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467P Examining the experience and preferences of patients with advanced cancer at a Tertiary Care Center

Annals of Oncology 2024年 35卷 S1576-S1576页

作者： C.W. Kwak K. Herring J. Troy S. Wolf C. Malhotra T. Leblanc Duke-NUS Medical School Duke-NUS Graduate Medical School Singapore Duke Cancer Patient Experience Research Program (CPEP) Duke Cancer Institute Durham NC USA Department of Biostatistics & Bioinformatics Duke University School of Medicine Durham NC USA Lien Centre for Palliative Care and Program in Health Services and Systems Research Duke-NUS Graduate Medical School Singapore Duke Cancer Institute Duke University School of Medicine Durham NC USA

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Multimodal learning with graphs

arXiv

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arXiv 2022年

作者： Ektefaie, Yasha Dasoulas, George Noori, Ayush Farhat, Maha Zitnik, Marinka Bioinformatics and Integrative Genomics Program Harvard Medical School BostonMA02115 United States Department of Biomedical Informatics Harvard University BostonMA02115 United States Harvard College CambridgeMA02138 United States Broad Institute of MIT and Harvard CambridgeMA02142 United States Harvard Data Science Initiative CambridgeMA02138 United States Division of Pulmonary and Critical Care Department of Medicine Massachusetts General Hospital BostonMA United States

Artificial intelligence for graphs has achieved remarkable success in modeling complex systems, ranging from dynamic networks in biology to interacting particle systems in physics. However, the increasingly heterogeneous graph datasets call for multimodal methods that can combine different inductive biases—the set of assumptions that algorithms use to make predictions for inputs they have not encountered during training. Learning on multimodal datasets presents fundamental challenges because the inductive biases can vary by data modality and graphs might not be explicitly given in the input. To address these challenges, multimodal graph AI methods combine different modalities while leveraging cross-modal dependencies using graphs. Diverse datasets are combined using graphs and fed into sophisticated multimodal architectures, specified as image-intensive, knowledge-grounded and language-intensive models. Using this categorization, we introduce a blueprint for multimodal graph learning, use it to study existing methods and provide guidelines to design new models. © 2022, CC BY.

关键词： Blueprints

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SpliceAPP: an interactive web server to predict splicing errors arising from human mutations

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BMC genomics 2024年第1期25卷 600页

作者： Ang-Chu Huang Jia-Ying Su Yu-Jen Hung Hung-Lun Chiang Yi-Ting Chen Yen-Tsung Huang Chen-Hsin Albert Yu Hsin-Nan Lin Chien-Ling Lin Institute of Molecular Biology Academia Sinica No. 128 Sec. 2 Academia Road Nangang District Taipei City 115014 Taiwan. Genome and Systems Biology Degree Program Academia Sinica and National Taiwan University Taipei Taiwan. Institute of Statistical Science Academia Sinica Taipei Taiwan. Bioinformatics Program International Graduate Program Academia Sinica Taipei Taiwan. Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan. Institute of Molecular Biology Academia Sinica No. 128 Sec. 2 Academia Road Nangang District Taipei City 115014 Taiwan. arith@gate.sinica.edu.tw. Institute of Molecular Biology Academia Sinica No. 128 Sec. 2 Academia Road Nangang District Taipei City 115014 Taiwan. mbcllin@gate.sinica.edu.tw. Genome and Systems Biology Degree Program Academia Sinica and National Taiwan University Taipei Taiwan. mbcllin@gate.sinica.edu.tw. Bioinformatics Program International Graduate Program Academia Sinica Taipei Taiwan. mbcllin@gate.sinica.edu.tw.

