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arXiv 2024年

作者： Kommu, Sindhura Wang, Yizhi Wang, Yue Wang, Xuan Department of Computer Science Virginia Polytechnic Institute and State University BlacksburgVA United States Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University ArlingtonVA United States

Inferring gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data is a complex challenge that requires capturing the intricate relationships between genes and their regulatory interactions. In this study, we tackle this challenge by leveraging the single-cell BERTbased pre-trained transformer model (scBERT), trained on extensive unlabeled scRNA-seq data, to augment structured biological knowledge from existing GRNs. We introduce a novel joint graph learning approach scTransNet that combines the rich contextual representations learned by pretrained single-cell language models with the structured knowledge encoded in GRNs using graph neural networks (GNNs). By integrating these two modalities, our approach effectively reasons over both the gene expression level constraints provided by the scRNA-seq data and the structured biological knowledge inherent in GRNs. We evaluate scTransNet on human cell benchmark datasets from the BEELINE study with cell type-specific ground truth networks. The results demonstrate superior performance over current state-of-the-art baselines, offering a deeper understanding of cellular regulatory mechanisms. © 2024, CC BY.

关键词： Graph neural networks

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DRUGAGENT: EXPLAINABLE DRUG REPURPOSING AGENT WITH LARGE LANGUAGE MODEL-BASED REASONING

arXiv

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arXiv 2024年

作者： Inoue, Yoshitaka Song, Tianci Fu, Tianfan Department of Computer Science and Engineering University of Minnesota MinneapolisMN United States Computer Science Department Rensselaer Polytechnic Institute TroyNY United States

Drug repurposing, the identification of new therapeutic applications for existing drugs, offers a promising approach to accelerate drug development. This paper introduces a novel multi-agent framework that enhances the drug repurposing process by leveraging state-of-the-art machine learning techniques and integrating diverse knowledge sources. Our framework consists of three specialized agents: an AI Agent that develops robust drug-target interaction (DTI) models;a Knowledge Graph Agent that extracts DTIs from established databases such as DGIdb, DrugBank, CTD, and STITCH;and a Search Agent that engages with biomedical literature to annotate and verify computational predictions. By synthesizing outputs from these agents, our system harnesses a wide array of data sources to propose viable repurposing candidates. Preliminary results demonstrate the framework's potential to not only predict drug-disease interactions but also to reduce the time and costs associated with traditional drug discovery methods. Our approach outperforms existing methods in predicting drug repurposing potential while providing interpretable results. This work highlights the scalability of multi-agent systems in biomedical research and their capacity to drive innovation in drug repurposing. The framework presented in this paper offers a promising pathway toward more efficient and cost-effective drug discovery processes, paving the way for accelerated therapeutic developments. Code is available https://***/r/DrugRepurposeLLM-E0B5/. © 2024, CC BY.

关键词： Drug discovery

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MAVR: Multi-Functional Point Cloud Annotations Using Virtual Reality

MAVR: Multi-Functional Point Cloud Annotations Using Virtual...

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IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Xiao Zhang Zhanhong Huang Xinming Huang Department of Electrical & Computer Engineering and Department of Computer Science Worcester Polytechnic Institute Worcester MA USA

Learning-based point cloud perception methods rely on labeled data for training data-driven models, necessi-tating the development of precise and efficient tools for point cloud annotations. In this paper, we propose MAVR, a multi-functional annotation framework based on virtual reality (VR) technology, capable of accurately labeling point cloud data for diverse applications, including part segmentation and object detection. We begin by evaluating the user interface (UI) efficiency through interactive efficiency analysis. Subsequently, a comprehensive three-step process is introduced, which consists of pre-processing, point selection, and post-tagging. For 3D object part segmentation and scene perception, we propose two distinct tagging pipelines. Our experimental results on various datasets validate the effectiveness of MAVR in accurately annotating point clouds from different data sources within an immersive workspace.

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A Novel CNN-Based IoT System Architecture for Real-Time Detection and Prevention of Animal Intrusion in Farmland 4

A Novel CNN-Based IoT System Architecture for Real-Time Dete...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Ashokkumar, C. Kumar, M. Arun Krishnan, R. Santhana Priya, S. Mohanap Narayanan, K. Lakshmi Julie, E. Golden School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Chennai India PSNA College of Engineering and Technology Department of Information Technology Dindigul India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Scad Polytechnic College Department of Computer Science and Engineering Cheranmahadevi India Francis Xavier Engineering College Department of Electronics and Communication Engineering Tirunelveli India Anna University Regional Campus Department of Computer Science and Engineering Tirunelveli India

