检索结果-内蒙古大学图书馆

Identify Non-mutational p53 Functional Deficiency in Human Cancers

Genomics, Proteomics & bioinformatics 2024年第5期22卷 85-98页

作者： Qianpeng Li Yang Zhang Sicheng Luo Zhang Zhang Ann L.Oberg David E.Kozono Hua Lu Jann N.Sarkaria Lina Ma Liguo Wang National Genomics Data Center China National Center for BioinformationBeijing 100101China Beijing Institute of Genomics Chinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China Division of Computational Biology Mayo Clinic College of Medicine and ScienceRochesterMN 55905USA Department of Radiation Oncology Dana-Farber Cancer Institute and Brigham and Women's HospitalBostonMA 02215USA Department of Biochemistry&Molecular Biology and Tulane Cancer Center Tulane University School of MedicineNew OrleansLA 70112USA Department of Radiation Oncology Mayo Clinic College of Medicine and ScienceRochesterMN 55905USA Department of Biochemistry and Molecular Biology Mayo Clinic College of Medicine and ScienceRochesterMN 55905USA Bioinformatics and Computational Biology Graduate Program University of Minnesota RochesterRochesterMN 55904USA

An accurate assessment of p53's functional statuses is critical for cancer genomic ***,there is a significant challenge in identifying tumors with non-mutational p53 inactivation which is not detectable through DNA *** undetected cases are often misclassified as p53-normal,leading to inaccurate prognosis and downstream association *** address this issue,we built the support vector machine(SVM)models to systematically reassess p53's functional statuses in TP53 wild-type(TP53^(WT))tumors from multiple The Cancer Genome Atlas(TCGA)***-validation demonstrated the good performance of the SVM models with a mean area under the receiver operating characteristic curve(AUROC)of 0.9822,precision of 0.9747,and recall of *** study revealed that a significant proportion(87%-99%)of TP53^(WT) tumors actually had compromised p53 *** analyses uncovered that these genetically intact but functionally impaired(termed as predictively reduced function of p53 or TP53^(WT)-pRF)tumors exhibited genomic and pathophysiologic features akin to TP53-mutant tumors:heightened genomic instability and elevated levels of ***,patients with TP53^(WT)-pRF tumors experienced significantly shortened overall survival or progression-free survival compared to those with predictively normal function of p53(TP53^(WT)-pN)tumors,and these patients also displayed increased sensitivity to platinum-based chemotherapy and radiation therapy.

关键词： Cancer Composite expression DNA mutation Machine learning p53 deficiency

来源：评论

学校读者我要写书评

暂无评论

Unveiling the effects of micro and nano plastics in embryonic development

引用

Toxicology Reports 2025年 14卷 101954页

作者： Nair, Sanjay R. Nihad, Muhammad Shenoy P, Sudheer Gupta, Sebanti Bose, Bipasha Stem Cells and Regenerative Medicine Centre Yenepoya Research Centre Yenepoya (Deemed to be University) University Road Deralakatte Karnataka Mangalore 575018 India Division of Data Analytics Bioinformatics and Structural Biology Yenepoya Research Centre Yenepoya (Deemed to be University) University Road Deralakatte Karnataka Mangalore 575018 India

The improper disposal and degradation of plastics causes the formation and spread of micro and nano-sized plastic particles in the ecosystem. The widespread presence of these micro and nanoplastics leads to their accumulation in the biotic and abiotic components of the environment, thereby affecting the cellular and metabolic functions of organisms. Despite being classified as xenobiotic agents, information about their sources and exposure related to reproductive health is limited. Micro and nano plastic exposure during early developmental stages can cause abnormal embryonic development. It can trigger neurotoxicity and inflammatory responses as well in the developing embryo. In embryonic development, a comprehensive study of their role in pluripotency, gastrulation, and multi-differentiation potential is scarce. Due to ethical concerns associated with the direct use of human embryos, pluripotent cells and its 3D in vitro models (with cell lines) are an alternative source for effective research. Thus, the 3D Embryoid body (EB) model provides a platform for conducting embryotoxicity and multi-differentiation potential research. Pluripotent stem cells such as embryonic and induced pluripotent stem cells derived embryoid bodies (EBs) serve as a robust 3D in vitro model that mimics characteristics similar to that of human embryos. Thus, the 3D EB model provides a platform for conducting embryotoxicity and multi-differentiation potential research. Accordingly, this review discusses the significance of 3D in vitro models in conducting effective embryotoxicity research. Further, we also evaluated the possible sources/routes of microplastic generation and analyzed their surface chemistry and cytotoxic effects reported till date. © 2025 The Authors

