检索结果-内蒙古大学图书馆

2024 ieee International conference on bioinformatics and Biomedicine, BIBM 2024

作者： Zhu, Ziyi Chen, C. L. Philip Liu, Haiqi Zhang, Tong School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Lab Guangzhou China Guangdong Provincial Key Laboratory of Computational Ai Models and Cognitive Intelligence China Engineering Research Center of the Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human China

ISBN: (纸本)9798350386226

Skeleton-based methods in Gait Emotion Recognition (GER) that abstract gait as a spatial-temporal graph in a non-Euclidean space have achieved remarkable success. However, existing studies neglect capturing crucial spatial-temporal implicit dependencies and make insufficient use of manually crafted affective complementary information. In this paper, we propose a novel Temporal Group Attention Network with Affective Complementary Learning (T2A). Specifically, we propose a Temporal Group Attention (TGA) module, which captures spatial-temporal implicit dependencies and crucial features across space-time. Moreover, we design an affective complementary learning strategy, which first introduces an Affective Denoising (AD) module to reduce the impact of noise in weak emotion-related information and learn augmented affective features. Then, we adopt a decision-level fusion of the denoised affective features with the deep skeleton features to compensate for the discrepancies between different feature spaces. Experimental results on Emotion-Gait and EMOGAIT demonstrate that our proposed method significantly outperforms state-of-the-art methods. © 2024 ieee.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

A Multi-objective Cluster-based Biased Random-Key Genetic Algorithm with Online Parameter Control Applied to Protein Structure Prediction 17

A Multi-objective Cluster-based Biased Random-Key Genetic Al...

引用

17th conference on Computer Science and intelligence Systems (FedCSIS)

作者： Marchi, Felipe Parpinelli, Rafael Stubs Santa Catarina State Univ Appl Comp Grad Program Joinville SC Brazil

ISBN: (数字)9788396242396

ISBN: (纸本)9788396242396

The protein structure prediction problem is one of the most important bioinformatics problems. computational methods can be used to approach this problem and de novo methods are able to generate protein structures without the need of having known similar structures to the predicted protein. These methods transform the prediction problem into an optimization problem, using optimization models that combine different energy functions and high-level information. These models usually have only a single optimization objective. However, it is known that this single objective optimization approach may harm the optimization search due to the existence of conflicts between the different terms that compose the optimization objective. The proposed model has three objectives: energy function, secondary structure, and contact maps. A multi-objective Biased RandomKey Genetic Algorithm (BRKGA) with online parameter control, named MOBO, is proposed as the optimizer. The final predictor comprises two phases of the MOBO algorithm and selects a final structure using the MUFOLD-CL clustering method. Results obtained demonstrated that the proposed predictor generated highly competitive results with the literature.

关键词： bioinformatics Multi-objective Optimization Evolutionary Computation Clustering Online Parameter Tuning Protein Structure Prediction

来源：评论

学校读者我要写书评

暂无评论

LCCH:A low computational complexity hybrid model based on the half-router attention for biopharmaceutical indicators prediction

LCCH:A low computational complexity hybrid model based on th...

引用

ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Yichen Song Chenxi Xia Simengxu Qiao Changdi Li Qunshan He Xinggao Liu Zhejiang University

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

AI4Biopharmaceutical is an important field of interest in both academia and industry. Due to the characteristics of biopharmaceutical industry data and the hardware limitation of real factories, biopharmaceutical indicators prediction models need to balance computational complexity and prediction effectiveness. We propose a low computational complexity hybrid model (LCCH) combing the biopharmaceutical mechanism with artificial intelligence for biopharmaceutical indicators prediction. We use the Doolittle method to reduce the computational complexity of the online decomposition phase to O(1), while employing the half-router attention incorporating biopharmaceutical mechanism to significantly reduce the computational complexity of the prediction phase. The model achieves the state-of-the-art results for the prediction of five biopharmaceutical indicators: amino nitrogen, reducing sugar, total sugar, bacterial concentration,and viscosity in the erythromycin pharmaceutical scenario. Compared to the other outstanding baseline models from the last three years, LCCH demonstrates the superiority and potential of the hybrid model for biopharmaceutical industrial applications.

