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Approximation of a compound-exchanging cell by a Dirac point

IFAC-PapersOnLine 2025年第1期59卷 73-78页

作者： Xiao Yang Qiyao Peng Sander C. Hille Mathematical Institute Leiden University Leiden the Netherlands Bioinformatics Computer Science Department Vrije Universiteit Amsterdam Amsterdam the Netherlands

Communication between single cells or higher organisms by means of diffusive compounds is an important phenomenon in biological systems. Modelling therefore often occurs, most straightforwardly by a diffusion equation with suitable flux boundary conditions at the cell boundaries. Such a model will become computationally inefficient and analytically complex when there are many cells, even more so when they are moving. We propose to consider instead a point source model. Each cell is virtually reduced to a point and appears in the diffusion equation for the compound on the full spatial domain as a singular reaction term in the form of a Dirac delta ‘function’ (measure) located at the cell’s centre. In this model, it has an amplitude that is a non-local function of the concentration of compound on the (now virtual) cell boundary. We prove the well-posedness of this particular parabolic problem with non-local and singular reaction term in suitable Sobolev spaces. We show for a square bounded domain and for the plane that the solution cannot be H 1 -smooth at the Dirac point. Further, we show a preliminary numerical comparison between the solutions to the two models that suggests that the two models are highly comparable to each other.

关键词： Cell communication Diffusion equation Numerical analysis Heat kernel Dirac measure Well-posedness

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Advanced deep learning approach for enhancing crop disease detection in agriculture using hyperspectral imaging

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Journal of Intelligent and Fuzzy Systems 2024年第2期46卷 3281-3294页

作者： Zekrifa, Djabeur Mohamed Seifeddine Lamani, Dharmanna Chaitanya, Gogineni Krishna Kanimozhi, K.V. Saraswat, Akash Sugumar, D. Vetrithangam, D. Koshariya, Ashok Kumar Manjunath, Manthur Sreeramulu Rajaram, A. Department of Bioinformatics Higher School of Food Science and Agri-Food Industry Ahmed Hamidouche Av Oued Smar Algiers Algeria Department of Computer Science & Information Technology School of Computing and Information Technology REVA University Rukmini Knowledge Park Kattigenahalli Karnataka Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Department of Computer Science Engineering Dr. K. N. Modi University Jaipur Rajasthan Newai India Department of Electrical and Electronics Engineering Karunya Institute of Technology and Sciences Deemed to be University Coimbatore India Department of Computer Science and Engineering University Institute of Engineering Chandigarh University Punjab Mohali India Department of Plant Pathology School of Agriculture Lovely Professional University Punjab Phagwara India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Tamilnadu Nagapattinam India

Crop diseases pose significant challenges to global food security and agricultural sustainability. Timely and accurate disease detection is crucial for effective disease management and minimizing crop losses. In recent years, hyperspectral imaging has emerged as a promising technology for non-destructive and early disease detection in crops. This research paper presents an advanced deep learning approach for enhancing crop disease detection using hyperspectral imaging. The primary objective is to propose a hybrid Autoencoder-Generative Adversarial Network (AE-GAN) model that effectively extracts meaningful features from hyperspectral images and addresses the limitations of existing techniques. The hybrid AE-GAN model combines the strengths of the Autoencoder for feature extraction and the Generative Adversarial Network for synthetic sample generation. Through extensive evaluation, the proposed model outperforms existing techniques, achieving exceptional accuracy in crop disease detection. The results demonstrate the superiority of the hybrid AE-GAN model, offering substantial advantages in terms of feature extraction, synthetic sample generation, and utilization of spatial and spectral information. The proposed model’s contributions to sustainable agriculture and global food security make it a valuable tool for advancing agricultural practices and enhancing crop health monitoring. With its promising implications, the hybrid AE-GAN model represents a significant advancement in crop disease detection, paving the way for a more resilient and food-secure future. © 2024 – IOS Press. All rights reserved.