关键词： 3D in vitro models Cell-lineage specification Embryoid bodies Embryotoxicity Micro and nano plastics PLuripotent stem cells

来源：评论

学校读者我要写书评

暂无评论

Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival data

arXiv

引用

arXiv 2024年

作者： Li, Siqi Shang, Yuqing Wang, Ziwen Wu, Qiming Hong, Chuan Ning, Yilin Miao, Di Ong, Marcus Eng Hock Chakraborty, Bibhas Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Health Services Research Centre Singapore Health Services Singapore Singapore Department of Emergency Medicine Singapore General Hospital Singapore Singapore Department of Statistics and Data Science National University of Singapore Singapore Singapore Institute of Data Science National University of Singapore Singapore Singapore

Objective Survival analysis serves as a fundamental component in numerous healthcare applications, where the determination of the time to specific events (such as the onset of a certain disease or death) for patients is crucial for clinical decision-making. Scoring systems are widely used for swift and efficient risk prediction. However, existing methods for constructing survival scores presume that data originates from a single source, posing privacy challenges in collaborations with multiple data owners. Materials and Methods We propose a novel framework for building federated scoring systems for multi-site survival outcomes, ensuring both privacy and communication efficiency. We applied our approach to sites with heterogeneous survival data originating from emergency departments in Singapore and the United States. Additionally, we independently developed local scores at each site. Results In testing datasets from each participant site, our proposed federated scoring system consistently outperformed all local models, evidenced by higher integrated area under the receiver operating characteristic curve (iAUC) values, with a maximum improvement of 11.6%. Additionally, the federated score’s time-dependent AUC(t) values showed advantages over local scores, exhibiting narrower confidence intervals (CIs) across most time points. Discussion The model developed through our proposed method exhibits effective performance on each local site, signifying noteworthy implications for healthcare research. Sites participating in our proposed federated scoring model training gained benefits by acquiring survival models with enhanced prediction accuracy and efficiency. Conclusion This study demonstrates the effectiveness of our privacy-preserving federated survival score generation framework and its applicability to real-world heterogeneous survival data. © 2024, CC BY.

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

A Short Note on Relevant Cuts

arXiv

引用

arXiv 2024年

作者： Domschke, Nico Gatter, Thomas Golnik, Richard Stadler, Peter F. Bioinformatics Group Department of Computer Science Leipzig University Härtelstraße 16–18 LeipzigD-04107 Germany Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig School of Embedded Composite Artificial Intelligence Leipzig University Härtelstraße 16–18 LeipzigD-04107 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Department of Theoretical Chemistry University of Vienna Währingerstraße 17 WienA-1090 Austria Facultad de Ciencias Universidad National de Colombia Bogotá Colombia Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States

The set of relevant cuts in a graph is the union of all minimum weight bases of the cut space. We show that a cut is relevant if and only if it is the a minimum weight cut between two distinct vertices. Moreover, we give a characterization in terms of Picard-Queyranne DAGs that can be used to accelerate the enumeration of the relevant cuts. © 2024, CC BY-NC-ND.

关键词： Trees (mathematics)

来源：评论

学校读者我要写书评

暂无评论

ConvNeXt Model for Breast Cancer Image Classification

ConvNeXt Model for Breast Cancer Image Classification

引用

International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Devin Setiawan Andrea Stevens Karnyoto Indo Intan Bens Pardamean Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83.

