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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Trinugroho, Joko Pebrianto Hidayat, Alam Ahmad Nirwantono, Rudi Asadi, Faisal Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Bacteria are microscopic organisms that can be found in many environments. They are abundant and have many roles in our life. Studying bacteria is essential so that we can identify the bacteria that are needed for many industrial applications. However, the main problem is that majority of the bacteria are unculturable, hampering the exploration of bacteria from different environments. Metagenomics approach which employs Next Generation Sequencing technology could help study bacteria by utilizing 16S rRNA marker gene. This study aims to demonstrate data mining and bioinformatics approaches to analyze 16S rRNA sequencing data. The raw sequencing data of 16S rRNA was collected from biological database. Then, the data were trimmed, denoised, and clustered to generate Amplicon Sequence Variants (ASVs). Diversity (alpha and beta) and taxonomic analyses were then conducted to elucidate the bacterial diversity and taxonomic profile of ASVs. The results of this work showed that Shannon and PD indices in China's hot spring were higher than Singapore and the USA. Furthermore, a significant difference was observed in the PD index. The unweighted UniFrac distance also showed there was a significant difference of the bacterial communities between the three locations. In addition, the taxonomic investigation unveiled prevalent bacterial groups in the ecosystem, namely Proteobacteria, Chloroflexi, Cyanobacteria, and Crenarchaeota. The research outcomes have the potential to serve as a foundational resource for subsequent bacterial metagenomic research, particularly the hot spring environment. © 2023 IEEE.

关键词： Bacteria

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data Mining and bioinformatics Approaches to Elucidate Bacterial Communities in the Extreme Environment

Data Mining and Bioinformatics Approaches to Elucidate Bacte...

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

作者： Joko Pebrianto Trinugroho Alam Ahmad Hidayat Rudi Nirwantono Faisal Asadi Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Mathematics Department School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science 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

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OncoPrint++: An Enhanced OncoPrint Beyond data Visualization for Clinical bioinformatics and Genomics

OncoPrint++: An Enhanced OncoPrint Beyond Data Visualization...

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IEEE International Conference on Big data

作者： Shiquan Wu Kwangbom Choi Paul Jung Jifeng Qian Paul Hiser Melih Acar Edith Szafer Glusman Leo Wang-Kit Cheung Bioinformatics & Computational Oncology Genomics Research Center AbbVie Company South San Franciso CA USA Bioinformatics Data Engineering Genomics Research Center AbbVie Company South San Franciso CA USA Information Research Genomics Research Center AbbVie Company South San Franciso CA USA Precision Medicine Oncology AbbVie Company South San Franciso CA USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

It is presented in this article a web-based tool named OncoPrint++. OncoPrint++ is a tool integrated with traditional OncoPrint/heatmap and enhanced functions and features for processing, visualizing and analyzing clinicogenomics data in oncology. OncoPrint++ functions as an all-in-one tool primarily designed for oncology studies, but it could be extended for general clinical bioinformatics and genomics. It will be available to use for free at GitHub/AWS.

关键词： data visualization Genomics Big data Oncology bioinformatics

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Gut metagenomic analysis of gastric cancer patients reveals Akkermansia, Gammaproteobacteria, and Veillonella microbiota as potential non-invasive biomarkers

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Genomics and Informatics 2024年第1期22卷 394-424页

作者： Nath, Anju R. Natarajan, Jeyakumar Data Mining and Text Mining Laboratory Department of Bioinformatics Bharathiar University Coimbatore 641 046 India

The goal of the study was to investigate the changes in the gut microbiota during the advancement of gastric cancer (GC) and identify pertinent taxa associated with the disease. We used a public fecal amplicon gastric cancer dataset from the Sequence Retrieval Archive (SRA), of patients with GC, gastritis, and healthy individuals. We did sequence pre-processing, including quality filtering of the sequences. Then, we performed a diversity analysis, evaluating α- and β-diversity. Next, taxonomic composition analysis was performed and the relative abundances of different taxa at the phylum and genus levels were compared between GC, gastritis, and healthy controls. The obtained results were subsequently subjected to statistical validation. To conclude, metagenomic function prediction was carried out, followed by correlation analysis between the microbiota and KEGG pathways. α analysis revealed a significant difference between male and female categories, while β analysis demonstrated significant distinctions between GC, gastritis, and healthy controls, as well as between sexes within the GC and gastritis groups. The statistically confirmed taxonomic composition analysis highlighted the presence of the microbes Bacteroides and Veillonella. Furthermore, through metagenomic prediction analysis and correlation analysis with pathways, three taxa, namely Akkermansia, Gammaproteobacteria, and Veillonella, were identified as potential biomarkers for GC. Additionally, this study reports, for the first time, the presence of two bacteria, Desulfobacteriota and Synergistota, in GC, necessitating further investigation. Overall, this research sheds light on the potential involvement of gut microbiota in GC pathophysiology;however, additional studies are warranted to explore its functional significance. © The Author(s) 2024.

