检索结果-内蒙古大学图书馆

Multi-omic approach decodes paradoxes of the triple-negative breast cancer: lessons for predictive, preventive and personalised medicine

引用

AMINO ACIDS 2018年第3-4期50卷 383-395页

作者： Golubnitschaja, Olga Filep, Nora Yeghiazaryan, Kristina Blom, Henricus Johannes Hofmann-Apitius, Martin Kuhn, Walther Rheinische Friedrich Wilhelms Univ Bonn Dept Radiol Sigmund Freud Str 25 D-53105 Bonn Germany Rheinische Friedrich Wilhelms Univ Bonn Breast Canc Res Ctr Bonn Germany Rheinische Friedrich Wilhelms Univ Bonn Ctr Integrated Oncol Bonn Germany Fraunhofer Inst Algorithms & Sci Comp SCAI Dept Bioinformat St Augustin Germany Univ Med Ctr Freiburg Dept Gen Pediat Adolescent Med & Neonatol Lab Clin Biochem & Metab Freiburg Germany Rheinische Friedrich Wilhelms Univ Bonn Ctr Obstet & Gynaecol Bonn Germany

Breast cancer epidemic in the early twenty-first century results in around two million new cases and half-a-million of the disease-related deaths registered annually worldwide. A particularly dramatic situation is attributed to some specific patient subgroups such as the triple-negative breast cancer (TNBC). TNBC is a particularly aggressive type of breast cancer lacking clear diagnostic approach and targeted therapies. Consequently, more than 50% of the TNBC patients die of the metastatic BC within the first 6 months of the diagnosis. In the current study we have hypothesised that multi-omic approach utilising blood samples may lead to discovery of a unique molecular signature of the TNBC subtype. The results achieved demonstrate, indeed, multi-omics as highly promising approach that could be of great clinical utility for development of predictive diagnosis, targeted prevention and treatments tailored to the person-overall advancing the management of the TNBC.

关键词： Breast cancer Triple-negative breast cancer Menopause *Omics Predictive diagnosis Biomarker pattern

来源：评论

学校读者我要写书评

暂无评论

FullMeSH: improving large-scale MeSH indexing with full text

引用

BIOINFORMATICS 2020年第5期36卷 1533-1541页

作者： Dai, Suyang You, Ronghui Lu, Zhiyong Huang, Xiaodi Mamitsuka, Hiroshi Zhu, Shanfeng Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Fudan Univ Shanghai Key Lab Intelligent Informat Proc Shanghai 200433 Peoples R China NIH Natl Ctr Biotechnol Informat Natl Lib Med Bldg 10 Bethesda MD 20892 USA Charles Sturt Univ Sch Comp & Math Albury NSW 2640 Australia Kyoto Univ Bioinformat Ctr Inst Chem Res Uji Kyoto Japan Aalto Univ Dept Comp Sci Espoo Finland Fudan Univ Shanghai Inst Artificial Intelligence Algorithms Shanghai 200433 Peoples R China Fudan Univ Inst Sci & Technol Brain Inspired Intelligence Shanghai 200433 Peoples R China Fudan Univ Minist Educ Key Lab Computat Neurosci & Brain Inspired Intell Shanghai Peoples R China

Motivation: With the rapidly growing biomedical literature, automatically indexing biomedical articles by medical Subject Heading (MeSH), namely MeSH indexing, has become increasingly important for facilitating hypothesis generation and knowledge discovery. Over the past years, many large-scale MeSH indexing approaches have been proposed, such as medical Text Indexer, MeSHlabeler, DeepMeSH and MeSHProbeNet. However, the performance of these methods is hampered by using limited information, i.e. only the title and abstract of biomedical articles. Results: We propose FullMeSH, a large-scale MeSH indexing method taking advantage of the recent increase in the availability of full text articles. compared to DeepMeSH and other state-of-the-art methods, FullMeSH has three novelties: (i) Instead of using a full text as a whole, FullMeSH segments it into several sections with their normalized titles in order to distinguish their contributions to the overall performance. (ii) FullMeSH integrates the evidence from different sections in a 'learning to rank' framework by combining the sparse and deep semantic representations. (iii) FullMeSH trains an Attention-based Convolutional Neural Network for each section, which achieves better performance on infrequent MeSH headings. FullMeSH has been developed and empirically trained on the entire set of 1.4 million full-text articles in the Pubmed Central Open Access subset. It achieved a Micro F-measure of 66.76% on a test set of 10 000 articles, which was 3.3% and 6.4% higher than DeepMeSH and MeSHlabeler, respectively. Furthermore, FullMeSH demonstrated an average improvement of 4.7% over DeepMeSH for indexing Check Tags, a set of most frequently indexed MeSH headings.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A review on multiplatform evaluations of semi-automatic open-source based image segmentation for cranio-maxillofacial surgery

