawling Social media SVM Twitter weather

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Developmental systems biology flourishing on new technologies

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Journal of Genetics and Genomics 2008年第10期35卷 577-584页

作者： Jing-Dong J. Han Yi Liu Huiling Xue Kai Xia Hong Yu Shanshan Zhu Zhang Chen Wei Zhang Zheng Huang Chunyu Jin Bo Xian Jing Li Lei Hou Yixing Han Chaoqun Niu Timothy C. Alcon Chinese Academy of Sciences Key Laboratory of Molecular Developmental Biology Center for Molecular Systems Biology Institute of Genetics and Developmental Biology Chinese Academy of Sciences Beijing 100101 China Key Laboratory of Machine Perception （Ministry of Education） Peking University Beijing 100871 China Bioinformatics and Computational Biology Graduate Program Iowa State University Ames IA 50011-1040 USA

Organism development is a systems level process. It has benefited greatly from the recent technological advances in the field of systems biology. DNA microarray, phenome, interactome and transcriptome mapping, the new generation of deep sequencing technologies, and faster and better computational and modeling approaches have opened new frontiers for both systems biologists and developmental biologists to reexamine the old developmental biology questions, such as pattern formation, and to tackle new problems, such as stem cell reprogramming. As showcased in the International Developmental Systems Biology Symposium organized by Chinese Academy of Sciences, developmental systems biology is flourishing in many perspectives, from the evolution of developmental systems, to the underlying genetic and molecular pathways and networks, to the genomic, epigenomic and noncoding levels, to the computational analysis and modeling. We believe that the field will continue to reap rewards into the future with these new approaches.

关键词： developmental systems biology new technologies symposium

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Dual functions of an exosome component protein Rrp46/CRN-5 in RNA degradation and DNA fragmentation

Dual functions of an exosome component protein Rrp46/CRN-5 i...

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亚洲晶体学联合大会

作者： Yu-Yuan Hsiao Lyudmila G.Doudeva Hanna S.Yuan Institute of Molecular Biology Academia Sinica Institute of Bioinformatics and Structural Biology National Tsing Hua University Graduate Institute of Biochemistry and Molecular Biology National Taiwan University Taiwan Institute of Chemical Biology and Molecular Biophysics Taiwan International Graduate ProgramAcademia Sinica

Rrp46 is one of the RNase PH family proteins, first identified as a component protein of the eukaryotic exosome, a protein complex involved in RNA turnover, surveillance and processing. The Rrp46 homolog, CRN-5, was s... 详细信息

关键词： DNase Dual functions of an exosome component protein Rrp46/CRN-5 in RNA degradation and DNA fragmentation DNA exosome RNA

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Jasmonate perception: Ligand-receptor interaction, regulation, and evolution

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Molecular Plant 2023年第1期16卷 23-42页

作者： Shuai Hu Kaiming Yu Jianbin Yan Xiaoyi Shan Daoxin Xie MOE Laboratory of Bioinformatics Tsinghua-Peking Center for Life SciencesSchool of Life SciencesTsinghua UniversityBeijing 100084China Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program School of Life SciencesTsinghua UniversityBeijing 100084China Shenzhen Branch Guangdong Laboratory of Lingnan Modern AgricultureKey Laboratory of Synthetic BiologyMinistry of Agriculture and Rural AffairsAgricultural Genomics Institute at ShenzhenChinese Academy of Agricultural SciencesShenzhen 518124China Kunpeng Institute of Modern Agriculture at Foshan Chinese Academy of Agricultural SciencesFoshan 528200China

Phytohormones integrate external environmental and developmental signals with internal cellular responses for plant survival and multiplication in changing ***(JA),which might originate from prokaryotes and benefit plant terrestrial adaptation,is a vital phytohormone that regulates diverse developmental processes and defense responses against various environmental *** this review,we first provide an overview of ligand-receptor binding techniques used for the characterization of phytohormone-receptor interactions,then introduce the identification of the receptor COI1 and active JA molecules,and finally summarize recent advances on the regulation of JA perception and its evolution.

关键词： jasmonate COI1 hormone perception ligand-receptor interaction ligand-receptor binding techniques evolution

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IoT for Water Quality Categorization 5

IoT for Water Quality Categorization

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5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Sastrohartono, Hermantoro Krisdiarto, Andreas Wahyu Uktoro, Arief Ika Rahutomo, Reza Suparyanto, Teddy Pardamean, Bens Institute of Agriculture Stiper Faculty of Agricultural Technology Agricultural Engineering Department Yogyakarta Indonesia Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics & Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350369489

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results. © 2023 IEEE.