关键词： Generative adversarial networks

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MassSpecGym: A benchmark for the discovery and identification of molecules 38

MassSpecGym: A benchmark for the discovery and identificatio...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Bushuiev, Roman Bushuiev, Anton de Jonge, Niek F. Young, Adamo Kretschmer, Fleming Samusevich, Raman Heirman, Janne Wang, Fei Zhang, Luke Dührkop, Kai Ludwig, Marcus Haupt, Nils A. Kalia, Apurva Brungs, Corinna Schmid, Robin Greiner, Russell Wang, Bo Wishart, David S. Liu, Li-Ping Rousu, Juho Bittremieux, Wout Rost, Hannes Mak, Tytus D. Hassoun, Soha Huber, Florian van der Hooft, Justin J.J. Stravs, Michael A. Böcker, Sebastian Sivic, Josef Pluskal, Tomáš Institute of Organic Chemistry and Biochemistry The Czech Academy of Sciences Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Republic Bioinformatics Group Wageningen University & Research Netherlands Department of Computer Science University of Toronto Canada Bioinformatics Institute for Computer Science Friedrich Schiller University Jena Germany Department of Computer Science University of Antwerp Belgium Department of computing science University of Alberta Canada Alberta Machine Intelligence Institute Canada Department of Molecular Genetics University of Toronto Canada Bright Giant GmbH Department of Computer Science Tufts University United States Department of Biological Sciences University of Alberta Canada Department of Computer Science Aalto University Finland Mass Spectrometry Data Center National Institute of Standards and Technology United States Department of Chemical and Biological Engineering Tufts University United States Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Germany Department of Biochemistry University of Johannesburg South Africa Eawag Swiss Federal Institute of Aquatic Science and Technology Switzerland

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym - the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym. © 2024 Neural information processing systems foundation. All rights reserved.

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A Transformer-Based Model Trained on Large Scale Claims Data for Prediction of Severe COVID-19 Disease Progression

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IEEE Journal of Biomedical and Health Informatics 2023年第9期27卷 4548-4558页

作者： Lentzen, Manuel Linden, Thomas Veeranki, Sai Madan, Sumit Kramer, Diether Leodolter, Werner Frohlich, Holger Schloss Birlinghoven Department of Bioinformatics Sankt Augustin53757 Germany Bonn53115 Germany Graz8010 Austria Technical University Institute of Neural Engineering Graz8010 Austria AIT Austrian Institute of Technology Graz8010 Austria University of Bonn Institute of Computer Science Bonn53115 Germany

In situations like the COVID-19 pandemic, healthcare systems are under enormous pressure as they can rapidly collapse under the burden of the crisis. Machine learning (ML) based risk models could lift the burden by identifying patients with a high risk of severe disease progression. Electronic Health Records (EHRs) provide crucial sources of information to develop these models because they rely on routinely collected healthcare data. However, EHR data is challenging for training ML models because it contains irregularly timestamped diagnosis, prescription, and procedure codes. For such data, transformer-based models are promising. We extended the previously published Med-BERT model by including age, sex, medications, quantitative clinical measures, and state information. After pre-training on approximately 988 million EHRs from 3.5 million patients, we developed models to predict Acute Respiratory Manifestations (ARM) risk using the medical history of 80,211 COVID-19 patients. Compared to Random Forests, XGBoost, and RETAIN, our transformer-based models more accurately forecast the risk of developing ARM after COVID-19 infection. We used Integrated Gradients and Bayesian networks to understand the link between the essential features of our model. Finally, we evaluated adapting our model to Austrian in-patient data. Our study highlights the promise of predictive transformer-based models for precision medicine. © 2013 IEEE.

关键词： COVID-19

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Medical image registration and its application in retinal images:a review

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 142-164页

作者： Qiushi Nie Xiaoqing Zhang Yan Hu Mingdao Gong Jiang Liu Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Singapore Eye Research Institute Singapore 169856Singapore State Key Laboratory of Ophthalmology Optometry and Visual ScienceEye HospitalWenzhou Medical UniversityWenzhou 325027China

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse informa-tion of images,which may be captured under different times,angles,or *** several surveys have reviewed the development of medical image registration,they have not systematically summarized the existing med-ical image registration *** this end,a comprehensive review of these methods is provided from traditional and deep-learning-based perspectives,aiming to help audiences quickly understand the development of medical image *** particular,we review recent advances in retinal image registration,which has not attracted much *** addition,current challenges in retinal image registration are discussed and insights and prospects for future research provided.

