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Genotype–phenotype correlation in IBD: unveiling putative inhibitors for IL10RA and IRF5 variants—an in silico analysis

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Journal of Proteins and Proteomics 2024年第3期15卷 509-522页

作者： Evthonis, Treesa Bency Samykannu, Gopinath Natarajan, Jeyakumar Data Mining and Text Mining Laboratory Department of Bioinformatics Bharathiar University Coimbatore India

Despite recent advances, the majority of the susceptibility genes for inflammatory bowel disease (IBD), a chronic inflammatory disorder that affects the gastrointestinal tract and is commonly diagnosed as Crohn disease and ulcerative colitis, are still unknown. Empirical data indicate that the human interleukin-10 receptor subunit alpha (IL10RA) mutation could impact the regulation of the gut inflammatory response, with interferon regulatory factor 5 (IRF5) being a key player in controlling mucosal inflammation in IBD. Notably, aloin, andrographolide, incensole, and curcumin show up as leading contenders that are significantly contributing to anti-inflammation and a natural adjuvant therapy for IBD. Nevertheless, the exact chemical process by which these substances block IL10RA and IRF5 is still unknown. To close this gap, we carried out molecular interaction and molecular dynamics (MD) simulation experiments to understand how synthetic and herbal drugs interact with IL10RA and IRF5 variants. Molecular docking experiments have demonstrated that aloin (−9.028/−8.12 kcal/mol) has the highest binding affinities when compared to mesalazine (−8.22/−8.615 kcal/mol), a synthetic drug. The study discusses the promising inhibitory effects of aloin and mesalazine on IL10RA and IRF5 variants, supported by structural studies and root mean square fluctuation (RMSF) analysis indicating the stability of aloin. Additionally, drug-likeness was evaluated through Pre-ADMET annotation, while pharmacokinetics analysis facilitated the calculation of binding energies. Integrated molecular docking and simulation studies suggest the potential of aloin with IBD variants. The findings demonstrate that analyzing phenotypic correlations aids in understanding IBD variants. Structural analysis of IL10RA and IRF5 proteins indicates how differences lead to functional abnormalities. This is a step toward personalized IBD treatment by multidimensional screening and herbal therapy.

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Short-Term Effects of Ambient Air Pollutants on Outpatient Visits for Childhood Allergic Diseases in Shanghai,China

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Journal of Shanghai Jiaotong university(Science) 2024年第6期29卷 979-994页

作者： HU Yi GU Jianlei WU Dan WANG Xiaolei LU Hui YU Guangjun Center for Biomedical Informatics Shanghai Children’s HospitalShanghai Jiao Tong University School of MedicineShanghai 200040China SJTU-Yale Joint Center for Biostatistics and Data Science Department of Bioinformatics and BiostatisticsShanghai Jiao Tong UniversityShanghai 200240China Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine Shanghai 200040China

This study aims to investigate the short-term effects of ambient air pollutants on outpatient visits for childhood allergic *** data on ambient air pollutants(NO2,SO2,CO and PM2.5)and outpatient visits for childhood allergic diseases(asthma,atopic dermatitis and allergic rhinitis)were obtained in Shanghai,China from 2013 to *** effects of ambient air pollutants were estimated for total outpatient visits for childhood allergic diseases,gender and age stratification and disease classification by using distributed lag non-linear model(DLNM).We found positive associations between short-term exposure to air pollutants and childhood allergic *** and children aged 7 years old were more likely to be sensitive to ambient air ***2 and SO2 showed stronger effects on asthma and atopic dermatitis,*** study provides evidence that short-term exposure to ambient air pollutants can increase the risk of childhood allergic diseases.

关键词： childhood allergic diseases ambient air pollutant nitrogen dioxide(NO2) sulfur dioxide(SO2) particulate matter<2.5μm in diameter(PM2.5)

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Dynamic k-Anonymity for Electronic Health Records: A Topological Framework 19th

Dynamic k-Anonymity for Electronic Health Records: A Topolo...

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19th International Workshop on data Privacy Management, DPM 2024, 8th International Workshop on Cryptocurrencies and Blockchain Technology, CBT 2024 and 10th Workshop on the Security of Industrial Control Systems and of Cyber-Physical Systems, CyberICPS 2024 which were held in conjunction with the 29th European Symposium on Research in Computer Security, ESORICS 2024

作者： Swaminathan, Arjhun Akgün, Mete Medical Data Privacy and Privacy Preserving Machine Learning Department of Computer Science University of Tübingen Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen Germany

ISBN: (纸本)9783031823480

With the rapid digitization of Electronic Health Records (EHRs), fast and adaptive data anonymization methods have become increasingly important. While tools from topological data analysis (TDA) have been proposed to anonymize static datasets—allowing the creation of multiple generalizations for different anonymization needs from a single computation—the application to dynamic datasets remains unexplored. To address this, our work adapts existing methodologies to the dynamic setting. We develop an improved version of weighted persistence barcodes that track higher-dimensional holes in data, allowing us to edit persistence information on the fly. Additionally, we introduce filtration trimming, a novel technique designed to update persistence data quickly with minimal computing effort when data is added. Our work represents a significant advancement in healthcare data privacy, offering a refined approach to protecting highly sensitive and evolving patient data through dynamic k-anonymity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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Intelligent Electrocardiogram Analysis in Medicine:data,Methods,and Applications

