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检索条件"任意字段=IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology"

共 3739 条记录，以下是581-590 订阅

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Learning Physiological Mechanisms that Predict Adverse Cardiovascular Events in Intensive Care Patients with Chronic Heart Disease

Learning Physiological Mechanisms that Predict Adverse Cardi...

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Annual International conference of the ieee Engineering in Medicine and biology Society (EMBC)

作者： Matthew Hodgman Emily Wittrup Kayvan Najarian Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Chronic heart disease is a burdensome, complex, and fatal condition. Learning the mechanisms driving the development of heart disease is key to early risk assessment and intervention. However, many current machine learning approaches lack sufficient interpretability. Using 2,737 patients with chronic heart disease from the MIMIC-III database, we trained an interpretable Tropical Geometry Fuzzy Neural Network to predict one-year occurrence of a severe cardiac procedure or mortality (AUROC=0.663). We present the 20 learned rules which explain the model predictions. We find that the rules are clinically valid and indicate underlying pathologies. We anticipate that with additional development and validation, these rules will aid clinicians in providing preventative care for chronic heart disease patients in intensive care units.

关键词： Heart Geometry Pathology MIMICs Machine learning Predictive models Fuzzy neural networks Physiology Risk management Diseases

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8th International conference on Algorithms for computational biology, AlCoB 2021

8th International Conference on Algorithms for Computational...

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8th International conference on Algorithms for computational biology, AlCoB 2021

ISBN: (纸本)9783030744311

The proceedings contain 12 papers. The special focus in this conference is on Algorithms for computational biology. The topics include: Improved DNA-versus-Protein Homology Search for Protein Fossils;the Maximum Weight Trace Alignment Merging Problem;can We Replace Reads by Numeric Signatures? Lyndon Fingerprints as Representations of Sequencing Reads for Machine Learning;exploiting Variable Sparsity in Computing Equilibria of Biological Dynamical Systems by Triangular Decomposition;a Recovery Algorithm and Pooling Designs for One-Stage Noisy Group Testing Under the Probabilistic Framework;novel Phylogenetic Network Distances Based on Cherry Picking;best Match Graphs with Binary Trees;Scalable and Accurate Phylogenetic Placement Using pplacer-XR;comparing Methods for Species Tree Estimation with Gene Duplication and Loss;reversal Distance on Genomes with Different Gene Content and Intergenic Regions Information.

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An Optimal Feature Selection with Neural Network-Based Classification Model for Dengue Fever Prediction 6

An Optimal Feature Selection with Neural Network-Based Class...

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6th International conference on Information Systems and Computer Networks, ISCON 2023

作者： Ramasamy, Vidhyavathi Vadivel, Sameswari Kothandapani, Sudha Mahilraj, Jenifer Sivaram, P. Sharma, Bhisham Alagappa University Department of Bioinformatics Tamilnadu Karaikudi India Alagappa University Department of Computational Logistics Tamilnadu Karaikudi India Dr M.G.R. Educational and Ri University Department of Cse Madhuravoyal TN Chennai India Npr College of Engineering Department of Cse Tamilnadu Natham India G H Raisoni College of Engineering Department of Artificial Intelligence Maharashtra Nagpur440016 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350346961

A vast range of neural network applications has been reported in recent research. This model can make various predictions using this model, and this research has taken dengue disease prediction. Uncertain Neural Network (UNN) is introduced to efficiently classify the dengue disease dataset. Research can be conducted in a wide range of fields to resolve the issue of missing and uncertain data. Also, real-time data sets contain a lot of missing attributes or values. Researchers use real-time data because it is approximately measured. Researchers thus pay more attention to disease prediction. The problem can be solved by putting into action the dengue prediction system that has been developed. The primary goal of this study is to create a reliable classification model based on feature selection for predicting dengue fever. This research work is applied for feature model creation and relative analysis to improve dengue virus prediction correctness in three stages: Collecting dengue virus datasets from the UCI Machine Learning library was the first step;secondly, a Genetic Algorithm (GA) is used for feature selection, the UNN model is used to create a classification model;As part of the final stage, Accuracy (A), Specificity (Sp), and Sensitivity (Se) can be calculated for justifying the result. A simulation of the proposed method is conducted using the WEKA tool. Therefore, the proposed classification procedure was the most effective model for predicting and detecting dengue fever.. © 2023 ieee.

关键词： Forecasting

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Explainable AI for Car Crash Detection using Multivariate Time Series 20

Explainable AI for Car Crash Detection using Multivariate Ti...