ISBN: (纸本)9798350300888

Animal intrusion is a significant challenge for farmers, causing extensive crop damage, human injuries, and substantial financial losses. Traditional animal movement monitoring and surveillance methods alone are insufficient to provide a permanent solution. To solve this problem, a novel system architecture that combines the latest convolutional neural network (CNN) algorithm and Internet of Things (IoT) technology is proposed. Our proposed system architecture integrates various components, including a Raspberry Pi as a central processing unit, cloud storage for efficient data management, and a GSM module for instant alert generation. To train the CNN algorithm, a comprehensive and diverse Animal Dataset consisting of various animal species commonly found in farmland areas is curated. The dataset encompasses a wide range of annotated images, enabling the CNN algorithm to accurately identify and classify animals. The Raspberry Pi serves as the core of the system, responsible for real-time image processing and analysis. Utilizing the power of the CNN algorithm, the Raspberry Pi processes the captured images from strategically placed surveillance cameras. When an animal intrusion is detected, the system promptly generates an alert via the integrated GSM module, providing immediate notifications to farmers and relevant authorities. Furthermore, the system leverages cloud storage to store and manage the collected data, facilitating easy access and retrieval for analysis and system improvement. This cloud-based approach enables scalability, allowing the system to handle large amounts of data efficiently. By integrating the CNN algorithm, IoT, Raspberry Pi, cloud storage, and GSM module, a comprehensive and robust framework for real-time detection and prevention of animal intrusion is provided. The system's ability to swiftly identify and alert farmers and authorities about potential threats minimizes crop damage, ensures human safety, and significantly reduces financial l

关键词： Signal detection

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Optimizing Crop Traits with ANN and Genetic Innovations for Sustainable Agriculture

Optimizing Crop Traits with ANN and Genetic Innovations for ...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Mahendran, Radha Danda, Ramanakar Reddy Anjum, Asma Kamde, Rashmi Abhay Firake, Bhagyashree Shailesh Wasnik, Jayshree Suhas Vels Institute of Science Technology and Advanced Studies Department of Bioinformatics Chennai India Architect Telangana Hyderabad India Hkbk College of Engineering Visvesvaraya Technological University Department of Computer Science and Engineering Bangalore India K J Somaiya Polytechnic Department of Electrical Engineering Maharashtra Mumbai India

ISBN: (纸本)9798331521394

Sustainable agriculture requires new strategies to tackle difficulties such as rising food demand, environmental pressures, and resource constraints. This study introduces a sophisticated approach that combines artificial neural networks (ANNs) with genetic advancements to enhance crop characteristics. The system attains enhanced accuracy (98.1%), F1 score (0.97), and reduced RMSE (0.012) by utilising high-dimensional genetic, phenotypic, and environmental data, surpassing baseline models. The technique integrates ANN predictions with gene-editing frameworks, guaranteeing accurate interventions and effective trait optimisation. The results demonstrate considerable improvements in forecast accuracy and resource efficiency compared to current techniques, creating a scalable, data-driven framework to promote sustainable agriculture practices worldwide. © 2025 IEEE.

关键词： Precision agriculture

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Evolution of Artificial Intelligence and Machine Learning in Betterment of Healthcare Services 1

Evolution of Artificial Intelligence and Machine Learning in...

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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Abu Eid, Aws Ismail Maula, Shabana Wagh, Aparna Vyas, Vibha Patil, Prakash Sridhar, P. Arab Open University Faculty of Computing Studies Amman Jordan Jaywant College of Engineering and Polytechnic Department of Computer Science Engineering Karad India Jaywant College of Management Karad India College of Engineering Pune Department of Electronics and Telecommunication Pune India Department of Radio-Diagnosis Karad India Institute of Aeronautical Engineering Department of Electrical and Electronics Engineering Hyderabad India

ISBN: (纸本)9798350360707

Artificial Intelligence usually comprises a system that is composed of hardware and software. Machine learning is a subset of artificial intelligence (AI). The study discovered that the environment and contemporary technology on the current healthcare services have created new demands, which has led to the formation of these five categories of research. By matching eligible patients with clinical trials, AI technologies in the healthcare industry may also be beneficial for medical research. There are numerous other fascinating uses for AI in the biomedical field. It is evident that AI is becoming more and more relevant in the field of biomedicine. AI technologies may be able to assist in addressing significant health issues, but its potential may be constrained by the caliber of the health data that is now available and by the fact that AI cannot exhibit certain human traits, like empathy. Numerous ethical and societal concerns are brought up by the use of AI, many of which are also brought up by the use of data and healthcare technologies more generally. © 2024 IEEE.

关键词： Adversarial machine learning

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Wearable Wellness: Depression Screening via Fitbit Data Collected During COVID-19 Pandemic

Wearable Wellness: Depression Screening via Fitbit Data Coll...

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IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： Nikola Grozdani America Muñoz Alexander Pietrick Ricardo Flores Avantika Shrestha Xingtong Guo Shichao Liu Elke A. Rundensteiner Computer Science Worcester Polytechnic Institute Worcester MA USA Computer Science/Information Systems National Louis University Chicago IL USA Computer Science/Data Science Worcester Polytechnic Institute Worcester MA USA Data Science Worcester Polytechnic Institute Worcester MA USA Architectural Engineering Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350309652

ISBN: (纸本)9798350309669

The COVID-19 pandemic has greatly increased depression among adolescents. The current depression diagnosis process requires significant patient effort and can be costly. Prior research through passively collected data has shown promising depression screening results but is limited by complex data collection and privacy concerns. In this research, we create multiple machine learning models to screen physiological data collected from Fitbit, a wearable biomarker, and depression screening surveys across 166 college students. The highest-scoring model on these physiological modalities achieved an F1-score of 0.92. Our research findings highlight the potential impact of digital technology development in current clinical practices.