关键词： Viscosity Microorganisms computational modeling Biological system modeling Predictive models Sugar industry Production facilities Nitrogen computational complexity Pharmaceuticals

来源：评论

学校读者我要写书评

暂无评论

SSGCN: A Spatial Domain Identification Framework Based on Graph Convolutional Neural Network with Adaptively Reweighting Edges

SSGCN: A Spatial Domain Identification Framework Based on Gr...

引用

ieee International conference on bioinformatics and computational biology (ICBCB)

作者： Wenhui Du Chunyang Chai Yujie Gu Shuang Hao Huiyan Sun School of Artificial Intelligence JiLin University Changchun China

ISBN: (数字)9798350375749

ISBN: (纸本)9798350375756

Leveraging spatial location and gene expression of spots for spatial domain identification through spatial transcriptomics data is important for understanding the tissue heterogeneity, and comprehending developmental processes. In this study, we propose a spatial domain identification framework SSGCN based on graph neural network, which fully considers spatial locations and similarity between spots according to their gene expression. Specifically, SSGCN takes spatial location and gene expression as input, reassigns edges' weights of graph by calculating the expression similarity between nodes, and ultimately learns a low-dimensional representation for spatial domain assignment. We conduct extensive experiments on four datasets from various platforms with different resolutions. The experimental results show that SSGCN is superior to other spatial clustering methods, and also demonstrate its cross-platform applicability.

关键词： Runtime Convolution Transcriptomics Olfactory Graph neural networks Spatial databases Breast cancer Organisms Gene expression Spatial resolution

来源：评论

学校读者我要写书评

暂无评论

Determination of Neuron Activation States Facilitated by Artificial intelligence

Determination of Neuron Activation States Facilitated by Art...

引用

2022 ieee International conference on bioinformatics and Biomedicine, BIBM 2022

作者： Topasna, Ana Xie, Eric Park, Sammy Huang, Sophia Wu, Xiaowei Xie, Hehuang Epigenomics and Computational Biology Lab Fralin Life Sciences Institute Virginia Tech BlacksburgVA24061 United States Department of Biomedical Sciences and Pathobiology Virginia-Maryland College of Veterinary Medicine Virginia Tech BlacksburgVA24061 United States Department of Biology Department of Psychology Reed College PortlandOR97202 United States Department of Computer Science University of Virginia CharlottesvilleVA22903 United States Department of Engineering Systems and Environment University of Virginia CharlottesvilleVA22903 United States Department of Statistics Virginia Tech BlacksburgVA24061 United States

ISBN: (纸本)9781665468190

Environmental stimuli evoke gene expression changes in neurons to achieve synaptic reconfiguration and memory formation. During the neuronal activation process, immediate early genes (IEGs) and late response genes (LRGs) have been defined according to their temporal changes in gene expression. Recent single cell studies indicated that different subtypes of neurons may have distinct sets of IEGs and LRGs in response to neuronal activation. Currently, it remains unexplored whether the gene expression patterns provided by single cell studies are sufficient to distinguish neuronal subtypes together with their activation states. In this study, we adopt Artificial Neural Network (ANN) models to predict neuron cell subtypes and activation states simultaneously based on a single cell RNA-Seq dataset generated from mouse visual cortex in response to light stimulus exposure. Four kinds of ANN architectures were designed to compare the prediction efficacies using two kinds of inputs: the full list of genes or the markers selected for neuron cell subtypes, IEGs, and LRGs. We observed that, with single-cell RNA-Seq data, neuron cell subtypes and activation states can be predicted accurately by ANN models. Moreover, compared to feeding the entire list of genes to fully connected ANNs, using selected marker genes and partially connected ANNs achieves comparable prediction accuracy with substantially improved computational efficiency. Altogether, our results demonstrated that ANNs are powerful in predicting neuron activation states and cell subtypes and may serve as a useful data mining approach to explore single cell RNA-Seq profiles. © 2022 ieee.

关键词： RNA

来源：评论

学校读者我要写书评

暂无评论

Flatter Minima of Loss Landscapes Correspond with Strong Corruption Robustness 27th

Flatter Minima of Loss Landscapes Correspond with Strong C...