引用

compUTER METHODS AND PROGRAMS IN BIOmedICINE 2019年 182卷 105102-000页

作者： Wallner, Juergen Schwaiger, Michael Hochegger, Kerstin Gsaxner, Christina Zemann, Wolfgang Egger, Jan Med Univ Graz Dept Oral & Maxillofacial Surg Auenbruggerpl 5-1 A-8036 Graz Austria Comp Algorithms Med Lab A-8010 Graz Austria Graz Univ Technol Inst Comp Graph & Vis Inffeldgasse 16c-2 A-8010 Graz Austria Shanghai Jiao Tong Univ Sch Mech Engn Dong Chuan Rd 800 Shanghai 200240 Peoples R China

Background and objectives: computer-assisted technologies, such as image-based segmentation, play an important role in the diagnosis and treatment support in cranio-maxillofacial surgery. However, although many segmentation software packages exist, their clinical in-house use is often challenging due to constrained technical, human or financial resources. Especially technological solutions or systematic evaluations of open-source based segmentation approaches are lacking. The aim of this contribution is to assess and review the segmentation quality and the potential clinical use of multiple commonly available and license-free segmentation methods on different medical platforms. Methods: In this contribution, the quality and accuracy of open-source segmentation methods was assessed on different platforms using patient-specific clinical CT-data and reviewed with the literature. The image-based segmentation algorithms GrowCut, Robust Statistics Segmenter, Region Growing 3D, Otsu & Picking, Canny Segmentation and Geodesic Segmenter were investigated in the mandible on the platforms 3D Slicer, MITK and MeVislab. comparisons were made between the segmentation algorithms and the ground truth segmentations of the same anatomy performed by two clinical experts (n = 20). Assessment parameters were the Dice Score Coefficient (DSC), the Hausdorff Distance (HD), and Pearsons correlation coefficient (r). Results: The segmentation accuracy was highest with the GrowCut (DSC 85.6%, HD 33.5 voxel) and the Canny (DSC 82.1%, HD 8.5 voxel) algorithm. Statistical differences between the assessment parameters were not significant (p < 0.05) and correlation coefficients were close to the value one (r > 0.94) for any of the comparison made between the segmentation methods and the ground truth schemes. Functionally stable and time-saving segmentations were observed. Conclusion: High quality image-based semi-automatic segmentation was provided by the GrowCut and the Canny segmentation method.

关键词： Multi-platform Algorithmic Bone segmentation Patient-individualized medicine

来源：评论

学校读者我要写书评

暂无评论

Deep learning-a first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact

引用

PEERJ compUTER SCIENCE 2021年 7卷 e773页

作者： Egger, Jan Pepe, Antonio Gsaxner, Christina Jin, Yuan Li, Jianning Kern, Roman Graz Univ Technol Fac Comp Sci & Biomed Engn Inst Comp Graph & Vis Graz Austria Comp Algorithms Med Lab Graz Austria Med Univ Graz Dept Oral & Maxillofacial Surg Graz Austria Univ Med Essen Inst AI Med IKIM Essen Germany Res Ctr Connected Healthcare Big Data Zhejiang Lab Hangzhou Zhejiang Peoples R China Med Univ Graz Dept Neurosurg Res Unit Expt Neurotraumatol Graz Austria Know Ctr Knowledge Discovery Graz Austria Graz Univ Technol Inst Interact Syst & Data Sci Graz Austria

Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by drawing inspiration from the learning of a human brain. Similar to the basic structure of a brain, which consists of (billions of) neurons and connections between them, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10,000 publications per year in the last years. For example, the search engine Pubmed alone, which covers only a sub-set of all publications in the medical field, provides already over 11,000 results in Q3 2020 for the search term 'deep learning', and around 90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines and outline the research impact that they already have during a short period of time. The categories (computer vision, language processing, medical informatics and additional works) have been chosen according to the underlying data sources In we r

关键词： Deep learning Artificial neural networks Machine learning Data analysis Image analysis Language processing Speech recognition Big data medical image analysis Meta-review

