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ieee International conference on Consumer Electronics-Asia (ICCE-Asia)

作者： Van Thuy Hoang O-Joun Lee Dept. of Artificial Intelligence The Catholic University of Korea Bucheon Rep. of Korea

ISBN: (数字)9798331530839

ISBN: (纸本)9798331530846

Molecules are naturally represented as graphs, which benefit graph models in understanding molecular structures to capture complex relationships and interactions between atoms. However, the limitations of labeled molecules can significantly impact the graph model's ability to generalize different molecular properties and structures, leading to poor performance. Lately, pretraining Graph Neural Networks (GNNs) have emerged as a powerful tool for learning molecular representations, addressing the challenge due to the lack of labeled molec-ular data. This study introduces and examines the pretraining GNNs strategies in learning molecular structure in computational chemistry and biology. We first categorize recent pretraining existing studies into three main groups: node-level strategies, contrastive learning, and graph-level strategies. Furthermore, we also highlight recent studies that significantly enhance model interpretability and prediction accuracy. This comprehensive perspective on the current state and future prospects of GNNs underscores their pivotal role in accelerating drug discovery and material innovation through advanced AI-driven analysis.

关键词： Surveys Technological innovation computational chemistry Analytical models Accuracy computational modeling Biological system modeling Predictive models Graph neural networks Drug discovery

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Leveraging Frequency Domain Learning in 3D Vessel Segmentation

Leveraging Frequency Domain Learning in 3D Vessel Segmentati...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Xinyuan Wang Chengwei Pan Hongming Dai Gangming Zhao Jinpeng Li Xiao Zhang Yizhou Yu Institute of Artificial Intelligence Beihang University Beijing China Zhongguancun Laboratory Beijing China School Of Computing National University of Singapore Department of Computer Science University of Hong Kong Hong Kong China Deepwise AI Lab Beijing China HwaMei Hospital University of Chinese Academy of Sciences (UCAS) Ningbo China Ningbo Institute of Life and Health Industry UCAS Ningbo China School of Mathematical Sciences Beihang University Beijing China

Coronary microvascular disease constitutes a substantial risk to human health. Employing computer-aided analysis and diagnostic systems, medical professionals can intervene early in disease progression, with 3D vessel segmentation serving as a crucial component. Nevertheless, conventional U-Net architectures tend to yield incoherent and imprecise segmentation outcomes, particularly for small vessel structures. While models with attention mechanisms, such as Transformers and large convolutional kernels, demonstrate superior performance, their extensive computational demands during training and inference lead to increased time complexity. In this study, we leverage Fourier domain learning as a substitute for multi-scale convo-lutional kernels in 3D hierarchical segmentation models, which can reduce computational expenses while preserving global receptive fields within the network. Furthermore, a zero-parameter frequency domain fusion method is designed to improve the skip connections in U-Net architecture. Experimental results on a public dataset and an in-house dataset indicate that our novel Fourier transformation-based network achieves remarkable dice performance (84.37% on ASACA500 and 80.32% on ImageCAS) in tubular vessel segmentation tasks and substantially reduces computational requirements without compromising global receptive fields.

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From Big Data to Massive Data: Towards a Massive Data Storage Solution for the Internet of Senses

From Big Data to Massive Data: Towards a Massive Data Storag...

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International conference on Big Data and Artificial intelligence (BDAI)

作者： Amjad Fayoumi Chanapat Orachorn Xiao Shi Management Science Lancaster University Lancaster UK

ISBN: (数字)9798350352009

ISBN: (纸本)9798350352016

The increased maturity of current digitalization techniques expands the scope of practical deployments in various fields, including the precipitation of “digital biology” as a novel and highly important new field of research. This research area specializes in the convergence of various digital technologies and investigations into biology, offering the possibility to produce new comprehension of and applications for living systems by leveraging frameworks of digital technology. The enhanced scope of current capacities in computation facilitates simulated models of multifaceted biological artefacts and novel ways to gather, analyze, and interpret data, moving far beyond the primitive scope of legacy solutions in this field. This paper explores potential “massive data” databases, developing a conceptual model to move beyond the existing “big data” paradigm.

