检索结果-内蒙古大学图书馆

ieee symposium Series on computational intelligence (SSCI)

作者： CS Arvind Aditi Totla Tanisha Jain Nandini Sinha R Jyothi K Aditya Keerthan Mohammed Farhan G Sumukh Guruprasad Ak Bioinformatics Institute A*STAR Singapore Singapore RV College of Engineering Bengaluru India Dr Ambedkar Institute of Technology Bengaluru India RV Institute of Technology and Management Bengaluru India Brigade Group Bengaluru India

ISBN: (纸本)9781728190495

Plants show visible symptoms of getting infected with a disease. Presently an experienced plant pathologist can diagnose the condition through visual inspection of disease-affected plants. However, manual visualization is time-consuming and depends on the plant pathologist's expertise in identifying plant disease. Hence this problem can be solved by a computer-aided diagnostic system with artificial intelligence (CADS-AI). This system will aid in improving and protecting the yield of the plant, but it lacks trust as the existing system is not flawless. Hence, in this research work, a plant disease classification with an explainable AI pipeline is developed which ensures trust in the CADS solution. Furthermore, an expert recommendation system will act as an alternative to expert plant pathologists. Tomato leaf diseases data from the PlantVillage dataset is used in the proposed solution. Transfer learning technique was adopted in training deep neural network models with original and augmented data of 16,684 and 53,476 images respectively. The best model for the dataset was efficientNet B5 with best F1 score accuracy of 0.9842 and 0.9930. The predicted output of B5 was interpreted with explainable AI techniques and validated using YOLOv4. Inference of the proposed solution was a client-server interface where end-users can upload infected leaf images via mobile phones or web browsers. This entire system was tested in real-time with 250 volunteers with 4G mobile network or 100 MBPS wifi. The average throughput time of the system is around 4.3 seconds.

关键词： Deep learning Visualization Computer architecture Real-time systems Mobile handsets Browsers Servers

来源：评论

学校读者我要写书评

暂无评论

Low-Rank Joint Subspace Construction for Cancer Subtype Discovery

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2020年第4期17卷 1290-1302页

作者： Khan, Aparajita Maji, Pradipta Indian Stat Inst Machine Intelligence Unit Biomed Imaging & Bioinformat Lab Kolkata 700108 W Bengal India

Multimodal data integration is an important framework for cancer subtype discovery as it can blend the inherent properties of individual modalities with their cross-platform correlations to infer clinically relevant subtypes. The main problem here is the appropriate selection of relevant and complementary modalities. Another problem is the 'high dimension-low sample size' nature of each modality. The current research work proposes a novel algorithm to construct a low-rank joint subspace from the low-rank subspaces of individual high-dimensional modalities. Statistical hypothesis testing is introduced to effectively estimate the rank of each modality by separating the signal component from its noise counterpart. Two quantitative indices are proposed to evaluate the quality of different modalities, the first one assesses the degree of relevance of the cluster structure embedded within each modality, while the second measure evaluates the amount of cluster information shared between two modalities. To construct the joint subspace, the algorithm selects the most relevant modalities with maximum shared information. During data integration, the intersection between two subspaces is also considered to select cluster information and filter out the noise from different subspaces. The efficacy of clustering on the joint subspace, extracted by the proposed algorithm, is compared with that of several existing integrative clustering approaches on real-life multimodal data sets. Experimental results show that the identified subtypes have closer resemblance with the clinically established subtypes as compared to the subtypes identified by the existing approaches. Survival analysis has revealed the significant differences between survival profiles of the identified subtypes, while robustness analysis shows that the identified subtypes are not sensitive towards perturbation of the data sets.