引用

27th International conference on Pattern Recognition, ICPR 2024

作者： Zhong, Liqun Zhu, Kaijie Yang, Ge School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Science Beijing100190 China

ISBN: (纸本)9783031781063

To develop deep neural networks (DNNs) that maintain strong performance under different natural image corruptions such as noise and blur, namely with strong corruption robustness, is essential for real-world applications. Current methods to enhance corruption robustness focus primarily on data augmentation. But these methods are often empirically designed without a clear theoretical foundation. Here, we show that flatter minima of loss landscapes generally correspond with stronger corruption robustness. Based on this finding, we have developed a method named LASAM-DA that combines layer adaptive sharpness-aware minimization(LASAM) with data augmentation (DA) to boost corruption robustness. LASAM pushes DNNs towards flat minima of loss landscapes, while DA guides DNNs to memorize diverse data distributions. LASAM and DA reinforce each other without interference. Experiments on CIFRA-10-C, CIFAR-100-C and Tiny-ImageNet-C show that LASAM-DA outperforms competing methods and achieves state-of-the-art performance. LASAM-DA is versatile, not restricted to specific data types, network architectures or data augmentation methods. Code is openly accessible at https://***/cbmi-group/Flatter-Minima-Helps-for-Robustness. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning

引用

ieee TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE intelligence 2022年第10期44卷 7112-7127页

作者： Elnaggar, Ahmed Heinzinger, Michael Dallago, Christian Rehawi, Ghalia Wang, Yu Jones, Llion Gibbs, Tom Feher, Tamas Angerer, Christoph Steinegger, Martin Bhowmik, Debsindhu Rost, Burkhard Tech Univ Munich TUM Dept Informat Bioinformat & Computat Biol i12 D-85748 Garching Germany Med AI Technol Wu Xi Ltd Wuxi 214000 Jiangsu Peoples R China Google Google AI Mountain View CA 94043 USA NVIDIA Santa Clara CA 95051 USA Seoul Natl Univ Sch Biol Sci Seoul 08826 South Korea Oak Ridge Natl Lab POB 2009 Oak Ridge TN 37830 USA

computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive models (Transformer-XL, XLNet) and four auto-encoder models (BERT, Albert, Electra, T5) on data from UniRef and BFD containing up to 393 billion amino acids. The protein LMs (pLMs) were trained on the Summit supercomputer using 5616 GPUs and TPU Pod up-to 1024 cores. Dimensionality reduction revealed that the raw pLM-embeddings from unlabeled data captured some biophysical features of protein sequences. We validated the advantage of using the embeddings as exclusive input for several subsequent tasks: (1) a per-residue (per-token) prediction of protein secondary structure (3-state accuracy Q3=81%-87%);(2) per-protein (pooling) predictions of protein sub-cellular location (ten-state accuracy: Q10=81%) and membrane versus water-soluble (2-state accuracy Q2=91%). For secondary structure, the most informative embeddings (ProtT5) for the first time outperformed the state-of-the-art without multiple sequence alignments (MSAs) or evolutionary information thereby bypassing expensive database searches. Taken together, the results implied that pLMs learned some of the grammar of the language of life. All our models are available through https://***/agemagician/ProtTrans.

关键词： Proteins Training Amino acids Task analysis Databases computational modeling Three-dimensional displays computational biology high performance computing machine learning language modeling deep learning

来源：评论

学校读者我要写书评

暂无评论

AI in bioinformatics and Genomics

AI in Bioinformatics and Genomics

引用

Intelligent & Innovative Practices in Engineering & Management (IIPEM), International conference on

作者： Sumita Mukherjee Kavita Thapliyal Alka Maurya Naina Chaudhary Sushree Sudeshna AIBS Amity University Noida India SIIB Pune India Amity University Tashkent