关键词： Support vector machines Accuracy Head Computational modeling Transfer learning Breast cancer Magnetic heads data models Reliability Image classification

来源：评论

学校读者我要写书评

暂无评论

Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare

arXiv

引用

arXiv 2024年

作者： Liu, Mingxuan Ning, Yilin Ke, Yuhe Shang, Yuqing Chakraborty, Bibhas Ong, Marcus Eng Hock Vaughan, Roger Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Department of Anaesthesiology and Perioperative Medicine Singapore General Hospital Singapore Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Department of Statistics and Data Science National University of Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Department of Emergency Medicine Singapore General Hospital Singapore Singapore Institute of Data Science National University of Singapore Singapore Singapore

Background The escalating integration of machine learning in high-stakes fields such as healthcare raises substantial concerns about model fairness and interpretability. Conventional bias-mitigation techniques often sacrifice model performance (e.g., accuracy), limiting their impact on aiding decision-making. This study introduces an interpretable bias mitigation framework, Fairness-Aware Interpretable Modeling (FAIM), aiming to balance model performance and fairness. Methods We propose the FAIM framework, which prioritizes fairness in predictive models that are nearly optimal in performance. Initiating with a performance-optimized model yet without explicit fairness considerations (i.e., "fairness-unaware"), we construct a set of nearly-optimal models (i.e., Integral Rashomon Set) to select a fairer alternative (i.e., "fairness-aware"). To identify this fairness-aware model, we introduce a fairness ranking index (FRI) to comprehensively rank model fairness, which encompasses multiple fairness metrics including equalized odds, equal opportunity, and equality of balanced error rate. This index is complemented by an interactive graphical interface that facilitates the incorporation of clinical expertise to guide model selection with contextualized fairness. Moreover, we integrate SHapley Additive exPlanations (SHAP) within FAIM to compare variable contributions between the fairness-unaware and fairness-aware models, thereby uncovering model modifications made to address fairness. We demonstrated the value of FAIM by predicting hospital admission using two real-world databases, MIMIC-IV-ED and SGH-ED, to test its performance in bias mitigation, particularly for sex and race. We gauged model performance via AUC, sensitivity and specificity, and evaluated model fairness via the aforementioned fairness metrics. Results For both datasets, the fairness-aware models yielded by FAIM automatically excluded race and reduced the contribution of sex and other bias-related variabl

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

LLM2Vec Sentence Embeddings Analysis in Sentiment Classification

LLM2Vec Sentence Embeddings Analysis in Sentiment Classifica...

引用

International Conference on data and Software Engineering (ICODSE)

作者： Matthew Martianus Henry Nur Adhianti Hervanto Mahmud Isnan Dian Kurnianingrum Chyntia Ika Ratnapuri Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Entrepreneurship Department Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506407

ISBN: (纸本)9798331506414

Sentiment analysis is crucial method in business intelligence to extract insights, which typically begin with sentiment classification. One of the latest frameworks for generating sentence embeddings for sentiment classification is LLM2Vec, which allows Transformer decoder-based models to generate sentence embeddings for text representation. Its capability is deemed language-agnostic, which, in this study, the framework is leveraged for Tokopedia tweet sentiment analysis to prove the claim. The base decoder models used in the LLM2Vec framework were Llama 3 8B, Llama 2 7B, Sheared Llama 1.3B, and Mistral 7B. Two BERT-based models, which are the Indonesian SBERT model and IndoBERT trained with the SimCSE approach, were employed as a comparison. The generated embeddings were classified using logistic regression, SVM, and MLP Classifier. Classifiers using embedding generated by LLM2Vec with Llama 3 8B and Mistral 7B achieves on-par performance with classifiers that utilize IndoBERT SimCSE embeddings, while classifiers using embeddings generated by LLM2Vec with Llama 2 7B and Sheared Llama 1.3B achieves much lower performance. Classifiers with Indonesian SBERT embeddings achieve the highest F1 score performance. Despite slightly lower performance, this study has proven the language-agnostic capability of LLM2Vec, especially with Llama 3 8B and Mistral 7B in colloquial Bahasa Indonesia sentiment analysis, since none of the base decoders were ever trained using the Bahasa Indonesia corpus.