关键词： Multimodal data analysis integrative clustering low-rank approximation multivariate normality

来源：评论

学校读者我要写书评

暂无评论

WeCoMXP: Weighted Connectivity Measure Integrating Co-Methylation, Co-Expression and Protein-Protein Interactions for Gene-Module Detection

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2020年第2期17卷 690-703页

作者： Mallik, Saurav Bandyopadhyay, Sanghamitra Indian Stat Inst Machine Intelligence Unit Kolkata 700108 W Bengal India Univ Miami Dept Publ Hlth Sci Coral Gables FL 33136 USA

The identification of modules (groups of several tightly interconnected genes) in gene interaction network is an essential task for better understanding of the architecture of the whole network. In this article, we develop a novel weighted connectivity measure integrating co-methylation, co-expression, and protein-protein interactions (called WeCoMXP) to detect gene-modules for multi-omics dataset. The proposed measure goes beyond the fundamental degree centrality measure through considering some formulation of higher-order connections. Thereafter, we apply the average linkage clustering method using the corresponding dissimilarity (distance) values of WeCoMXP scores, and utilize a dynamic tree cut method for identifying some gene-modules. We validate the modules through literature search, KEGG pathway, and gene-ontology analyses on the genes representing the modules. Furthermore, the top 10 TFs/miRNAs that are connected with the maximum number of gene-modules and that regulate/target the maximum number of genes from these connected gene-modules, are identified. Moreover, our proposed method provides a better performance than the existing methods in terms of several cluster-validity indices in maximum times.

关键词： WeCoMXP gene-module co-expression co-methylation protein-protein interactions Limma TF-miRNA-gene network

来源：评论

学校读者我要写书评

暂无评论

Deep Manifold Preserving Autoencoder for Classifying Breast Cancer Histopathological Images

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2020年第1期17卷 91-101页

作者： Feng, Yangqin Zhang, Lei Mo, Juan Sichuan Univ Machine Intelligence Lab Coll Comp Sci Chengdu 610065 Sichuan Peoples R China

Classifying breast cancer histopathological images automatically is an important task in computer assisted pathology analysis. However, extracting informative and non-redundant features for histopathological image classification is challenging due to the appearance variability caused by the heterogeneity of the disease, the tissue preparation, and staining processes. In this paper, we propose a new feature extractor, called deep manifold preserving autoencoder, to learn discriminative features from unlabeled data. Then, we integrate the proposed feature extractor with a softmax classifier to classify breast cancer histopathology images. Specifically, it learns hierarchal features from unlabeled image patches by minimizing the distance between its input and output, and simultaneously preserving the geometric structure of the whole input data set. After the unsupervised training, we connect the encoder layers of the trained deep manifold preserving autoencoder with a softmax classifier to construct a cascade model and fine-tune this deep neural network with labeled training data. The proposed method learns discriminative features by preserving the structure of the input datasets from the manifold learning view and minimizing reconstruction error from the deep learning view from a large amount of unlabeled data. Extensive experiments on the public breast cancer dataset (BreaKHis) demonstrate the effectiveness of the proposed method.

关键词： Breast cancer Manifolds Feature extraction Neural networks Training Computer architecture Histopathological image classification breast cancer diagnose manifold learning autoencoder deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Exploring Graph-Based Neural Networks for Automatic Brain Tumor Segmentation 9th

Exploring Graph-Based Neural Networks for Automatic Brain Tu...

引用

9th International symposium on From Data Models and Back, DataMod 2020

作者： Saueressig, Camillo Berkley, Adam Kang, Elliot Munbodh, Reshma Singh, Ritambhara Department of Computer Science Brown University Providence United States Center for Computational Molecular Biology Brown University Providence United States Department of Radiation Oncology Brown Alpert Medical School Providence United States

ISBN: (纸本)9783030706494

Manual evaluation of medical images, such as MRI scans of brain tumors, requires years of training, is time-consuming, and is often subject to inter-annotator variation. The automatic segmentation of medical images is a long-standing challenge that seeks to alleviate these issues, with great potential benefits for physicians and patients. In the past few years, variations of Convolutional Neural Networks (CNNs) have established themselves as the state-of-the-art methodology for this task. Recently, Graph-based Neural Networks (GNNs) have gained considerable attention in the deep learning community. GNNs exploit the structural information present in graphical data by aggregating information over connected nodes, allowing them to effectively capture relation information between data elements. In this project, we propose a GNN-based approach to brain tumor segmentation. We represent 3D MRI scans of the brain as a graph, where different regions in the images are represented by nodes and edges connect adjacent regions. We apply several variations of GNNs for the automatic segmentation of brain tumors from MRI scans. Our results show GNNs give reasonable performance on the task and allow for realistic modeling of the data. Furthermore, they are far less computationally expensive and time-consuming to train than state-of-the-art segmentation models. Lastly, we assign Shapley value-based contribution scores to input MRI features to learn what features are relevant for a particular segmentation, generating interesting insights into explaining the predictions of the proposed model. © 2021, Springer Nature Switzerland AG.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