关键词： computer-aided diagnosis Medical image registration Deep learning Generative model Transformer Retina

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Migratable AI Systems for Tailoring User Experience through Multimodal Affective-Cognitive State Analysis

Migratable AI Systems for Tailoring User Experience through ...

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2023 International Conference of the Italian Association for Artificial Intelligence Doctoral Consortium, AIxIA-DC 2023

作者： La Monica, Ludovica Institute of Cognitive Science and Technology National Research Council Rome00185 Italy Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome00128 Italy

In the era of digital technologies permeating numerous aspects of society, the development of intelligent artificial agents endowed with autonomy, social capabilities, reactivity, and proactivity has become a pivotal innovation. Traditionally, these agents were confined to singular forms, known as embodiments, which dictated their capabilities and the environments in which they operated. To transcend these limitations and enable agents to expand their capabilities, the concept of agent migration has emerged, allowing them to seamlessly transition between different embodiments. My research program delves into the realm of agent migration, uncovering its challenges and potential opportunities. The central objective is to leverage this technology to deepen our understanding of human affective and cognitive states across various contexts. Specifically, my aim is to develop a migratory agent architecture capable of recognizing and characterizing individuals’ emotional experiences using different sets of capabilities tied to distinct embodiments. In summary, the intent of this research is to harness migratable AI to promote the technical advantages brought about by agent migration and gain insights into how to utilize the acquisition of additional capabilities resulting from migration to craft a more comprehensive and potentially enduring architecture, to be applied in the realm of affective computing. © 2022 Copyright for this paper by its authors.

关键词： Autonomous agents

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Exploring Latent Space for Generating Peptide Analogs Using Protein Language Models

Exploring Latent Space for Generating Peptide Analogs Using ...

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2024 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2024

作者： Liang, Po-Yu Huang, Xueting Duran, Tibo Wiemer, Andrew J. Bai, Jun University of Cincinnati Department of Computer Science OH United States University of Connecticut Department of Pharmaceutical Sciences StorrsCT United States Institute for Systems Genomics University of Connecticut Department of Pharmaceutical Sciences StorrsCT United States

ISBN: (纸本)9798350386226

Generating peptides with desired properties is crucial for drug discovery and biotechnology. Traditional sequence-based and structure-based methods often require extensive datasets, which limits their effectiveness. In this study, we proposed a novel method that utilized autoencoder shaped models to explore the protein embedding space, and generate novel peptide analogs by leveraging protein language models. The proposed method requires only a single sequence of interest, avoiding the need for large datasets. Our results show significant improvements over baseline models in similarity indicators of peptide structures, descriptors and bioactivities. The proposed method validated through Molecular Dynamics simulations on TIGIT inhibitors, demonstrates that our method produces peptide analogs with similar yet distinct properties, highlighting its potential to enhance peptide screening processes. Future work will focus on validating this approach in wet lab experiments. The full paper could be found at: https://***/abs/2408.08341 © 2024 IEEE.

关键词： deep learning peptide sequence generation protein language models

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Novel Plant Leaf Disease Detection Approach using Hybrid Deep Learning Strategy 5

Novel Plant Leaf Disease Detection Approach using Hybrid Dee...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Vamshi, Jalla Tanwar, Shashi Thatipudi, Jayendra Gopal Ponsangeetha, A. Mukherjee, Saptarshi Mandhare, Jagdish B. School of Agriculture Sr University Telangana Warangal India G D Goenka University Department- Computer Science and Engineering Haryana Gurugram India University of North Texas Dept. of Computer Science United States Velammal Institute of Technology Department of Computer Science and Engineering Panchetti India Goldsmith Institute of Advanced Research Department-Mtech Bioinformatics Giar West Bengal Kolkata India Bharati Vidyapeeth College of Engineering Department of Instrumentation Navi Mumbai India