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Chinese Medical Sciences Journal 2023年第1期38卷 38-48页

作者： Yu-Xia Guan Ying An Feng-Yi Guo Wei-Bai Pan Jian-Xin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Big Data Institute Central South UniversityChangsha 410083China

Electrocardiogram(ECG)is a low-cost,simple,fast,and non-invasive *** can reflect the heart’s electrical activity and provide valuable diagnostic clues about the health of the entire ***,ECG has been widely used in various biomedical applications such as arrhythmia detection,disease-specific detection,mortality prediction,and biometric *** recent years,ECG-related studies have been carried out using a variety of publicly available datasets,with many differences in the datasets used,data preprocessing methods,targeted challenges,and modeling and analysis *** we systematically summarize and analyze the ECGbased automatic analysis methods and ***,we first reviewed 22 commonly used ECG public datasets and provided an overview of data preprocessing *** we described some of the most widely used applications of ECG signals and analyzed the advanced methods involved in these ***,we elucidated some of the challenges in ECG analysis and provided suggestions for further research.

关键词： Electrocardiogram database preprocessing machine learning medical big data analysis

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Emotion Detection Mastery: EEG Signal Analysis with Balanced vs. Unbalanced data

Emotion Detection Mastery: EEG Signal Analysis with Balanced...

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2024 International Conference on Brain Computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： Menaka, Jadov Sreelekshmi, G.I. Poulose, Alwin School of Data Science Indian Institute of Science Education and Research Kerala Thiruvananthapuram695551 India University of Kerala Department of Computational Biology and Bioinformatics Kerala Thiruvananthapuram695581 India

ISBN: (纸本)9798331540067

Emotion is crucial in daily life, influencing stress levels, decision-making, relationships, and overall mental health. Recognizing emotions accurately is valuable across sectors like healthcare, education, and beyond. This paper presents a comparative study on emotion recognition, applying various deep learning models with the DEAP dataset, which includes EEG recordings from 32 participants who watched 40 short music video clips. We extracted time-domain, frequency-domain, and wavelet features from the preprocessed EEG data to capture nuanced patterns within the signals. The models assessed in this study include convolutional neural networks (CNN), long short-term memory networks (LSTM), gated recurrent units (GRU), and hybrid architectures, each trained on both balanced and unbalanced datasets. To address class imbalance, data was augmented using the Synthetic Minority Oversampling Technique (SMOTE). Results indicate that the Attention GRU model achieved slightly higher performance by utilizing its attention mechanism to capture extended dependencies within the data. Additionally, model accuracy improved by 5-9% when trained on balanced datasets, underscoring the importance of data balancing in enhancing classification outcomes. © 2024 IEEE.

关键词： Convolutional neural networks

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Systematic Literature Review on Metaheuristic Algorithms in SNP Analysis for Diseases 6

Systematic Literature Review on Metaheuristic Algorithms in ...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Sulistyawan, Jason Sebastian Nugroho, Kuncahyo Setyo Pardamean, Bens Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331539603

Single-nucleotide polymorphism (SNP) analysis has become a pivotal strategy for drug discovery within bioinformatics, especially for incurable diseases like cancer. With the increasing number of researchers starting to embrace metaheuristic methods, a review was done by gathering papers from the Google Scholar database from 2018 to 2023, which resulted in 20 papers after title, abstract, and content filtering. The findings show that the Genetic Algorithm and the Harmony Search Algorithm have become popular approaches in SNP analysis, particularly in studies on breast cancer, age-related macular degeneration, and colorectal cancer. However, the review shows that while researchers have proven most methods effective in finding disease-related SNPs, a more measurable study in SNP analysis is needed, due to the lack of elaboration on measurement metrics in the found studies. © 2024 IEEE.

关键词： metaheuristic algorithms single nucleotide polymorphisms

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Semi-automated meningioma segmentation with bounding boxes 9th

Semi-automated meningioma segmentation with bounding boxes

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9th International Conference on Computer Science and Computational Intelligence, ICCSCI 2024

作者： Heryanto, Nur Adhianti Isnan, Mahmud Henry, Matthew Martianus Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

Segmentation is manually performed by physicians, which takes considerable time and may be subject to observers. Automating this task can increase efficiency and consistency. Existing studies on meningioma segmentation used data from limited study centers, indicating the need for research on multi-center data to assess generalizability. In this work, two semi-automated methods with bounding box priors, LiteMedSAM and BBU-Net, are evaluated on the brain tumor segmentation (BraTS) 2023 meningioma dataset collected from five study-centers. Preprocessing included exclusion of small tumors, z-score normalization, and extraction of slices that contain tumors, generating 25,602 2D axial magnetic resonance imaging (MRI) scans. A fine-tuning strategy is adopted for LiteMedSAM while BBU-Net is trained from scratch. The models are evaluated using a five-fold cross-validation, with data split at the case level. Results show that while U-Net models can achieve performance close to LiteMedSAM, the foundation model has overall better performance, with more than 90% in all evaluation scores. © 2024 The Authors.