关键词： Internet of things

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Learning natural selection from the site frequency spectrum

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17th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2013

作者： Ronen, Roy Udpa, Nitin Halperin, Eran Bafna, Vineet Bioinformatics Graduate Program University of California San Diego CA United States International Computer Science Institute Berkeley CA United States Blavatnik School of Computer Science Tel-Aviv University Tel-Aviv Israel Department of Molecular Microbiology and Biotechnology Tel-Aviv University Tel-Aviv Israel Department of Computer Science and Engineering University of California San Diego CA United States

ISBN: (纸本)9783642371943

Genetic adaptation to external stimuli occurs through the combined action of mutation and selection. A central problem in genetics is to identify loci responsive to specific selective pressures. Over the last two decades, many tests have been proposed to identify genomic signatures of natural selection. However, the power of these tests changes unpredictably from one dataset to another, with no single dominant method. We build upon recent work that connects many of these tests in a common framework, by describing how positive selection strongly impacts the observed site frequency spectrum (SFS). Many of the proposed tests quantify the skew in SFS to predict selection. Here, we show that the skew depends on many parameters, including the selection coefficient, and time since selection. Moreover, for each of the different regimes of positive selection, informative features of the scaled SFS can be learned from simulated data and applied to population-scale variation data. Using support vector machines, we develop a test that is effective over all selection regimes. On simulated datasets, our test outperforms existing ones over the entire parameter space. We apply our test to variation data from Drosophila melanogaster populations adapted to hypoxia, and identify new loci that were missed by previous approaches, but strengthen the role of the Notch pathway in hypoxia tolerance. © 2013 Springer-Verlag.

关键词： Genome

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Database management system design for automatic weather information with twitter data collection 6

Database management system design for automatic weather info...

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

作者： Purwandari, Kartika Perbangsa, Anzaludin Samsinga Sigalingging, Join W. C. Krisna, Adhi Asta Anggrayani, Suci Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Meteorological Climatological and Geophysical Agency Database Center Division of Bmkg Jakarta10720 Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Improvement of data management about weather in Indonesia is very supportive of the need for weather information. Currently, the weather information that is disseminated to the public is only based on sensor location points that are spread across Indonesia. However, supporting data generated from these sensors is only limited to certain areas. We hence develop a database system that can help collect weather information automatically via Twitter. This facilitates the Indonesian people in knowing weather information based on the completeness of data from sensor data and Twitter. The steps from the database that are built will be very helpful in collecting data about the weather throughout Indonesia. The basic step that we use in our database are conceptual, logical and physical design. The information obtained in this study is very important for efficient and effective data collection to achieve real-time automation of weather information. © 2021 IEEE.

关键词： Database systems

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Implementation of Computer Vision of Jakarta Weather Image Categorization Using ResNet 8

Implementation of Computer Vision of Jakarta Weather Image C...

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

作者： Purwandari, Kartika Sigalingging, Join W.C. Hidayat, Alam Ahmad Cenggoro, Tjeng Wawan Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta10720 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

At the moment, weather data is crucial for supporting neighborhood activities. The economy and trade are both centered in Jakarta, which is also Indonesia's capital. Therefore, it is crucial to have access to weather information so that these activities don't get disrupted, which would then hinder commercial and trade activity. Social media has been a very popular tool for spreading information recently. Particularly on Instagram, where users favor taking images and sharing the information they encounter. @jktinfo is the Instagram account that posts information about the situation in Jakarta and the area, including the current weather. The @jktinfo account is utilized in this project to gather data. Utilizing a variety of techniques, the collected photographs of sunny, cloudy, and wet situations were. © 2023 The Authors. Published by Elsevier B.V.

关键词： computer vision data augmentation deep transfer learning residual network weather image categorization

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Utilizing Semi-supervised Method in Predicting BRCA1 Pathogenicity Variants

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Procedia Computer Science 2023年 227卷 36-45页

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

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.

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

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Data Mining for Predicting Customer Satisfaction Using Clustering Techniques

Data Mining for Predicting Customer Satisfaction Using Clust...

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International Conference on Information Management and Technology (ICIMTech)

作者： Kartika Purwandari Join W. C. Sigalingging Muhammad Fhadli Shinta Nur Arizky Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia National Central University Taoyuan Taiwan BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728170718

ISBN: (纸本)9781728170725

Managing customer satisfaction has become an important business trend, including restaurants business. This study aims to determine the application of the K-means, Spectral Clustering (SC), and Agglomerative Clustering (AC) method for measuring customer satisfaction on a family restaurant in Taiwan. We contribute the data collection process and application of data mining in a family restaurant. The clustering analysis based on agglomerative clustering approach performs as well as the K-means approach to cluster the same characteristics of the customers. At last, this study shows the measurement result of customer satisfaction and provides improvement suggestion to the restaurant concerned.

关键词： Data mining Customer satisfaction Principal component analysis Computer science Reliability Data visualization

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