来源：评论

学校读者我要写书评

暂无评论

DeepDock: Enhancing Ligand-protein Interaction Prediction by a Combination of Ligand and Structure Information

DeepDock: Enhancing Ligand-protein Interaction Prediction by...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Liao, Zhirui You, Ronghui Huang, Xiaodi Yao, Xiaojun Huang, Tao Zhu, Shanfeng Fudan Univ Sch Comp Sci Shanghai Peoples R China Fudan Univ Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China Charles Sturt Univ Sch Comp & Math Albury NSW Australia Macau Univ Sci & Technol State Key Lab Qual Res Chinese Med Taipa Macao Peoples R China Hong Kong Baptist Univ Sch Chinese Med Hong Kong Peoples R China Shenzhen Zhiyao Informat Technol Ltd Shenzhen Peoples R China Fudan Univ Shanghai Inst Artificial Intelligence Algorithms Shanghai Peoples R China Fudan Univ ISTBI Shanghai Peoples R China Nanjing Univ Inst Artificial Intelligence Biomed Nanjing Peoples R China

ISBN: (纸本)9781728118673

The prediction of precise protein-ligand binding activities can accelerate drug discovery by virtual screening-a computational technique that predicts whether a small molecule ligand is able to bind to a specific target. Thus, it is crucial to improve the performance of virtual screening. However, previous models for solving this problem are either ligand- based or structure-based. In this paper, we propose a universal deep neural network model called DeepDock that predicts protein-ligand interaction by using both ligand and structure information. Using the combination of two types of information, our model consists of embedding, convolution, max pooling, and fully-connected layers. In particular, different types of inputs are concatenated before being fed into the fully-connected layers. In the experiments, we compare our approach to the competing methods against two benchmark datasets under different settings. The experiment results have demonstrated that DeepDock can improve predictive performance by more than 4% on both DUD-E and MUV datasets in terms of AUPR

关键词： virtual screening ligand-protein interaction structure-based ligand-based deep learning neural network

来源：评论

学校读者我要写书评

暂无评论

Augmented Reality for Head and Neck Carcinoma Imaging: Description and Feasibility of an Instant Calibration, Markerless Approach

引用

compUTER METHODS AND PROGRAMS IN BIOmedICINE 2021年 200卷 105854-105854页

作者： Gsaxner, Christina Pepe, Antonio Li, Jianning Ibrahimpasic, Una Wallner, Juergen Schmalstieg, Dieter Egger, Jan Graz Univ Technol Inst Comp Graph & Vis Inffeldgasse 16 A-8010 Graz Austria Med Univ Graz Dept Oral & Maxillofacial Surg Auenbruggerpl 5 A-8036 Graz Austria Comp Algorithms Med Lab Graz Austria BioTechMed Graz Mozartgasse 12-2 A-8010 Graz Austria AZ Monica Hosp Antwerp Dept Cranio Maxillofacial Surg Antwerp Belgium Antwerp Univ Hosp Antwerp Belgium

Background and Objective: Augmented reality (AR) can help to overcome current limitations in computer assisted head and neck surgery by granting "X-ray vision" to physicians. Still, the acceptance of AR in clinical applications is limited by technical and clinical challenges. We aim to demonstrate the benefit of a marker-free, instant calibration AR system for head and neck cancer imaging, which we hypothesize to be acceptable and practical for clinical use. Methods: We implemented a novel AR system for visualization of medical image data registered with the head or face of the patient prior to intervention. Our system allows the localization of head and neck carcinoma in relation to the outer anatomy. Our system does not require markers or stationary infrastructure, provides instant calibration and allows 2D and 3D multi-modal visualization for head and neck surgery planning via an AR head-mounted display. We evaluated our system in a pre-clinical user study with eleven medical experts. Results: medical experts rated our application with a system usability scale score of 74.8 +/- 15.9, which signifies above average, good usability and clinical acceptance. An average of 12.7 +/- 6.6 minutes of training time was needed by physicians, before they were able to navigate the application without assistance. Conclusions: Our AR system is characterized by a slim and easy setup, short training time and high usability and acceptance. Therefore, it presents a promising, novel tool for visualizing head and neck cancer imaging and pre-surgical localization of target structures. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Augmented Reality Head and Neck Surgery computer-Assisted Surgery Image-guided Surgery medical Visualization

来源：评论

学校读者我要写书评

暂无评论

Prediction of interactiveness of proteins and nucleic acids based on feature selections