关键词： Diversity analysis Gastric cancer Gut microbiota Metagenomic analysis QIIME2

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KeyEE:Enhancing Low-Resource Generative Event Extraction with Auxiliary Keyword Sub-Prompt

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Big data Mining and Analytics 2024年第2期7卷 547-560页

作者： Junwen Duan Xincheng Liao Ying An Jianxin Wang Hunan Key Laboratory of Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Big Data Institute Central South UniversityChangsha 410083China

Event Extraction(EE)is a key task in information extraction,which requires high-quality annotated data that are often costly to *** classification-based methods suffer from low-resource scenarios due to the lack of label semantics and fine-grained *** recent approaches have endeavored to address EE through a more data-efficient generative process,they often overlook event keywords,which are vital for *** tackle these challenges,we introduce KeyEE,a multi-prompt learning strategy that improves low-resource event extraction by Event Keywords Extraction(EKE).We suggest employing an auxiliary EKE sub-prompt and concurrently training both EE and EKE with a shared pre-trained language *** the auxiliary sub-prompt,KeyEE learns event keywords knowledge implicitly,thereby reducing the dependence on annotated ***,we investigate and analyze various EKE sub-prompt strategies to encourage further research in this *** experiments on benchmark datasets ACE2005 and ERE show that KeyEE achieves significant improvement in low-resource settings and sets new state-of-the-art results.

关键词： natural language processing Event Extraction(EE) Multi-Prompt Learning(MPL) low-resource

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Aggregating Structural Features for Computational Analysis of Mutations in BRCT Domains of BRCA1 Protein

Aggregating Structural Features for Computational Analysis o...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Hidayat, Alam Ahmad Nirwantono, Rudi Isnan, Mahmud Trinugroho, Joko Pebrianto Pardamean, Bens Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Binus Graduate Program Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features. © 2023 IEEE.

关键词： Proteins

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Toward a unified information framework for cell atlas assembly

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National Science Review 2022年第3期9卷 7-10页

作者： Sijie Chen Yanting Luo Haoxiang Gao Fanhong Li Jiaqi Li Yixin Chen Renke You Hairong Lv Kui Hua Rui Jiang Xuegong Zhang Bioinformatics Division of BNRIST and Department of Automation MOE Key Lab of BioinformaticsTsinghua University Fuzhou Institute of Data Technology School of Medicine Tsinghua University School of Life Sciences Center for Synthetic and Systems Biology Tsinghua University

Cells are the basic structural and functional unit of the human body. Virtually all cells of a human body possess the same genome, but they exhibit colorful heterogeneities in phenotypes and *** heterogeneities includ...

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A Machine Learning Approach for Outcome Prediction in Postanoxic Coma Patients Using Frequency Domain Features 50

A Machine Learning Approach for Outcome Prediction in Postan...

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50th Computing in Cardiology, CinC 2023

作者： Venkataramani, Vijay Vignesh Garg, Akshit Maity, Maitreya Priyakumar, U Deva Iiit Center for Computational Natural Sciences and Bioinformatics Hyderabad India Iiit IHub Data Hyderabad India

ISBN: (纸本)9798350382525

In this work, we describe the creation of our machine-learning-based solution for coma prognosis after cardiac arrest using longitudinal EEG and ECG recordings for the 'Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023'. Our team, 'ComaToast', had its best submission ranked 28 out of 36 teams selected worldwide, with a challenge score of 0.381 on the official leaderboard for the hidden test set. We use a combination of age and signal features from EEG and ECG recordings. Frequency domain features, specifically mean power spectral density from 4 different bands of frequencies (Delta, Theta, Alpha and Beta) and mean Burst Suppression Ratio, were extracted from pre-processed EEG recordings from the first and last available recording for a given patient. Features like mean and standard deviations were extracted along channels for ECG recordings. After imputing missing values, these features are fed to an XGBoost classifier for the final binary classification of the outcome prediction task. The features are fed to a random forest regressor to predict the CPC outcome for every patient. A solution like ours, which uses a simple model and training technique, may be more viable than deep-learning solutions in general use cases. In our final model, our approach achieved a 5-fold cross-validation score of 0.34 on the public train set. © 2023 CinC.

关键词： Forecasting

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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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Chronic disease patients have fewer social contacts:A pilot survey with implications for transmission dynamics

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Infectious Disease Modelling 2024年第3期9卷 926-930页

作者： J.Vanderlocht S.Møgelmose K.Van Kerckhove P.Beutels N.Hens Interuniversity Institute of Biostatistics and Statistical Bioinformatics Data Science InstituteHasselt UniversityHasseltBelgium Centre for Health Economic Research and Modelling Infectious Diseases University of AntwerpAntwerpBelgium

Non-communicable diseases(NCD)are the most important cause of death in the *** socio-economic costs associated with NCDs makes it imperative to prevent and control them in the 21st *** severe toll that the COVID-19 pandemic has taken worldwide is an unfortunate illustration of our limited insight into the infectious risk for the global ***-incidence between NCD and infection offers an underexplored opportunity to design preventive *** a pilot survey,we observed that the NCD population displays a substantial reduction in their social contacting behavior as compared to the general *** indicates that existing mathematical models based on contact surveys in the general population are not applicable to the NCD population and that the risk of acquiring an infection following a contact is probably *** demonstration of reduced social mixing in several chronic conditions,raises the question to what extent the social mixing is influenced by the burden of *** advocate the design of disease-specific contact surveys to address how the burden of disease associates with social contact behavior and the risk of *** SARS-CoV-2 pandemic offers an unprecedented opportunity to gain insight into the importance of infection in the NCD population and to find ways to improve healthcare procedures.

关键词： survey mixing insight

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