2018 ieee Conference on computational intelligence in bioinformatics and computational biology, CIBCB 2018

2018 IEEE Conference on Computational Intelligence in Bioinf...

引用

2018 ieee Conference on computational intelligence in bioinformatics and computational biology, CIBCB 2018

ISBN: (纸本)9781538613993

The proceedings contain 22 papers. The topics discussed include: high-performance and distributed computing in a probabilistic finite element comparison study of the human lower leg model with total knee replacement;ensemble validation paradigm for intelligent data analysis in autism spectrum disorders;parameter selection for modeling of epidemic networks;analysis of grapevine gene expression data using node-based resilience clustering;RNA secondary structure graphical rendering library;chemical structure recognition and prediction: a machine learning technique;improving medical search tasks using learning to rank;and application of ensemble learning to the differential gene expression in left-right breast tumors.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Improving the prediction of cardiovascular risk with machine-learning and DNA methylation data 16

Improving the prediction of cardiovascular risk with machine...

引用

16th ieee International Conference on computational intelligence in bioinformatics and computational biology (ieee CIBCB)

作者： Cugliari, Giovanni Benevenuta, Silvia Guarrera, Simonetta Sacerdote, Carlotta Panico, Salvatore Krogh, Vittorio Tumino, Rosario Vineis, Paolo Fariselli, Piero Matullo, Giuseppe Univ Turin Dept Med Sci Turin Italy Piedmont Reference Ctr Epidemiol & Canc Prevent Turin Italy Federico II Univ Naples Dept Clin Med & Surg Naples Italy IRCCS Ist Nazl Tumori Epidemiol & Prevent Unit Milan Italy Dept Canc Registry & Histopathol Ragusa Italy Sch Publ Hlth Dept Epidemiol & Biostat London England

ISBN: (纸本)9781728114620

Classically, the cardiovascular risk of individual is evaluated using phenomenological variables (PV) such as blood pressure, body mass, smoker status, gender, age etc. Here we show that, on prospective study (after 10-15 years) these PV display a poor agreement with case-control samples. We were able to obtain more accurate predictions using both DNA methylation data and PV as input features of a Random Forest model, achieving a ROC-AUC of 0.74. Furthermore, the Random Forest output correlates with the reliability of the predictions producing a ROC-AUC of 0.90 when only the most reliable predictions are taken into consideration.

关键词： Epigenetic biomarkers DNA methylation Genomics computational statistics

来源：评论

学校读者我要写书评

暂无评论

Volumetric Representation of Biomolecules Using Cell Decomposition and Robotics 16

Volumetric Representation of Biomolecules Using Cell Decompo...

引用

16th ieee International Conference on computational intelligence in bioinformatics and computational biology (ieee CIBCB)

作者： Chakraborty, Arindam Gohil, Mahendra Kumar Savitha, G. Dasgupta, Bhaskar Indian Inst Technol Kanpur Ctr Mechatron Mech Engn Kanpur 208016 Uttar Pradesh India Banasthali Vidyapith Fac Biophys & Mol Dynam Vanasthali 304022 Rajasthan India Indian Inst Technol Kanpur Ctr Mechatron Fac Mech Engn Kanpur 208016 Uttar Pradesh India

ISBN: (纸本)9781728114620

Biological macro-molecules come in a large variety of complexes. Representation and modelling their structures are essential to provide a computational analogue of experimentally determined structures. In this work, the authors propose a representational scheme for biomolecules with the aid of approximate cell decomposition and a nano-robotic schema. The kino-chemical information of a molecule is programmatically retrieved from reliable repositories (RLooM, Protein data bank etc.) of biochemical structures and a Nano-manipulator schema is obtained from the D-H representation of the molecular chain under consideration. The molecule is enclosed in a suitable bounding box, and using approximate cell decomposition in conjunction with bonding information, a volumetric steric clash description of the chain is proposed which serves as a model for infringement function estimation and subsequent structural prediction for proteins and nucleic acids. From the robotic representation, the variable kinematic description due to rotating atoms is always at hand to generate the new corresponding infringement description within the bounding *** of appropriately indexed cells give a reasonably accurate estimate of infringement in a particular configuration and the estimates are found to be comparable to experimental results. The proposed rendering have been extensively compared with a number of professional software renderings from NMR and crystallographic data.