引用

MOLECULAR DIVERSITY 2010年第4期14卷 627-633页

作者： Yuan, YouLang Shi, XiaoHe Li, XinLei Lu, WenCong Cai, YuDong Gu, Lei Liu, Liang Li, MinJie Kong, XiangYin Xing, Meng Shanghai Univ Coll Sci Dept Chem Chem Data Min Lab Shanghai 200444 Peoples R China Shanghai Jiao Tong Univ Sch Med Inst Hlth Sci Shanghai 200030 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci Shanghai Peoples R China Shanghai Univ Inst Syst Biol Shanghai 200444 Peoples R China Univ Bonn B IT Dept Life Sci Informat D-53113 Bonn Germany Fraunhofer Inst Algorithms & Sci Comp SCAI Dept Bioinformat D-53754 St Augustin Germany

It is important to identify which proteins can interact with nucleic acids for the purpose of protein annotation, since interactions between nucleic acids and proteins involve in numerous cellular processes such as replication, transcription, splicing, and DNA repair. This research tries to identify proteins that can interact with DNA, RNA, and rRNA, respectively. mRMR (Minimum redundancy and maximum relevance), with its elegant mathematical formulation, has been applied widely in processing biological data and feature analysis since its introduction in 2005. mRMR plus incremental feature selection (IFS) is known to be very efficient in feature selection and analysis, and able to improve both effectiveness and efficiency of a prediction model. IFS is applied to decide how many features should be selected from feature list provided by mRMR. In the end, the selected features of mRMR and IFS are further refined by a conventional feature selection method-forward feature wrapper (FFW), by reordering the features. Each protein is coded by 132 features including amino acid compositions and physicochemical properties. After the feature selection, k-Nearest Neighbor algorithm, the adopted prediction model, is trained and tested. As a result, the optimized prediction accuracies for the DNA, RNA, and rRNA are 82.0, 83.4, and 92.3%, respectively. Furthermore, the most important features that contribute to the prediction are identified and analyzed biologically. The predictor, developed for this research, is available for public access at http://***/protein_na_mrmr/.

关键词： Nucleic acid and protein interaction Feature selection Prediction mRMR Forward feature wrapper

来源：评论

学校读者我要写书评

暂无评论

GENERALISATION OF SEGMENTATION USING GENERATIVE ADVERSARIAL NETWORKS 21

GENERALISATION OF SEGMENTATION USING GENERATIVE ADVERSARIAL ...

引用

21st IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Ferreira, Andre Luijten, Gijs Puladi, Behrus Kleesiek, Jens Alves, Victor Egger, Jan Univ Hosp Essen AoR Inst AI Med IKIM Essen Germany Univ Minho Ctr Algoritmi LASI Braga Portugal Comp Algorithms Med Lab Graz Austria Graz Univ Technol Inst Comp Graph & Vis ICG Graz Austria Univ Hosp Essen AoR Canc Res Ctr Cologne Essen CCCE West German Canc Ctr Essen WTZ Essen Germany Univ Hosp RWTH Aachen Inst Med Informat Aachen Germany Univ Hosp RWTH Aachen Dept Oral & Maxillofacial Surg Aachen Germany German Canc Consortium DKTK Partner Site Essen Essen Germany TU Dortmund Univ Dept Phys Dortmund Germany Univ Hosp Essen Ctr Virtual & Extended Real Med ZvRM Essen Germany

ISBN: (纸本)9798350313345;9798350313338

State-of-the-art deep learning algorithms are easily biased and evaluated in misleading scenarios, especially in the medical context, where scenarios change rapidly and diseases develop quickly. The BraTS 2024 GoAT challenge aims to evaluate how brain tumour segmentation algorithms can adapt to different circumstances when these are not available for training. Our solution utilises state-of-the-art conditional generative adversarial networks to generate realistic new cases and train a segmentation algorithm that takes advantage of the convolutions and attention mechanisms. Our solution achieved a DSC value of 0.855, 0.863, 0.883 and an HD95 value of 24.83, 24.10 and 21.72 for the enhancing tumour, the tumour core and the whole tumour in the validation set, respectively.