关键词： Visualization Precipitation computational modeling Biological system modeling Memory Big Data Three-dimensional printing Safety Artificial intelligence Standards

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Discovering a Biomechanical Prediction Model for Adolescent Idiopathic Scoliosis by Combining PINN with State Space Models

Discovering a Biomechanical Prediction Model for Adolescent ...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Rui Lin Junhua Zhang Department of Electronic Engineering Yunnan University

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Biomechanics are crucial for diagnosing and analyzing Adolescent Idiopathic Scoliosis (AIS) in CT images. Recent research suggests that spine biomechanics follow equilibrium equations, but the exact mathematical model and parameters are not fully understood. In this study, we use equilibrium equations and state space models to develop a computational biomechanics framework. Specifically, we create a model combining biomechanical analysis based on equilibrium equations with state-space image segmentation. First, we propose PM-UNet, a segmentation method using a state-space model to capture bone structure in CT images. Then, Physical Information Neural Networks (PINN) with equilibrium equations, using control matrices α and β, calculate biomechanical properties. We validate the model by comparing results with FEM calculations. The experimental results show that the PM-UNet and PINN framework effectively extracts mechanical features and calculates biomechanical properties, providing a foundation for advanced analysis and improving surgical planning and brace design for AIS.

关键词： Biomechanics Analytical models Image segmentation Biological system modeling Computed tomography Scoliosis Predictive models Feature extraction Mathematical models Artificial intelligence

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Multi-scale Cardiac Modeling for ECG Forward Problem Based on Finite Element Method

Multi-scale Cardiac Modeling for ECG Forward Problem Based o...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Luyao Chen Yacong Li Mengmeng Li Xiaodong Lv Zhuowei Yang Henggui Zhang Beijing Academy of Artificial Intelligence Beijing China Department of Cardiology Beijing Anzhen Hospital Capital Medical University Beijing China Department of Neurology Xuanwu Hospital Capital Medical University Beijing China School of Mechanical Engineering Hubei University of Technology Wuhan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The forward problem of electrocardiography is essential for understanding the mechanisms underlying cardiac electrical activity, validating and optimizing the inverse problem, and advancing personalized medicine through patient-specific modeling. Comprehensive computation of the forward problem requires multi-scale modeling and the execution of complex computational processes. The challenges associated with constructing, solving, and validating multi-scale models arise from the need to accurately represent biophysical processes by establishing precise interactions across various spatial and temporal scales. In this study, we aimed to integrate single-cell modeling, which captures the electrophysiological activity of cardiac cells, with finite element methods (FEM), which simulates the propagation of electrical signals from cardiac tissue to the body surface, to achieve a comprehensive simulation of the electrocardiogram (ECG) forward problem. We developed a multi-scale tissue structure model, incorporating it with single-cell models and FEM to compute the distribution of body surface potentials. Additionally, we simulated a 12-lead ECG by appropriately positioning electrodes. The results demonstrate that our proposed approach effectively simulates the QRS and T-wave components of the ECG and accurately captures variations in sub-cellular parameters under both normal and abnormal conditions as reflected in the ECG.

关键词： Electrodes Inverse problems computational modeling Biological system modeling Precision medicine Electrocardiography Electrophysiology Finite element analysis Surface treatment Periodic structures

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Formation Routes of CO from O(1D)+Toluene: A computational Study 22nd

Formation Routes of CO from O(1D)+Toluene: A Computational S...