ISBN: (数字)9798350390049

ISBN: (纸本)9798350390056

Artificial intelligence (AI) has revolutionized various fields, including bioinformatics and genomics, by offering powerful tools and techniques to analyse and interpret complex biological data. This research paper explores the significant role of AI in bioinformatics and genomics, highlighting its applications, advancements, challenges, and future directions. Through a comprehensive review of relevant literature, case studies, and examples, and aims the power of AI transformation to provide insights on the study of biological systems, accelerating advancements in personalized medicine and biotechnology and enhancing our understanding of genomics. These areas have risen from the needs of biologists to utilize and help interpret the vast amounts of data that are constantly being gathered in genomic research. Artificial intelligence (AI) has increasingly gained attention in bioinformatics research and computational molecular biology has become common for the researchers to apply the off-shelf systems with the availability of different types of AI algorithms. At present researchers are facing difficulties in choosing the best method that could be applied to a specific data set, with various intelligent methods available in the literature. The researchers need to apply machine learning and AI with different tools and techniques which present the data in an annotated with context, estimates of accuracy, proper predictions and explanation. comprehensible fashion. This article aims to review the use of AI in the areas of bioinformatics and computational molecular biology (DNA sequencing). The AI in functional genomics, and its application in this paper defines the importance of applicability and interpretability including ethical, legal and economic issues.

关键词： Biotechnology Accuracy Reviews Systems biology Genomics Prediction algorithms biology Reflection bioinformatics Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

MCA-Net: Multi-Feature Coding and Attention Convolutional Neural Network for Predicting lncRNA-Disease Association

引用

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第5期19卷 2907-2919页

作者： Zhang, Yuan Ye, Fei Gao, Xieping Xiangtan Univ Minist Educ Key Lab Intelligent Comp & Informat Proc Xiangtan 411105 Peoples R China Xiangnan Univ Key Lab Med Imaging & Artif Intelligence Hunan Pr Chenzhou 423000 Peoples R China

With the advent of the era of big data, it is troublesome to accurately predict the associations between lncRNAs and diseases based on traditional biological experiments due to its time-consuming and subjective. In this paper, we propose a novel deep learning method for predicting lncRNA-disease associations using multi-feature coding and attention convolutional neural network (MCA-Net). We first calculate six similarity features to extract different types of lncRNA and disease feature information. Second, a multi-feature coding method is proposed to construct the feature vectors of lncRNA-disease association samples by integrating the six similarity features. Furthermore, an attention convolutional neural network is developed to identify lncRNA-disease associations under 10-fold cross-validation. Finally, we evaluate the performance of MCA-Net from different perspectives including the effects of the model parameters, distinct deep learning models, and the necessity of attention mechanism. We also compare MCA-Net with several state-of-the-art methods on three publicly available datasets, i.e., LncRNADisease, Lnc2Cancer, and LncRNADisease2.0. The results show that our MCA-Net outperforms the state-of-the-art methods on all three dataset. Besides, case studies on breast cancer and lung cancer further verify that MCA-Net is effective and accurate for the lncRNA-disease association prediction.

关键词： Diseases Predictive models Deep learning Semantics Encoding bioinformatics Convolutional neural networks lncRNA-disease association prediction multi-feature coding deep learning attention mechanism

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Model for Computer Vision: Sustainable Image Classification Using Machine Learning 1st

Deep Learning Model for Computer Vision: Sustainable Image C...

引用

1st International conference on Sustainable Development through Machine Learning, Artificial intelligence, and IoT, ICSD 2023

作者： Alankar, Bhavya Chauhan, Ritu Kaur, Harleen Ebadati, E. Omid Mahdi Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University UP Noida India Department of Mathematics and Computer Science Kharazmi University Tehran Iran

ISBN: (纸本)9783031470547

The core challenge within computer vision is image classification, which involves assigning an image a label and categorizing it using the features that have been extracted from it. Even though this work appears rudimentary, it requires the application involving artificial intelligence, specifically Deep Learning, a subfield of Machine Learning. In this paper, we have discussed how Convolutional Neural Networks (CNN), the most modern technique for image classification, are used in Deep Learning to classify images. We have discussed the applications of image classification and drawn the conclusion that using deep learning is preferable to using machine learning. Additionally, we have also presented a comparative study of various CNN models to observe how they could potentially be employed as pre-trained models for the image classification job. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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：