BACKGROUND:Splicing variants are a major class of pathogenic mutations, with their severity equivalent to nonsense mutations. However, redundant and degenerate splicing signals hinder functional assessments of sequence variations within introns, particularly at branch sites. We have established a massively parallel splicing assay to assess the impact on splicing of 11,191 disease-relevant variants. Based on the experimental results, we then applied regression-based methods to identify factors determining splicing decisions and their respective weights. RESULTS:Our statistical modeling is highly sensitive, accurately annotating the splicing defects of near-exon intronic variants, outperforming state-of-the-art predictive tools. We have incorporated the algorithm and branchpoint information into a web-based tool, SpliceAPP, to provide an interactive application. This user-friendly website allows users to upload any genetic variants with genome coordinates (e.g., chr15 74,687,208 A G), and the tool will output predictions for splicing error scores and evaluate the impact on nearby splice sites. Additionally, users can query branch site information within the region of interest. CONCLUSIONS:In summary, SpliceAPP represents a pioneering approach to screening pathogenic intronic variants, contributing to the development of precision medicine. It also facilitates the annotation of splicing motifs. SpliceAPP is freely accessible using the link https://***/SpliceAPP . Source code can be downloaded at https://***/hsinnan75/SpliceAPP .

关键词： Human mutations LASSO regression RNA splicing Splicing variant prediction

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Complete genome sequence of Bacillus halotolerans F29-3, a fengycin-producing strain

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Microbiology Resource Announcements 2024年第4期13卷 e0124623页

作者： Shu, Hung-Yu Chen, Chien-Chi Ku, Hsin-Tzu Wang, Chun-Lin Wu, Keh-Ming Weng, Hui-Ying Liu, Shih-Tung Chen, Chyi-Liang Chiu, Cheng-Hsun Department of Bioscience Technology Chang Jung Christian University Tainan Taiwan Bioresource Collection and Research Center Food Industry Research and Development Institute Hsinchu Taiwan Bioinformatics Department Welgene Biotech Co. Ltd. Taipei Taiwan Biomedical Industry Ph.D. Program National Yang Ming Chiao Tung University Taipei Taiwan Department of Microbiology and Immunology College of Medicine Chang Gung University Taoyuan Taiwan Molecular Infectious Disease Research Center Chang Gung Memorial Hospital Taoyuan Taiwan Division of Pediatric Infectious Diseases Department of Pediatrics Chang Gung Memorial Hospital Taoyuan Taiwan

Bacillus halotolerans F29-3, a Gram-positive bacterium, is recognized for its synthesis of the antifungal substance fengycin. This announcement introduces the complete genome sequence and provides insights into the genetic products related to antibiotic secondary metabolites, including non-ribosomal peptide synthetase (NRPS), polyketide synthase (PKS), and NRPS/PKS combination. © 2024 Shu et al.

关键词： Bacillus halotolerans Bacillus subtilis fengycin non-ribosomal peptide synthetase polyketide synthase

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Accuracy Enhancement of L-Band EPR Tooth Dosimetry by Implementing Multiple Harmonic Detection

SSRN

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SSRN 2024年

作者： Oh, Jeonghun Koo, Chang Uk Park, Jong In Choi, Kwon Lee, Junyoung Hirata, Hiroshi Ye, Sung-Joon Program in Biomedical Radiation Sciences Graduate School of Convergence Science and Technology Seoul National University Seoul08826 Korea Republic of Ionizing Radiation Metrology Group Korea Research Institute of Standards and Science Daejeon34113 Korea Republic of Research Institute for Convergence Science Seoul National University Seoul08826 Korea Republic of Division of Bioengineering and Bioinformatics Faculty of Information Science and Technology Hokkaido University Sapporo060–0814 Japan Department of Applied Bioengineering Graduate School of Convergence Science and Technology Seoul National University Seoul08826 Korea Republic of Advanced Institute of Convergence Technology Seoul National University Suwon16629 Korea Republic of Biomedical Research Institute Seoul National University Hospital Seoul03080 Korea Republic of

In a large-scale radiation accident, the swift classification of individuals exposed to high levels of radiation is crucial to minimize casualties. In-vivo tooth dosimetry using L-band electron paramagnetic resonance (EPR) spectroscopy is a promising method for this purpose, as it can estimate individual exposure levels rapidly and non-invasively. However, the accuracy of dose estimation associated with this technique remains a great challenge. To enhance the accuracy, digital multiple harmonic detection (MHD) was incorporated into a home-built L-band EPR spectrometer. This approach enabled the utilization of information present in higher harmonics of EPR signals, which has been often discarded in traditional EPR spectroscopy. Acquired multiple harmonic spectra were reconstructed to the first derivative spectrum to merge information and mitigate the signal broadening. The modulation amplitude varied in a range of 0.3 to 0.7 mT to maximize the accuracy. In a 0.5-minute measurement at 0.6 mT, the standard error of inverse prediction (SEIP) was reduced to 0.57 Gy from 0.71 Gy. The statistical significancy of the enhancement was validated. In conclusion, the accuracy enhanced by this MHD can contribute to improving the efficiency of triage systems in the aftermath of a mass radiation accident. © 2024, The Authors. All rights reserved.