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22nd International conference on computational Science and Its Applications , ICCSA 2022

作者： Rosi, Marzio Casavecchia, Piergiorgio Balucani, Nadia Recio, Pedro Caracciolo, Adriana Skouteris, Dimitrios Cavallotti, Carlo Department of Civil and Environmental Engineering University of Perugia Perugia06125 Italy Department of Chemistry Biology and Biotechnologies University of di Perugia Perugia06123 Italy Ann and H.J. Smead Department of Aerospace Engineering Sciences University of Colorado BoulderCO80303 United States Master-Tec Perugia06128 Italy Department of Chemistry Materials and Chemical Engineering Politecnico di Milano Milan20131 Italy

ISBN: (纸本)9783031105616

The interaction between oxygen atoms in their first electronically excited state 1D with toluene has been characterized by electronic structure calculations. We focused our attention, in particular, on the different pathways leading to the formation of CO. Six different reaction channels have been investigated. Our results suggest that, while for accurate energies high level calculations, as CCSD(T), are necessary, in particular when strong correlation effects are present, for semi-quantitative results DFT methods are adequate and provide information useful when larger systems than toluene as polycyclic aromatic hydrocarbons are under investigation. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： computational chemistry

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KenDTI: An Ensemble Model for Predicting Drug-Target Interaction by Integrating Multi-Source Information

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第4期18卷 1305-1314页

作者： Yu, Zhimiao Lu, Jiarui Jin, Yuan Yang, Yang Shanghai Jiao Tong Univ Sch Elect Informat & Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Chem & Chem Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Brain Comp & Machine Intelligence Dept Comp Sci & Engn Key Lab Shanghai Educ Commiss Intelligent Interac Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Brain Sci & Technol Res Ctr Shanghai 200240 Peoples R China

The identification of drug-target interactions (DTIs) is an essential step in the process of drug discovery. As experimental validation suffers from high cost and low success rate, various computational models have been exploited to infer potential DTIs. The performance of DTI prediction depends heavily on the features extracted from drugs and target proteins. The existing predictors vary in input information and each has its own advantages. Therefore, combining the advantages of individual models and generating high-quality representations for drug-target pairs are effective ways to improve the performance of DTI prediction. In this study, we exploit both biochemical characteristics of drugs via network integration and molecular sequences via word embeddings, then we develop an ensemble model, KenDTI, based on two types of methods, i.e., network-based and classification-based. We assess the performance of KenDTI on two large-scale datasets, The experimental results show that KenDTI outperforms the state-of-the-art DTI predictors by a large margin. Moreover, KenDTI is robust against missing data in input networks and lack of prior knowledge. It is able to predict for drug-candidate chemical compounds with scarce information.

关键词： Drugs Proteins Feature extraction Training Chemicals Predictive models Prediction algorithms Drug-target interaction convolutional neural networks word embeddings network integration ensemble classifier

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WIP: Identifying Tutorial Affordances for Interdisciplinary Learning Environments

WIP: Identifying Tutorial Affordances for Interdisciplinary ...

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Frontiers in Education (FIE) conference

作者： Hannah Kim Sergei L. Kosakovsky Pond Stephen MacNeil Department of Biology Temple University Philadelphia PA USA Dept. of Computer & Information Sciences Temple University Philadelphia PA USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This work-in-progress research paper explores the effectiveness of tutorials in interdisciplinary learning environments, specifically focusing on bioinformatics. Tutorials are typically designed for a single audience, but our study aims to uncover how they function in contexts where learners have diverse backgrounds. With the rise of interdisciplinary learning, the importance of learning materials that accommodate diverse learner needs has become evident. We chose bioinformatics as our context because it involves at least two distinct user groups: those with computational backgrounds and those with biological backgrounds. The goal of our research is to better understand current bioinformatics software tutorial designs and assess them in the conceptual framework of interdisciplinarity. We conducted a content analysis of 22 representative bioinformatics software tutorials to identify design patterns and understand their strengths and limitations. We found common codes in the representative tutorials and synthesized them into ten themes. Our assessment shows degrees to which current bioinformatics software tutorials fulfill interdisciplinarity.