关键词： Support vector machines Sentiment analysis Logistic regression Social networking (online) Blogs Transformers Encoding Decoding Business intelligence Software engineering

来源：评论

学校读者我要写书评

暂无评论

APIR:Aggregating Universal Proteomics database Search Algorithms for Peptide Identification with FDR Control

引用

Genomics, Proteomics & bioinformatics 2024年第2期22卷 171-187页

作者： Yiling Elaine Chen Xinzhou Ge Kyla Woyshner MeiLu McDermott Antigoni Manousopoulou Scott B.Ficarro Jarrod A.Marto Kexin Li Leo David Wang Jingyi Jessica Li Department of Statistics and Data Science University of CaliforniaLos AngelesCA 90095USA Department of Immuno-Oncology Beckman Research InstituteCity of Hope National Medical CenterDuarteCA 91010USA Department of Quantitative and Computational Biology University of Southern CaliforniaLos AngelesCA 90089USA Department of Cancer Biology and Blais Proteomics Center Dana-Farber Cancer InstituteDepartment of PathologyBrigham and Women’s Hospital and Harvard Medical SchoolBostonMA 02215USA Department of Pediatrics City of Hope National Medical CenterDuarteCA 91010USA Bioinformatics Interdepartmental Program University of CaliforniaLos AngelesCA 90095USA Department of Human Genetics University of CaliforniaLos AngelesCA 90095USA Department of Computational Medicine University of CaliforniaLos AngelesCA 90095USA Department of Biostatistics University of CaliforniaLos AngelesCA 90095USA

Advances in mass spectrometry(MS)have enabled high-throughput analysis of proteomes in biological *** state-of-the-art MS data analysis relies on database search algorithms to quantify proteins by identifying peptide–spectrum matches(PSMs),which convert mass spectra to peptide *** database search algorithms use distinct search strategies and thus may identify unique ***,no existing approaches can aggregate all user-specified database search algorithms with a guaranteed increase in the number of identified peptides and a control on the false discovery rate(FDR).To fill in this gap,we proposed a statistical framework,Aggregation of Peptide Identification Results(APIR),that is universally compatible with all database search ***,under an FDR threshold,APIR is guaranteed to identify at least as many,if not more,peptides as individual database search algorithms *** of APIR on a complex proteomics standard dataset showed that APIR outpowers individual database search algorithms and empirically controls the *** data studies showed that APIR can identify disease-related proteins and post-translational modifications missed by some individual database search *** APIR framework is easily extendable to aggregating discoveries made by multiple algorithms in other high-throughput biomedical data analysis,e.g.,differential gene expression analysis on RNA sequencing *** APIR R package is available at https://***/yiling0210/APIR.

关键词： Shotgun proteomics Peptide–spectrum match Peptide identification Aggregation of lists FDR control

来源：评论

学校读者我要写书评

暂无评论

'EMDB-the Electron Microscopy data Bank' (vol 1, gkad1019, 2023)