关键词： Paramagnetic resonance

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Unsupervised News Topic Modelling with Doc2Vec and Spherical Clustering

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Procedia Computer Science 2021年 179卷 40-46页

作者： Arif Budiarto Reza Rahutomo Hendra Novyantara Putra Tjeng Wawan Cenggoro Muhamad Fitra Kacamarga Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Information System Department School of Information System Bina Nusantara University Jakarta Indonesia 11480 Eureka AI Singapore Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

In the digital and Internet era, companies are racing to profile their target users based on their online activities. One of the reliable sources is the news articles they read that can represent their interests. However, extracting latent information from the news articles is not an easy task for a human. In this paper, we introduced a practical model to automatically extract latent information from news articles with pre-determined topics. Our proposed model used unsupervised learning, thus alleviating the need for humans to label news items manually. Doc2vec was used to generate word vectors for each article. Afterward, a spectral clustering algorithm was applied to group the data based on the similarity. A supervised Long Short Term Memory (LSTM) model was built to compare the clustering performance. The best 1, best 3, and best 5 scores were used to evaluate our model. The result showed that our model could not outperformed LSTM model for the best 1 score. However, the best 5 score result indicated that our model was sufficiently robust to cluster the articles based on topic similarity. Additionally, the proposed unsupervised model was implemented in both an on-premise server, and a cloud server. Surprisingly, our proposed method could run faster in the cloud server despite its less number of CPU cores.

关键词： Topic Modelling Latent Information News Article Document Embedding Spherical Clustering

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Transcriptomic analysis of World Trade Center particulate Matter-induced pulmonary inflammation and drug treatments

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Environment International 2023年 177卷 108027页

作者： Chen, Yun-Ti Li, Jinhui Chang, Jen-Ning Luo, Yong-Chun Yu, Wuyue Chen, Lung-Chi Yang, Jinn-Moon Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Chemistry and Chemical Biology Harvard University CambridgeMA02138 United States Department of Urology Stanford University Medical Center StanfordCA94304 United States Department of Environmental Medicine New York University School of Medicine New YorkNY10010 United States Degree Program of Applied Science and Technology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Center for Intelligent Drug Systems and Smart Bio-devices National Yang Ming Chiao Tung University Hsinchu30010 Taiwan

Over 400,000 people are estimated to have been exposed to World Trade Center particulate matter (WTCPM) since the attack on the Twin Towers in Lower Manhattan on September 11, 2001. Epidemiological studies have found that exposure to dust may cause respiratory ailments and cardiovascular diseases. However, limited studies have performed a systematic analysis of transcriptomic data to elucidate the biological responses to WTCPM exposure and the therapeutic options. Here, we developed an in vivo mouse exposure model of WTCPM and administered two drugs (i.e., rosoxacin and dexamethasone) to generate transcriptomic data from lung samples. WTCPM exposure increased the inflammation index, and this index was significantly reduced by both drugs. We analyzed the transcriptomics derived omics data using a hierarchical systems biology model (HiSBiM) with four levels, including system, subsystem, pathway, and gene analyses. Based on the selected differentially expressed genes (DEGs) from each group, WTCPM and the two drugs commonly affected the inflammatory responses, consistent with the inflammation index. Among these DEGs, the expression of 31 genes was affected by WTCPM exposure and consistently reversed by the two drugs, and these genes included Psme2, Cldn18, and Prkcd, which are involved in immune- and endocrine-related subsystems and pathways such as thyroid hormone synthesis, antigen processing and presentation, and leukocyte transendothelial migration. Furthermore, the two drugs reduced the inflammatory effects of WTCPM through distinct pathways, e.g., vascular-associated signaling by rosoxacin, whereas mTOR-dependent inflammatory signaling was found to be regulated by dexamethasone. To the best of our knowledge, this study constitutes the first investigation of transcriptomics data of WTCPM and an exploration of potential therapies. We believe that these findings provide strategies for the development of promising optional interventions and therapies for airborne part

关键词： Dust

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Uncertainty abounds, what now?