关键词： Biological system modeling Molecular biophysics Kinematics computational modeling Proteins Manipulators

来源：评论

学校读者我要写书评

暂无评论

Guest Editorial Data Science in Smart Healthcare: Challenges and Opportunities

引用

ieee JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2020年第11期24卷 3041-3043页

作者： Di Camillo, Barbara Nicosia, Giuseppe Buffa, Francesca Lo, Benny Univ Padua Dept Informat Engn Padua Italy Univ Cambridge Cambridge Syst Biol Ctr Dept Biochem Cambridge England Univ Catania Sch Med Dept Biomed &Biotechnol Sci Catania Italy Univ Oxford Med Sci Div Dept Oncol Computat Biol & Integrat Genom Lab Oxford England Imperial Coll London Hamlyn Ctr Dept Surg & Canc London England

The fifteen articles in this special section focus on data science used in smart healthcare applications. A shift toward a data-driven socio-economic health model is occurring. This is the result of the increased volume, velocity and variety of data collected from the public and private sector in healthcare, and biology in general. In the past five-years, there has been an impressive development of computational intelligence and informatics methods for application to health and biomedical science. However, the effective use of data to address the scale and scope of human health problems has yet to realize its full potential. The barriers limiting the impact of practical application of standard data mining and machine learning methods have been inherent to the characteristics of health data. Besides the volume of the data (‘big data’), these are challenging due to their heterogeneity, complexity, variability and dynamic nature. Finally, data management and interpretability of the results have been limited by practical challenges in implementing new and also existing standards across the different health providers and research institutions. The scope of this Special issue is to discuss some of these challenges and opportunities in health and biological data science, with particular focus on the infrastructure, software, methods and algorithms needed to analyze large datasets in biological and clinical research.

关键词： Special issues and sections Data models Machine learning Data science Machine learning Medical services bioinformatics

来源：评论

学校读者我要写书评

暂无评论

BP-DDI: Drug-drug interaction prediction based on biological information and pharmacological text

BP-DDI: Drug-drug interaction prediction based on biological...

引用

ieee International Conference on bioinformatics and Biomedicine (BIBM)

作者： Mingliang Dou Han Han Genlang Chen Fei Guo Jijun Tang College of Intelligence and Computing Tianjin University Tianjin China Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China School of Computer and Data Engineering NingboTech University Ningbo China School of Computer Science and Engineering Central South University Changsha China College of Engineering and Computing University of South Carolina U.S.

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

In the treatment of many diseases, combination drug therapy has been widely used and achieved good clinical efficacy. However, drug-drug interaction (DDI) may occur between multiple drugs and pose a huge threat to the health of patients. Therefore, predicting the presence or absence of DDI among multiple drugs is an important part of pharmacovigilance. Currently, various computational methods for DDI prediction usually use biological information such as molecular structures, targets and enzymes of drugs, or construct heterogeneous networks about drugs, diseases, and genes, so as to obtain abundant information related to drugs. In addition to biological data, pharmacology texts also contain a wealth of information about drug properties, but these texts have not yet been applied to DDI predictions. In this study, we first collect six types of pharmacology texts from DrugBank that can reflect properties of drugs, and propose a novel method named BP-DDI which can combine biological information and pharmacological text to realize DDI event prediction. BP-DDI first extracts biological features (chemical substructure features and target features) from biological data, and then extracts specific types of text features from the collected pharmacology text data. Finally, the biological features are fused with different types of pharmacological text features in order to predict DDI events. Our experiments demonstrate that BP-DDI outperforms existing methods on all three types of prediction tasks. BP-DDI achieves 0.9052 on ACC, and achieves 0.9612 on AUPR.

关键词： Drugs Semantics Prediction methods Feature extraction biology Data mining Task analysis

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：