关键词： Magnetic resonance imaging

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Exploring EfficientNet Variants for Image Encoding in Auxiliary Signal Guided Knowledge Encoder-Decoder Framework 9th

Exploring EfficientNet Variants for Image Encoding in Auxili...

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9th International Conference on Computer Science and Computational Intelligence, ICCSCI 2024

作者： Henry, Matthew Martianus Heryanto, Nur Adhianti Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

The growing number of medical images has led to radiologist burnout, which seriously impacts the radiologist's performance. To address the previously mentioned issue, an Auxiliary Signal Guided Knowledge (ASGK) multimodal encoder-decoder framework was designed to automatically generate the medical report based on the proposed medical graph and natural language decoder. It utilizes DenseNet-121 as the image encoder. With DenseNet-121 lack of computational and memory efficiency, this study aims to explore the potential of EfficientNetB0 to EfficientNetB4 as an ASGK image encoder substitute. The framework is trained with IU X-Ray dataset for 30 epochs, with Adam optimizer, a learning rate of 0.01 with 0.8 decay rate, binary cross entropy loss for the medical tags, and cross-entropy loss for the generated medical captions. During the framework training process with each image encoder, the parameter that achieves the highest CIDEr score on the validation set is considered the best image encoder parameter and will be used on the test set. On the test set, EfficientNetB3 as an ASGK image encoder has been shown to increase the CIDEr score to 0.35, a significant increase from the 0.28 CIDEr score obtained by the ASGK using DenseNet-121. This score is only a 1% decrease from the best validation score. It suggests that not only EfficientNetB3 increases the framework's performance, it is also less prone to overfitting. This study has demonstrated that EfficientNetB3 is a potential image encoder substitute for DenseNet-121 in the ASGK framework. © 2024 The Authors.

关键词： Medical imaging

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Leveraging user-item interactions and hotel characteristics for hotel recommendations in Indonesia with graph neural networks 9th

Leveraging user-item interactions and hotel characteristics ...

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9th International Conference on Computer Science and Computational Intelligence, ICCSCI 2024

作者： Panarto, Francis Alexander Phanghegar, Tyrone Matthew Gunardi, Vincent Cenggoro, Tjeng Wawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Travelers usually check information about the destination before they decide to go. However, sometimes the information is too much to handle and causes confusion. A filtering mechanism is needed to help them make their choices. The solution is to implement a recommendation system;some common techniques that are used for this are Collaborative Filtering and content-based filtering, but both techniques have their flaws. Therefore, we propose a solution to combine those two methods through Graph Neural Network architectures to help fix their flaws. The proposed method will be used for recommending hotels in Indonesia using the HotelRec dataset as the base dataset and hotel features acquired from the TripAdvisor API. We use several GNN architectures, such as GCN, LightGCN, GraphSAGE, and GAT, to implement our approach. Then, we compare it to several baseline models, such as SVD and NeuMF, which are common baselines in recommendation systems. The performance of these models is calculated based on three metrics: HitRate@K, NDCG@K, and Recall@K. Based on the result of this experiment, we found that incorporating hotel features into the learning process of the model, accompanied by user-item interactions, boosted the model's performance by at least 20% in all metrics, especially in datasets with a smaller number of interactions. © 2024 The Authors.

关键词： Collaborative filtering

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Recurrent evolution of seaweed body plan complexity among photosynthetic eukaryotes

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Molecular Plant 2024年第7期17卷 999-1001页

作者： Elisa S.Goldbecker Iker Irisarri Jan de Vries Department of Applied Bioinformatics Institute for Microbiology and GeneticsUniversity of GottingenGoldschmidtstr.137077 GottingenGermany Section Phylogenomics Center for Molecular Biodiversity ResearchLeibniz Institute for the Analysis of Biodiversity Change(LIB)Museum of Nature HamburgMartin-Luther-King-Platz 320146 HamburgGermany Department of Applied Bioinformatics Campus Institute Data Science(CIDAS)University of GottingenGoldschmidtstr.137077 GottingenGermany Department of Applied Bioinformatics Gottingen Center for Molecular Biosciences(GZMB)University of GottingenGoldschmidtstr.137077 GottingenGermany

The evolutionary origin of multicellularity is an age-old question in biological *** studies have delved into the evolutionary processes that led to the evolution of multicellular life forms from unicellular *** beauty and the challenge about this topic are that evolution has brought forth various forms of multicellular life,multiple times independently,scattered across the evolutionary tree of *** to the challenge,multicellular organisms exist in a diversity of forms and complexities,and the mechanisms by which multicellularity emerged are also likely different between animals,algae,or fungi(Niklas and Newman,2020).Recently,Nelson et al.(2024)presented a large-scale comparative genomics study focused on particularly intricate multicellular organisms:macroalgae(seaweeds),which emerged independently in three distinct eukaryotic lineages-green algae(Chlorophyta),red algae(Rhodophyta),and brown algae(Phaeophyceae).

关键词： algae seaweed evolution

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