引用

Science 2025年第6740期387卷 1261页

作者： Dov Greenbaum Mark Gerstein The reviewer is at the Zvi Meitar Institute for Legal Implications of Emerging Technologies The reviewer is at the Harry Radzyner Law School The reviewer is at the Dina Recanati School of Medicine Reichman University Herzliya Israel The reviewer is at the Department of Biomedical Informatics and Data Science The reviewer is at the Program in Computational Biology and Bioinformatics The reviewer is at the Department of Computer Science Yale University New Haven CT USA.

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SCA-AD, a Novel Method to Uniformly Identify Cells across Single-Cell RNA Sequencing Datasets, Applied to Adult and Pediatric Self-Renewing AML Cells

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Blood 2024年 144卷 2218-2218页

作者： Yoonkyu Lee Wen Wang Timothy K Starr Klara E Noble-Orcutt Chad L Myers Zohar Sachs Masonic Cancer Center University of Minnesota Minneapolis MN Bioinformatics and Computational Biology Program University of Minnesota Minneapolis MN Division of Hematology Oncology and Transplantation Department of Medicine University of Minnesota Minneapolis MN Department of Computer Science and Engineering University of Minnesota Minneapolis MN Department of Obstetrics Gynecology and Women's Health University of Minnesota Minneapolis MN

Introduction Single cell RNA sequencing (scRNAseq) is commonly used to determine cell identity. Several scRNAseq analysis methods are available. Most methods use arbitrary scores to represent the similarity of the query cell to a reference profile and do not assess the statistical significance of those scores. We previously presented a method to identify cells that express a profile of interest (the reference profile) within a scRNAseq dataset. Our method, called Single Cell Correlation Analysis (SCA), identifies cells in a query dataset that are similar to a reference profile. SCA represents a novel advance in the field because it calculates the statistical significance of the correlation coefficient using a permutation-based false discovery rate (FDR) estimation. Here, we advanced SCA to uniformly identify cells across multiple scRNAseq datasets. The new method is called SCA-Across Datasets (SCA-AD). We tested the performance of SCA-AD in validated scRNAseq datasets of normal murine and human bone marrow, benchmarked SCA-AD to other methods, and used SCA-AD to identify and compare self-renewing cells in human AML samples from adult and pediatric patients. Methods and Results SCA-AD uses a universal scoring system to assign cell identity across datasets. Spearman's correlation is used to score the similarity of query cells to a reference profile. SCA-AD integrates a common background dataset into each query dataset which ensures heterogeneity in the data and allows SCA-AD to establish a threshold value for cell identity across the datasets. FDR calculations are used to establish the threshold value. We tested SCA-AD in validated scRNAseq datasets of normal murine and human bone marrow. In both cases, the self-renewing compartment was experimentally-validated using gold-standard, in vivo self-renewal assays. We extracted a reference normal bone marrow self-renewal profile from the in vivo- validated murine scRNAseq dataset (Rodriguez-Fraticelli et al. Nature 2020).

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Emergence of ceftazidime-avibactam resistance through in-vivo bla KPC-2 to bla KPC-33 conversion during treatment of ST11 Klebsiella pneumoniae associated infections

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International Journal of Antimicrobial Agents 2024年第2期64卷

作者： Yi-Chyi Lai Luo-Wei Lin Yu-Lin Lee Department of Internal Medicine Chung Shan Medical University Hospital Taichung Taiwan Department of Microbiology and Immunology School of Medicine Chung Shan Medical University Taichung Taiwan PhD Program in Medical Biotechnology Institute of Genomics and Bioinformatics National Chung-Hsing University Taichung Taiwan School of Medicine Chung Shan Medical University Taichung Taiwan

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