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2nd International Conference on Computer System, Information Technology, and Electrical Engineering, COSITE 2023

作者： Elwirehardja, Gregorius Natanael Isnan, Mahmud Perbangsa, Anzaludin Samsinga Muchtar, Kahlil Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mahmud Isnan Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350343069

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches. © 2023 IEEE.

关键词： Machine learning

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Study of enhancer-promoter interactome during mouse embryonic development with H3K4me3 PLAC-seq

Study of enhancer-promoter interactome during mouse embryoni...

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第十八次中国暨国际生物物理大会

作者： Miao Yu Rongxin Fang Ivan Juric Nathan Zemke Armen Abnousi Rong Hu Ming Hu Bing Ren School of Life Sciences Fudan University Ludwig Institute for Cancer Research Bioinformatics and Systems Biology Graduate Program University of California Department of Quantitative Health Sciences Lerner Research Institute Cleveland Clinic Foundation Center for Epigenomics Department of Cellular and Molecular Medicine University of California

Formation of long-range chromatin loops is a crucial step in transcriptional activation of target genes by distal enhancers. Mapping such structural features can help define target genes for enhancers and annotate non-coding sequence variants linked to human ***, we developed Proximity Ligation-Assisted ChIP-seq (PLAC-seq), which detects high-resolution long-range chromatin interactions at selected genomic regions through combination of in situ Hi-C and chromatin IP. We also developed a computational method, MAPS (Model-based Analysis of PLAC-seq and HiChIP) to process the data from such experiments and identify long-range chromatin interactions with high reproducibility and precision. By application of H3K4me3 PLAC-seq in mouse fetal tissues we identify182,403 long-range chromatin interactions at 10kb resolution. Through integrative analysis with RNA-seq, ATAC-seq and ChIP-seq data, we are able to link the genes with their potential regulatory enhancers.

关键词： Study of enhancer-promoter interactome during mouse embryonic development with H3K4me3 PLAC-seq

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Continuity Equation of Left Invariant Gauge Field in Quantum Genotype Of Deltacron

SSRN

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SSRN 2022年

作者： Hannongbua, Supot Kanjamapornkul, Kabin Rungrotmongkol, Thanyada Department of Chemistry Faculty of Science Chulalongkorn University Bangkok10330 Thailand Biocatalyst and Environmental Biotechnology Research Unit Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok10330 Thailand Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok10330 Thailand

The continuity equation for the invariant gauge field of Deltacron was investigated from the replication and recombination of Delta with Omicron. The new definition of left translation of the invariant gauge field in the quantum genotype of virus and host cell was introduced. The Yang-Mills field in genetic code is an induced curvature in the parallel transport of a unified field between hydrogen bonding between three types of biomolecules. where we defined the invariant gauge field in quantum genotype. This issue allows us to identify the Poincare duality between the nteraction between the behavior field of gene expression in parasitism state and in the curvature of connection in fiber space of Spike protein folding molecule. The Chern Simons current is the anti-self-dual state of mutation between two types of interactive behavior fields in The recombination process of Deltacron was used to plot the Yang-Mills curvature directly from the underlying genetic codes. Further analysis shows that the difference between the tensor correlation with various times of Deltacron the change slope of Omicron BA.1 to the flat curve of the network correlation and closedness centrality of amino acids in receptor binding domain of Spike protein. © 2022, The Authors. All rights reserved.

关键词： Gene expression

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Rejection of aliases for a direct sampling mixer with complex poles

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2016年第1期136卷 60-67页

作者： Imai, Hiroyuki Wada, Kazuyuki Electrical Engineering Program Graduate School of Science and Technology Meiji University 1-1-1 Higashi-Mita Tama-ku Kawasaki214-8571 Japan Department of Electronics and Bioinformatics School of Science and Technology Meiji University 1-1-1 Higashi-Mita Tama-ku Kawasaki214-8571 Japan

This paper proposes the design scheme of direct sampling mixer (DSM) by which unallowable amplitude characteristics due to aliases spreading over relatively broad band is suppressed. It is pointed out that the periodic frequency characteristic of a stage processing decimated signal causes decrease of attenuation in the stopband of the DSM, and an IIR section with a low decimation rate is employed as the first stage. A DSM the amplitude characteristic of which falls into the spectrum mask for IEEE 802.11 is designed as an example. Simulation results show that the designed DSM satisfies specification widely in not only the passband but also the stopband. It is confirmed that gradual decimation approach or use of a non-decimated section for the first stage is effective. © 2016 The Institute of Electrical Engineers of Japan.