关键词： Content management Codes Affordances Focusing Tutorials Software bioinformatics Biological information theory

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Identify ncRNA Subcellular Localization via Graph Regularized k-Local Hyperplane Distance Nearest Neighbor Model on Multi-Kernel Learning

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第6期19卷 3517-3529页

作者： Zhou, Haohao Wang, Hao Tang, Jijun Ding, Yijie Guo, Fei Tianjin Univ Coll Intelligence & Comp Sch Comp Sci & Technol Tianjin 300072 Peoples R China Univ South Carolina Dept Comp Sci & Engn Columbia SC 29208 USA Univ Elect Sci & Technol China Yangtze Delta Region Inst Quzhou Quzhou 324000 Zhejiang Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410083 Hunan Peoples R China

Non-coding RNAs (ncRNAs) are a type of RNAs which are not used to encode protein sequences. Emerging evidence shows that lots of ncRNAs may participate in many biological processes and must be widely involved in many types of cancers. Therefore, understanding their functionality is of great importance. Similar to proteins, various functions of ncRNAs relies on their subcellular localizations. Traditional high-throughput methods in wet-lab to identify subcellular localization is time-consuming and costly. In this paper, we propose a novel computational method based on multi-kernel learning to identify multi-label ncRNA subcellular localizations, via graph regularized k-local hyperplane distance nearest neighbor algorithm. First, we construct six types of sequencebased feature descriptors and select important feature vectors. Then, we build a multi-kernel learning model with Hilbert-Schmidt independence criterion (HSIC) to obtain optimal weights for vairous features. Furthermore, we propose the graph regularized k-local hyperplane distance nearest neighbor algorithm (GHKNN) as a binary classification model for detecting one kind of non-coding RNA subcellular localization. Finally, we apply One-vs-Rest strategy to decompose multi-label problem of non-coding RNA subcellular localizations. Our method achieves excellent performance on three ncRNA datasets and three human ncRNA datasets, and outperforms other outstanding machine learning methods. Comparing to existing method, our model also performs well especially on small datasets. We expect that this model will be useful for the prediction of subcellular localization and the study of important functional mechanisms of ncRNAs. Furthermore, we establish user-friendly web server (http://***/) with the implementation of our method, which can be easily used by most experimental scientists.

关键词： RNA Location awareness Feature extraction Proteins Kernel Databases Biological information theory Non-coding RNA subcellular localization multi-label classification multi-kernel learning k-local hyperplane distance nearest neighbor

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Scheme and Construction of a Smart Vacuum Cleaner Robot 3

Scheme and Construction of a Smart Vacuum Cleaner Robot

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3rd International conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Hasan, Md. Ibrahim Zeyad, Mohammad Ahmed, S.M. Masum Tasrif Anubhove, Md. Sadik Hossain, Eftakhar Hasan, Sayeed Mahmud, Dewan Mahnaaz Advanced Bioinformatics Computational Biology and Data Science Laboratory Bangladesh Energy and Technology Research Division Chattogram4226 Bangladesh Erasmus+ Joint MSC in Smart Cities and Communities Heriot-Watt University Edinburgh United Kingdom Erasmus+ Joint MSC in Smart Cities and Communities Faculty of Engineering Mons Belgium

ISBN: (纸本)9781665494007

A smart vacuum robot is a remarkable technological achievement that makes cleaning more efficient, faster, and easier. According to this study, the smart vacuum cleaning robot can be operated manually, automatically, mopped/swept, vacuumed/vacuumed, and observed from 360°. A design of a smart vacuum-cleaning robot is proposed with different characteristics. The Arduino IDE and Proteus Design Suite 8 Professional software were employed for analytical analysis, with a microcontroller serving as the primary control component. The hardware implementation comprises a microcontroller, ultrasonic sensor, and light-dependent resistor (LDR) where the ultrasonic sensor identifies obstacles (trash). Therefore, the LDR emits light and sends the microcontroller's signal to the microprocessor. All the sensors in the proposed system operated flawlessly during the experiment. Moreover, all the sensors were performing perfectly. With additional features of this robot, it can continue its operation during darkness. The proposed robot is equipped with a number of creative and intelligent features that allow it to provide the maximum possible support to employees, housewives, and ordinary people. This work presents a new notion of an advanced cleaning robot employing cutting-edge technology that can work independently in automated and regular situations and dramatically improve the cleaning system for humanity. © 2023 ieee.

关键词： Microcontrollers

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