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20th ieee International conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2021

作者： Tronchin, Lorenzo Sicilia, Rosa Cordelli, Ermanno Celsi, Lorenzo Ricciardi Maccagnola, Daniele Natale, Massimo Soda, Paolo Unit of Computer Systems and Bioinformatics Università Campus Bio-Medico di Roma Italy ELIS Innovation Hub Italy Advanced Analytics Generali Italia Milano Italy

ISBN: (纸本)9781665421195

The pervasiveness of Artificial intelligence approaches in effectively supporting the decision process in many applications has raised the need to explain their behaviour. In this context, we present the application and evaluation of three eXplainable Artificial intelligence methods in a real-world multimodal task of anomaly detection on telematics data. We cope with the challenge of explaining Multivariate Time Series and of translating methods designed for images to this domain. © 2021 ieee.

关键词： Anomaly detection

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EMoNet: An environment causal learning for molecule OOD generalization

EMoNet: An environment causal learning for molecule OOD gene...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Limin Li Kuo Yang Wenjie Du Zhongchao Yi Zhengyang Zhou Yang Wang School of Software Engineering Univ. of Science & Technology of China Hefei China School of Data Science Univ. of Science & Technology of China Hefei China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Data-driven molecular computing has become an increasingly popular topic in AI for molecular and bioinformatic science. Current molecular modeling exploits the Graph Neural Networks (GNNs) to achieve the representation but they mostly fail to generalize to out-of-distribution (OOD) samples. Even though recent advances on OOD-oriented graph learning discovered the invariant rationale on graphs, they still ignore two important issues, i.e., 1) the increasing number and types of molecules expand patterns of environments on graphs, resulting in failures of invariant rationale based models, 2) the associations between discovered molecular subgraphs and corresponding properties are complex where causal substructures cannot fully interpret the labels. To this end, we propose an environment causal learning framework, EMoNet, to tackle the unresolved OOD challenge in molecular science. Specifically, we model the graph environments via bypassing invariant subgraphs. We first incorporate chemistry principle into our graph growth generator to imitate environment growth, and then devise an environment-GIB to squash out environment and finally introduce a cross-attention causal aggregation, allowing dynamic interactions between environments and invariances. We perform experiments on seven datasets and extensive experiments demonstrate strong generalization ability of EMoNet.

关键词： Chemistry Molecular computing computational modeling Generators Graph neural networks bioinformatics Artificial intelligence

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Predicting Compound-Protein Interaction by Deepening the Systemic Background via Molecular Network Feature Embedding

Predicting Compound-Protein Interaction by Deepening the Sys...

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2022 ieee International conference on bioinformatics and Biomedicine, BIBM 2022

作者： Wang, Han Zhu, Hangxu Li, Wenhao Liu, Ming Zhang, Li Xu, Dong Institute of Computational Biology Northeast Normal University School of Information Science and Technology Changchun China Changchun University of Technology School of Mathematics and Statistics School of Computer Science and Engineering Changchun China University of Missouri Bond Life Sciences Center Department of Electrical Engineering and Computer Science Columbia United States

ISBN: (纸本)9781665468190

Identifying compound-protein interactions (CPI) is crucial for drug screening, drug repurposing, and combination therapy studies. The performance of CPI prediction depends heavily on the features extracted from compounds and target proteins. The existing prediction methods use different feature combinations, but both molecular-based and network-based models have the problem of incomplete feature representations. Therefore, completely integrating the relevant features of CPI would be an effective way to solve the existing problem. This study proposed a novel model named MCPI, which integrated the PPI (protein-protein interaction) network, CCI (compound-compound interaction) network, and structure features of CPI to improve prediction performance. We compared our model with other existing methods for predicting CPI on public datasets. The experimental results showed that MCPI outperformed the peer methods. In addition, in response to the SARS-CoV-2 pandemic, we applied the model to search for potential inhibitors among FDA-approved drugs and validated the prediction results through the literature. This work may also provide potential guidance for drug development. © 2022 ieee.

关键词： Coronavirus

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SIMARD-LinearFold: Long Sequence RNA Design with Simulated Annealing

SIMARD-LinearFold: Long Sequence RNA Design with Simulated A...

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ieee Congress on Evolutionary Computation (ieee CEC)

作者： McBride, Ryan Tsang, Herbert H. Trinity Western Univ Appl Res Lab Langley BC Canada

ISBN: (纸本)9781728183923

RNA Design is an essential problem in bioinformatics to tailor RNA Sequences that guide our biology and medicine. While there are many RNA Design solutions in literature, the field has been limited by the high runtime to estimate how a candidate RNA sequence will fold, especially with "long sequences" over 750 "bases";as these folding algorithms are called millions of times per RNA Design problem, this limits RNA Design to shorter sequences despite the prevalence of long sequences in nature;for example, computationally designing COVID's over 20000 bases RNA sequence is not feasible with current RNA Design algorithms. To address this issue, we are the first work to integrate LinearFold, a faster RNA prediction algorithm used by Baidu to analyze COVID with a higher efficiency, into RNA Design. We compare the runtime and solution quality of our Applied Research Lab's Simulated Annealing solution (SIMARD) with and without LinearFold to design sequences thousands of bases long that timeout after weeks of runtime with current algorithms. We also survey challenges in terms of solution quality and adjusting the cooling schedule parameters. This work is thus a first step into RNA Design for longer RNA sequences.