关键词： Bandpass filters

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An Exploratory Computational Approach for Elucidating a Nonstructural Protein Interaction in Dengue Virus 2

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Procedia Computer Science 2024年 245卷 470-479页

作者： Alam Ahmad Hidayat Rudi Nirwantono Bens Pardamean Mathematics Department School of Computer Science Bina Nusantara University Indonesia 11480 Biotechnology Department Faculty of Engineering 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

As the dengue infection still impacts hundreds of millions of people globally, unprecedented efforts in dengue drug development have been more progressive in recent decades. Computational methods provide a fast, sustainable, and efficient screening of active compounds and newly created drug molecules, including those specifically targeting nonstructural proteins (NS) of dengue viruses. In this work, protein modeling for the NS proteins of DENV-2/16681 strain was performed using a template-based homology modeling for the NS3 protein and an Artificial Intelligence (AI)-based prediction via AlphaFold for the NS4B protein. Moreover, the protein-protein interaction between the two structures was predicted using the HADDOCK server, which employs information about active and passive residues of the interaction interface to guide the docking process. After the modeling and its respective refinement process, the predicted structures of NS3 and NS4B improved their steric clashing scoring from MolProbity assessment. The refined models were then docked, and the resulting docking pose was analyzed to extract the interacting residues based on the polar contacts within the interface of the two proteins. Our result presents a preliminary study to create a dataset related to in silico molecular interactions of the NS3-NS4B interaction of different DENV types. It is helpful for building a computational pipeline for elucidating protein-ligand problems in dengue drug screenings.

关键词： bioinformatics computational biology dengue protein docking structural biology

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Predicting drug response through tumor deconvolution by cancer cell lines

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Patterns 2024年第4期5卷 100949页

作者： Hsu, Yu-Ching Chiu, Yu-Chiao Lu, Tzu-Pin Hsiao, Tzu-Hung Chen, Yidong Bioinformatics Program Taiwan International Graduate Program National Taiwan University Taipei115 Taiwan Bioinformatics Program Institute of Statistical Science Taiwan International Graduate Program Academia Sinica Taipei115 Taiwan Institute of Health Data Analytics and Statistics Department of Public Health College of Public Health National Taiwan University Taipei100 Taiwan Greehey Children's Cancer Research Institute University of Texas Health San Antonio San AntonioTX78229 United States Department of Medicine School of Medicine University of Pittsburgh PittsburghPA15261 United States UPMC Hillman Cancer Center University of Pittsburgh PittsburghPA15232 United States Department of Medical Research Taichung Veterans General Hospital Taichung40705 Taiwan Department of Population Health Sciences University of Texas Health San Antonio San AntonioTX78229 United States

Large-scale cancer drug sensitivity data have become available for a collection of cancer cell lines, but only limited drug response data from patients are available. Bridging the gap in pharmacogenomics knowledge between in vitro and in vivo datasets remains challenging. In this study, we trained a deep learning model, Scaden-CA, for deconvoluting tumor data into proportions of cancer-type-specific cell lines. Then, we developed a drug response prediction method using the deconvoluted proportions and the drug sensitivity data from cell lines. The Scaden-CA model showed excellent performance in terms of concordance correlation coefficients (>0.9 for model testing) and the correctly deconvoluted rate (>70% across most cancers) for model validation using Cancer Cell Line Encyclopedia (CCLE) bulk RNA data. We applied the model to tumors in The Cancer Genome Atlas (TCGA) dataset and examined associations between predicted cell viability and mutation status or gene expression levels to understand underlying mechanisms of potential value for drug repurposing. © 2024 The Authors

关键词： Tumors

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Single-molecule localization microscopy of expanded mammalian centrioles unravels the ultrastructural constitution of distal appendages

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Biophysical journal 2023年第3S1期122卷 17a页

作者： Chang, Ting-Jui B Hsu, Jimmy C-C Yang, Tony Physics National Taiwan University Taipei City Taiwan Nano Science and Technology Program Taiwan International Graduate Program Academia Sinica Taipei City Taiwan Electrical Engineering National Taiwan University Taipei City Taiwan Electrical Engineering National Taiwan University Taipei City Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei City Taiwan

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Identification of cell cycle-related regulatory motifs using a kernel canonical correlation analysis

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BMC Genomics 2009年第SUPPL. 3期10卷 1-10页

作者： Rhee, Je-Keun Joung, Je-Gun Chang, Jeong-Ho Fei, Zhangjun Zhang, Byoung-Tak Graduate Program in Bioinformatics Seoul National University Seoul 151-744 South Korea Center for Biointelligence Technology (CBIT) Seoul National University Seoul 151-744 South Korea Boyce Thompson Institute for Plant Research Cornell University Ithaca NY 14853 United States Konan Technology Inc Seoul 135-080 South Korea USDA Robert W. Holley Center for Agriculture and Health Ithaca NY 14853 United States School of Computer Science and Engineering Seoul National University Seoul 151-744 South Korea