关键词： COVID-19 Training Surveys Wearable Health Monitoring Systems Pandemics Biological system modeling Machine learning

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Artificial Intelligence: A Promising Prospect for Better Management of Diabetic Patients 5

Artificial Intelligence: A Promising Prospect for Better Man...

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5th IEEE International Conference on Artificial Intelligence and Big Data, ICAIBD 2022

作者： Hossin, Md Altab Islam, Md Safiqul Hassan, Sani Abubakar Ukwuoma, Chiagoziem Chima Nuri, Most. Miftahul Jannat Siddiquee, Md. Tofael Alam Chengdu University School of Innovation and Entrepreneurship Chengdu China Thakurgaon Polytechnic Institute Department of Computer Technology Thakurgaon Bangladesh University of Electronic Science and Technology of China School of Electronics Science and Engineering Chengdu China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Hajee Mohammad Danesh Science and Technology University Department of Electronic and Communication Engineering Dinajpur Bangladesh Mymensingh Polytechnic Institute Department of Computer Science and Engineering Mymensingh Bangladesh

ISBN: (纸本)9781665499132

Diabetic as an incurable chronic disease is increasing rapidly over time, and its impacts on other diseases are also striking. Indeed, science and technology have drastically developed, and it is also in the healthcare section. Getting diabetes education to help self-management is essential to help patients enhance their metabolic control and quality of life. Using artificial intelligence (AI) technologies, we can make significant progress in transforming available genetic data and clinical information into valuable knowledge. In disease education, given its potential benefits in encouraging individualization and full-course education intervention, AI would be extremely advantageous based on the unique situations of different individuals. The use of AI in different facets of diabetes education was explored based on collected data. © 2022 IEEE.

关键词： Artificial intelligence

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Smart Contracts for the CloudAnchor Platform 20th

Smart Contracts for the CloudAnchor Platform

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20th International Conference on Practical Applications of Agents and Multi-Agent Systems , PAAMS 2022

作者： Vasco, Eduardo Veloso, Bruno Malheiro, Benedita ISEP/IPP – School of Engineering Polytechnic Institute of Porto Porto Portugal UPT – Universidade Portucalense Porto Portugal INESC TEC – Institute for Systems and Computer Engineering Technology and Science Porto Portugal

ISBN: (纸本)9783031181917

CloudAnchor is a multi-agent brokerage platform for the negotiation of Infrastructure as a Service cloud resources between Small and Medium Sized Enterprises, acting either as providers or consumers. This project entails the research, design, and implementation of a smart contract solution to permanently record and manage contractual and behavioural stakeholder data on a blockchain network. Smart contracts enable safe contract code execution, increasing trust between parties and ensuring the integrity and traceability of the chained contents. The defined smart contracts represent the inter-business trustworthiness and Service Level Agreements established within the platform. CloudAnchor interacts with the blockchain network through a dedicated Application Programming Interface, which coordinates and optimises the submission of transactions. The performed tests indicate the success of this integration: (i) the number and value of negotiated resources remain identical;and (ii) the run-time increases due to the inherent latency of the blockchain operation. Nonetheless, the introduced latency does not affect the brokerage performance, proving to be an appropriate solution for reliable partner selection and contractual enforcement between untrusted parties. This novel approach stores all brokerage strategic knowledge in a distributed, decentralised, and immutable database. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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Robot-Assisted Wide-Area Photoacoustic System

Robot-Assisted Wide-Area Photoacoustic System

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IEEE Symposium (IUS) Ultrasonics

作者： Shang Gao Xihan Ma Haichong K. Zhang Department of Robotics Engineering Worcester Polytechnic Institute Worcester USA Department of Biomedical Engineering Worcester Polytechnic Institute Worcester USA Department of Computer Science Worcester Polytechnic Institute Worcester USA

Liver ischemia and reperfusion injury (IRI) is a vascular complication that can lead to hepatic functional impairment. IRI is relevant in liver transplantation and during liver surgery performed with intermittent vascular inflow occlusion. Detecting intraoperative liver oxygenation impairment and ischemia could help prevent IRI, which is challenging partially due to the multiple hepatic vascular inflows. We propose a robot-assisted wide-area photoacoustic (PA) imaging system to monitor the liver oxygenation level intraoperatively. PA imaging has been widely used for oxygenation detection in preclinical research. We used a robotic arm to actuate the 2D PA probe and scan across the liver surface. An accurate volumetric tomography can be reconstructed via registering 2D images to the recorded robot trajectory. An ex vivo porcine liver sample was scanned using the proposed system. The result suggested that the robot-assisted wide-area PA tomography has the potential to be incorporated into clinical practice to evaluate liver SO2 intraoperatively.

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