关键词： Generative Adversarial Networks Synthetic data nnU-Net Swin UNETR Segmentation

来源：评论

学校读者我要写书评

暂无评论

medical deep learning-A systematic meta-review

引用

compUTER METHODS AND PROGRAMS IN BIOmedICINE 2022年 221卷 106874-106874页

作者： Egger, Jan Gsaxner, Christina Pepe, Antonio Pomykala, Kelsey L. Jonske, Frederic Kurz, Manuel Li, Jianning Kleesiek, Jens Graz Univ Technol Inst Comp Graph & Vis Fac Comp Sci & Biomed Engn Inffeldgasse 16 A-8010 Graz Styria Austria Med Univ Graz Dept Oral &Maxillofacial Surg Auenbruggerpl 5-1 A-8036 Graz Styria Austria Comp Algorithms Med Lab Graz Styria Austria Univ Med Essen Inst Med IKIM Girardetstr 2 D-45131 Essen Germany Univ Med Essen Canc Res Ctr Cologne Essen CCCE Hufelandstr 55 D-45147 Essen Germany German Canc Consortium DKTK Partner Site Essen Hufelandstr 55 D-45147 Essen Germany

Deep learning has remarkably impacted several different scientific disciplines over the last few years. For example, in image processing and analysis, deep learning algorithms were able to outperform other cutting-edge methods. Additionally, deep learning has delivered state-of-the-art results in tasks like autonomous driving, outclassing previous attempts. There are even instances where deep learning outperformed humans, for example with object recognition and gaming. Deep learning is also showing vast potential in the medical domain. With the collection of large quantities of patient records and data, and a trend towards personalized treatments, there is a great need for automated and reliable processing and analysis of health information. Patient data is not only collected in clinical centers, like hospitals and private practices, but also by mobile healthcare apps or online websites. The abundance of collected patient data and the recent growth in the deep learning field has resulted in a large increase in research effort s. In Q2/2020, the search engine Pubmed returned already over 11,0 0 0 results for the search term 'deep learning', and around 90% of these publications are from the last three years. However, even though Pubmed represents the largest search engine in the medical field, it does not cover all medical-related publications. Hence, a complete overview of the field of 'medical deep learning' is almost impossible to obtain and acquiring a full overview of medical sub-fields is becoming increasingly more difficult. Nevertheless, several review and survey articles about medical deep learning have been published within the last few years. They focus, in general, on specific medical scenarios, like the analysis of medical images containing specific pathologies. With these surveys as a foundation, the aim of this article is to provide the first high-level, systematic meta-review of medical deep learning surveys. (c) 2022 The Author(s). Published by Elsevi

关键词： Deep learning Artificial neural networks Machine learning Data analysis Image analysis medical image analysis medical image processing medical imaging Patient data Pathology Detection Segmentation Registration Generative adversarial networks Pubmed Systematic Review Survey Meta-review Meta-survey

来源：评论

学校读者我要写书评

暂无评论

Applying Disentanglement in the medical Domain: An Introduction for the MAD Workshop 1st

Applying Disentanglement in the Medical Domain: An Introduct...

引用

1st Workshop on medical Applications with Disentanglements (MAD) at the MICCAI Conference

作者： Fragemann, Jana Liu, Xiao Li, Jianning Tsaftaris, Sotirios A. Egger, Jan Kleesiek, Jens Univ Hosp Essen AoR Inst AI Med IKIM Girardetstr 2 D-45131 Essen Germany Univ Edinburgh Sch Engn Edinburgh EH9 3FG Midlothian Scotland Graz Univ Technol TUG Inst Comp Graph & Vis ICG Inffeldgasse 16 A-8010 Graz Austria Comp Algorithms Med Lab Graz Austria Alan Turing Inst London NW1 2DB England Univ Hosp Essen AoR Canc Res Ctr Cologne Essen CCCE Hufelandstr 55 D-45147 Essen Germany German Canc Consortium DKTK Partner Site EssenHufelandstr 55 D-45147 Essen Germany

ISBN: (纸本)9783031250453;9783031250460

For medical applications, trustworthiness, interpretability, and robustness are necessary properties of (deep) neural networks. For generative models, one approach towards this could be analyzing and structuring the latent space representation. In this context, the term disentanglement is often used, but still not uniquely defined. In 2022, we organized the first workshop about medical Applications with Disentanglements (MAD) at the MICCAI conference in Singapore (https://mad. ***/). The workshop had a general call for disentanglement papers in the medical field and the submitted papers are published in a Springer challenge proceedings. The aim of the introduction paper of this proceeding is to present the necessary background information for them. Thus, we give a short overview of this field and how challenges for deep learning in healthcare could be addressed with the help of disentanglement.

关键词： Disentanglement Generative models Deep learning MAD Workshop MICCAI

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：