Background: Gene regulation is a key mechanism in higher eukaryotic cellular processes. One of the major challenges in gene regulation studies is to identify regulators affecting the expression of their target genes in specific biological processes. Despite their importance, regulators involved in diverse biological processes still remain largely unrevealed. In the present study, we propose a kernel-based approach to efficiently identify core regulatory elements involved in specific biological processes using gene expression profiles. Results: We developed a framework that can detect correlations between gene expression profiles and the upstream sequences on the basis of the kernel canonical correlation analysis (kernel CCA). Using a yeast cell cycle dataset, we demonstrated that upstream sequence patterns were closely related to gene expression profiles based on the canonical correlation scores obtained by measuring the correlation between them. Our results showed that the cell cycle-specific regulatory motifs could be found successfully based on the motif weights derived through kernel CCA. Furthermore, we identified co-regulatory motif pairs using the same framework. Conclusion: Given expression profiles, our method was able to identify regulatory motifs involved in specific biological processes. The method could be applied to the elucidation of the unknown regulatory mechanisms associated with complex gene regulatory processes. © 2009 Rhee et al;licensee BioMed Central Ltd.

关键词： Canonical Correlation Analysis Regulatory Motif Upstream Sequence Gene Expression Dataset Yeast Cell Cycle

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Model of metabolism and gene expression predicts proteome allocation in Pseudomonas putida

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NPJ systems biology and applications 2025年第1期11卷 55页

作者： Juan D Tibocha-Bonilla Vishant Gandhi Chloe Lieng Oriane Moyne Rodrigo Santibáñez-Palominos Karsten Zengler Bioinformatics and Systems Biology Graduate Program University of California San Diego 9500 Gilman Drive La Jolla CA 92093-0760 USA. Department of Bioengineering University of California San Diego La Jolla CA 92093-0412 USA. Department of Pediatrics University of California San Diego 9500 Gilman Drive La Jolla CA 92093-0760 USA. Department of Bioengineering University of California San Diego La Jolla CA 92093-0412 USA. kzengler@ucsd.edu. Department of Pediatrics University of California San Diego 9500 Gilman Drive La Jolla CA 92093-0760 USA. kzengler@ucsd.edu. Center for Microbiome Innovation University of California San Diego 9500 Gilman Drive La Jolla CA 92093-0403 USA. kzengler@ucsd.edu. Program in Materials Science and Engineering University of California San Diego 9500 Gilman Drive La Jolla CA 92093-0418 USA. kzengler@ucsd.edu.

The genome-scale model of metabolism and gene expression (ME-model) for Pseudomonas putida KT2440, iPpu1676-ME, provides a comprehensive representation of biosynthetic costs and proteome allocation. Compared to a metabolic-only model, iPpu1676-ME significantly expands on gene expression, macromolecular assembly, and cofactor utilization, enabling accurate growth predictions without additional constraints. Multi-omics analysis using RNA sequencing and ribosomal profiling data revealed translational prioritization in P. putida, with core pathways, such as nicotinamide biosynthesis and queuosine metabolism, exhibiting higher translational efficiency, while secondary pathways displayed lower priority. Notably, the ME-model significantly outperformed the M-model in alignment with multi-omics data, thereby validating its predictive capacity. Thus, iPpu1676-ME offers valuable insights into P. putida's proteome allocation and presents a powerful tool for understanding resource allocation in this industrially relevant microorganism.

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EQTL AND MUTATIONAL ANALYSIS OF NB-LRR MEDIATED IMMUNITY TO POWDERY MILDEW

EQTL AND MUTATIONAL ANALYSIS OF NB-LRR MEDIATED IMMUNITY TO ...

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The 13th International Cereal Rusts and Powdery Mildews Conference(第13届国际禾谷类锈病与白粉病大会)

作者： Roger WISE Priyanka SURANA Greg FUERST Ruo XU Divya MISTRY Julie DICKERSON Dan NETTLETON Department of Statistics Iowa State UniversityAmesIowa 50011 Department of Plant Pathology and Microbiology Center for Plant Responses to Environmental StressesIowa State UniversityAmesIowa 50011 Department of StatisticsIowa State UniversityAmesIowa 50011 Bioinformatics and Computational Biology Graduate ProgramIowa State UniversityAmesIowa 50011 Department of Electrical and Computer Engineering Iowa State UniversityAmesIowa 50011USA Bioinformatics and Computational Biology Graduate ProgramIowa State UniversityAmesIowa 50011 Bioinformatics and Computational Biology Graduate Program Iowa State UniversityAmesIowa 50011 Department of Plant Pathology and MicrobiologyCenter for Plant Responses to Environmental StressesIowa State UniversityAmesIowa 50011 Department of Plant Pathology and Microbiology Center for Plant Responses to Environmental StressesIowa State UniversityAmesIowa 50011 Bioinformatics and Computational Biology Graduate Program Iowa State UniversityAmesIowa 50011 Crop and Insect GeneticsGenomicsand Informatics Research UnitU.S.Department of Agriculture-Agricultural Research ServiceIowa State UniversityAmesIowa 50011 Crop and Insect Genetics Genomicsand Informatics Research UnitU.S.Department of Agriculture-Agricultural Research ServiceIowa State UniversityAmesIowa 50011

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