ISBN: (纸本)9798350385649

The early detection and identification of plant diseases are pivotal for precision agriculture, aiming to mitigate crop losses and optimize yields. This study presents a novel approach to plant disease identification, leveraging Deep Learning (DL) techniques with a focus on transfer learning. Traditional methods often rely on complex image enhancement, disease region segmentation, and feature extraction, whereas our approach employs Convolutional Neural Networks (CNNs) for efficient and accurate disease detection. To enhance accessibility and diagnostic capabilities, we implement Hybrid DL Strategy, which consists of EfficientNetB0 and MobileNetV2 model, tailored for lightweight applications suitable for smartphone implementation. Efficient NetB0 model, which considers depth, width, and resolution during convolution, enhancing the model's capacity to capture intricate features critical for accurate disease diagnosis. The overall accuracy of proposed method is 98.44%, surpassing CNN, ResNet, and AlexNet. This high accuracy underscores the effectiveness of the proposed model in correctly classifying both diseased and healthy plant leaves. © 2024 IEEE.

关键词： Convolutional neural networks

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Explore the Protein Targets of Cinnamomum Zeylanicum's Phytoconstituents Against Pathogenic Staphylococcus Aureus: Gc-Ms Profiling, Molecular Docking, Pharmacophore, and Pathway Reconstruction

SSRN

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SSRN 2024年

作者： Prabha, Sarit Warkare, Sudeesh Ranga, Akash Pandey, Khushhali M. Bioinformatics and Center for Genomics Lab Department of Biological Science and Engineering Maulana Azad National Institute of Technology Bhopal India

Staphylococcus aureus is a leading cause of severe infections, including sepsis, due to its diverse virulence factors. Alternative treatments are essential because of the side effects and resistance linked to conventional therapies. Medicinal plants like Cinnamomum zeylanicum, rich in secondary metabolites, are potential alternatives. This study explores the antimicrobial potential and target protein interaction pathway of C. zeylanicum bark against pathogenic S. aureus. Methanolic extraction of the C. zeylanicum bark was followed by qualitative and quantitative screening and GC-MS analysis to detect phytoconstituents. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were evaluated against pathogenic S. aureus;additionally, virulence was assessed by PCR of plc, icaA, and nuc genes. In silico analysis included molecular docking, pharmacophore, and ADMET analysis of the identified compounds against seven S. aureus target proteins. PCA of docking was conducted and generated a tetrapartite network and target protein interaction pathway using a string and KEGG database. The methanolic crude extract, containing 19 phytoconstituents, showed a significant MIC and MBC value as 5mg/mL. In silico analysis revealed that phytoconstituents alfa-copaene, alpha-muurolene, and tau-cadinol are potential inhibitors, displaying favourable binding interactions with proteins D-alanine-D-alanine ligase, dihydrofolate reductase, peptide deformylase, and penicillin-binding protein 2. Protein network and pathway analysis suggest that inhibiting the target proteins can disrupt processes like cell wall formation, DNA replication, and protein synthesis. C. zeylanicum contains phenol and flavonoid-derived phytoconstituents with the potential to inhibit pathogenic S. aureus. Further studies are needed to fully evaluate its therapeutic potential for infection treatment. © 2024, The Authors. All rights reserved.

关键词： Molecular docking

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Liver Disease Prediction using various Image Processing Techniques and Machine Learning Algorithms 1

Liver Disease Prediction using various Image Processing Tech...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Saravanan, M.S. Prasad, Siva Rama Krishna Dhanalakshmi, S. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Guru Nanak Institutions Technical Campus Department of It Ibrahimpatnam India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Bioinformatics Chennai India

ISBN: (纸本)9798350352931

Predicting liver disease usually entails estimating a person's chance of contracting ailments related to the liver using a variety of techniques. The prediction is made using the Novel Decision Tree algorithm and the Naive Bayes algorithm. From CHAOS Grand challenge webpage, data collected for liver images. Decision tree classifiers are compared to Naive Bayes classifiers for their accuracy and precision in diagnosing liver disease in patients. When it comes to predicting liver illness, the Naive Bayes classifier is 75.15 %accurate, whereas the Novel Decision tree classifier is 80.03 %accurate. 0.022 is the most significant number. g-power is used to calculate the sample size for two group at 16. The 95 percent alpha value for the g power is 0.05. The prediction of liver disease at early stage achieved by comparing Novel Decision Tree to Naive Bayes, Novel Decision Tree outperforms the latter in terms of precision and accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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