引用

NUCLEIC ACIDS RESEARCH 2023年第D1期52卷 D1701-D1701页

作者： Turner, Jack Abbott, Sanja Fonseca, Neli Pye, Ryan Carrijo, Lucas Duraisamy, Amudha Kumari Salih, Osman Wang, Zhe Kleywegt, Gerard J. Morris, Kyle L. Patwardhan, Ardan Cellular Structure and 3D Bioimaging European Molecular Biology Laboratory (EMBL) European Bioinformatics Institute (EBI) Wellcome Genome Campus Hinxton Cambridgeshire CB10 1SD United Kingdom Research Collaboratory for Structural Bioinformatics Protein Data Bank Institute for Quantitative Biomedicine Rutgers The State University of New Jersey Piscataway 08854 NJ United States Department of Chemistry and Chemical Biology Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Cancer Institute of New Jersey Rutgers The State University of New Jersey New Brunswick 08901 NJ United States Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California San Diego La Jolla 92093 CA United States Protein Data Bank in Europe European Molecular Biology Laboratory (EMBL) European Bioinformatics Institute (EBI) Wellcome Genome Campus Hinxton Cambridgeshire CB10 1SD United Kingdom CEITEC Central European Institute of Technology Masaryk University Kamenice 5 Brno 62500 Czech Republic Molecular Biophysics Unit Indian Institute of Science Bangalore India Protein Data Bank Japan Protein Research Foundation Minoh Osaka 562-8686 Japan Protein Data Bank Japan Institute for Protein Research Osaka University Suita Osaka 565-0871 Japan Biological Magnetic Resonance Data Bank Department of Molecular Biology and Biophysics UConn Health 263 Farmington Ave. Farmington 06030-3305 CT United States National Facility for Protein Science Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai 201210 China School of Life Science and Technology Shanghai Tech University Shanghai 201210 China

The Electron Microscopy data Bank (EMDB) is the global public archive of three-dimensional electron microscopy (3DEM) maps of biological specimens derived from transmission electron microscopy experiments. As of 2021, EMDB is managed by the Worldwide Protein data Bank consortium (wwPDB; ***) as a wwPDB Core Archive, and the EMDB team is a core member of the consortium. Today, EMDB houses over 30 000 entries with maps containing macromolecules, complexes, viruses, organelles and cells. Herein, we provide an overview of the rapidly growing EMDB archive, including its current holdings, recent updates, and future plans.

关键词：

来源：评论

学校读者我要写书评

暂无评论

uniLIVER: a human liver cell atlas for data-driven cellular state mapping

引用

Journal of Genetics and Genomics 2025年

作者： Wu, Yanhong Fan, Yuhan Miao, Yuxin Li, Yuman Du, Guifang Chen, Zeyu Diao, Jinmei Chen, Yu-Ann Ye, Mingli You, Renke Chen, Amin Chen, Yixin Li, Wenrui Guo, Wenbo Dong, Jiahong Zhang, Xuegong Wang, Yunfang Gu, Jin MOE Key Lab of Bioinformatics BNRIST Bioinformatics Division Department of Automation Tsinghua University Beijing 100084 China Hepato-Pancreato-Biliary Center Beijing Tsinghua Changgung Hospital Tsinghua University Beijing 102218 China Clinical Translational Science Center Beijing Tsinghua Changgung Hospital Tsinghua University Beijing 102218 China Fuzhou Institute of Data Technology Fujian Fuzhou 350207 China Center for Synthetic and Systems Biology School of Life Sciences and School of Medicine Tsinghua University Beijing 100084 China

The liver performs several vital functions such as metabolism, toxin removal, and glucose storage through the coordination of various cell types. With the recent breakthrough of the single-cell/single-nucleus RNA-seq (sc/snRNA-seq) techniques, there is a great opportunity to establish a reference cell map of the liver at single-cell resolution with transcriptome-wise features. In this study, we build a unified liver cell atlas uniLIVER (http://***/database/uniliver) by integrative analysis of a large-scale sc/snRNA-seq data collection of normal human liver with 331,125 cells and 79 samples from 6 datasets. Moreover, we introduce LiverCT, a novel machine learning based method for mapping any query dataset to the liver reference map by introducing the definition of “variant” cellular states analogy to the sequence variants in genomic analysis. Applying LiverCT on liver cancer datasets, we find that the “deviated” states of T cells are highly correlated with the stress pathway activities in hepatocellular carcinoma, and the enrichments of tumor cells with the hepatocyte-cholangiocyte “intermediate” states significantly indicate poor prognosis. Besides, we find that the tumor cells of different patients have different zonation tendencies and this zonation tendency is also significantly associated with the prognosis. This reference atlas mapping framework can also be extended to any other tissues. © 2025 Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, and Genetics Society of China

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：