关键词： bioinformatics RNA Design Simulated Annealing Evolutionary Algorithms computational intelligence

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Deep Active Inference Agent with Continuous Action Space

Deep Active Inference Agent with Continuous Action Space

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Cognitive Sciences, Genomics and bioinformatics (CSGB)

作者： Fedosov Nikita Alexey Voskoboynikov Artificial Intelligence Research Institute (AIRI) Moscow Russia Centre for Bioelectric Interfaces Higher School of Economics (HSE) Moscow Russia

Active inference is a promising and unifying framework for the intelligent agent behavior formal description, with ongoing intensive research of the means for sophisticated model implementations which would be able to challenge complex realistic environments. In this paper we introduce a previously used deep learning-based active inference model with a few adaptations into the action-perception architecture and propose our new generative adversarial network (GAN) -based approach to optimally sample from the continuous action space unlike from discrete one as it was done in most of related works. We show that the proposed model is able to learn and solve a simple but realistic environment, optimize its effect on the world in accordance with its own preferences, exhibiting the exploration behavior at the same time. We hope our work will help to make active inference agent design principles more flexible and generalizable.

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Cross-chain Sharing of Personal Health Records: Heterogeneous and Interoperable Blockchains

Cross-chain Sharing of Personal Health Records: Heterogeneou...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Yongyang Lv Xiaohong Li Yingwenbo Wang Kui Chen Zhe Hou Ruitao Feng College of Intelligence and Computing Tianjin University Tianjin China School of Information and Communication Technology Griffith University Brisbane Australia Faculty of Science and Engineering Southern Cross University Gold Coast Australia

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

With the widespread adoption of medical informatics, a wealth of valuable personal health records (PHR) has been generated. Concurrently, blockchain technology has enhanced the security of medical institutions. However, these institutions often function as isolated data silos, limiting the potential value of PHRs. As the demand for data sharing between hospitals on different blockchains grows, addressing the challenge of cross-chain data sharing becomes crucial. When sharing PHRs across blockchains, the limited storage and computational capabilities of medical Internet of Things (IoT) devices complicate the storage of large volumes of PHRs and the handling of complex calculations. Additionally, varying blockchain cryptosystems and the risk of internal attacks further complicate the cross-chain sharing of PHRs. This paper proposes a scheme for sharing PHRs across heterogeneous and interoperable blockchains. Medical IoT devices can encrypt and store real-time PHRs in an InterPlanetary File System, requiring only simple operations for data sharing. An enhanced proxy re-encryption(PRE) algorithm addresses the differences in blockchain cryptosystems. Multi-dimensional analysis demonstrates that this scheme offers robust security and excellent performance.

关键词： Performance evaluation Limiting Hospitals Internet of Medical Things Biomedical measurement Real-time systems Blockchains InterPlanetary File System bioinformatics

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Subgraph-based Self-Supervised Learning Framework for Enzymatic Reaction Feasibility Prediction

Subgraph-based Self-Supervised Learning Framework for Enzyma...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Feng Yang Juan Liu Qiang Zhang Zhihui Yang Jianghang Liu Guangsheng Wu Institute of Artificial Intelligence School of Computer Science Wuhan University China School of Mathematics and Computer Science Xinyu University China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Enzymatic Reaction feasibility prediction is used to determine whether the reaction generated by computational methods can actually occur, which can effectively reduce the complexity of synthetic pathway design. Existing methods often use SMILES or molecular fingerprints to represent molecules, resulting in a lack of molecular structural information. Although some GNNs have been leveraged to solve this problem, the complexity of the intra and inter-substructures interactions of molecules in microorganisms makes it difficult for traditional GNNs to accurately model it. To address these problems, we propose a subgraph-based self-supervised learning framework to predict the feasibility of enzymatic reactions. Specifically, we first propose a subgraph-based two-branch graph neural network. This network leverages the atom graph and substructure graph of a molecule to thoroughly capture its structural and semantic information. Besides, a subgraph interaction module is designed to facilitate the full integration of features. Subsequently, we propose a domain knowledge-guided self-supervised learning task, utilizing molecular fingerprints and substructures to capture the consistency between them effectively. The experimental results show that the proposed method outperforms existing state-ofthe-art methods significantly on all datasets.

关键词： Microorganisms Semantics Self-supervised learning Fingerprint recognition Graph neural networks Complexity theory bioinformatics

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