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Artificial intelligence in cancer target identification and drug discovery

Signal Transduction and Targeted Therapy 2022年第6期7卷 1951-1974页

作者： Yujie You Xin Lai Yi Pan Huiru Zheng Julio Vera Suran Liu Senyi Deng Le Zhang College of Computer Science Sichuan UniversityChengdu 610065China Laboratory of Systems Tumor Immunology Friedrich-Alexander-Universität Erlangen-Nürnberg(FAU)and Universitätsklinikum ErlangenErlangen 91052Germany Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesRoom D5131068 Xueyuan AvenueShenzhen University TownShenzhen 518055China School of Computing Ulster UniversityBelfast BT151EDUK Institute of Thoracic Oncology Department of Thoracic SurgeryWest China HospitalSichuan UniversityChengdu 610065China Key Laboratory of Systems Biology Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesChinese Academy of SciencesHangzhou 310024China Key Laboratory of Systems Health Science of Zhejiang Province Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as ***,we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover ***,we describe the scope of artificial intelligence biology analysis for novel anticancer target ***,we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence ***,we showcase the applications of artificial intelligence approaches in cancer target identification and drug *** together,the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer,thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

关键词： drugs cancer drug

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud computing System 14

HealthEdge: A Machine Learning-Based Smart Healthcare Framew...

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14th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Hennebelle, Alain Materwala, Huned Ismail, Leila Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR. © 2023 Elsevier B.V.. All rights reserved.

关键词： Edge computing

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation

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Procedia computer science 2023年 220卷 339-347页

作者： Leila Ismail Muhammad Danish Waseem Clouds and Distributed Computing and Systems (CLOUDS) Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia. Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE. National Water and Energy Center United Arab Emirates University UAE.

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately.

关键词： computer Vision Deep Learning Image Processing Machine Learning Pain Detection Patient-Centric Smart healthcare eHealth

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GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability

arXiv

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arXiv 2024年

作者： Luo, Zihan Song, Xiran Huang, Hong Lian, Jianxun Zhang, Chenhao Jiang, Jinqi Xie, Xing Huazhong University of Science and Technology Wuhan China Microsoft Research Asia Beijing China The National Engineering Research Center for Big Data Technology and System Service Computing Technology and Systems Laboratory Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

Evaluating and enhancing the general capabilities of large language models (LLMs) has been an important research topic. Graph is a common data structure in the real world, and understanding graph data is a crucial part for advancing general intelligence. To evaluate and enhance the graph understanding abilities of LLMs, in this paper, we propose a benchmark named GraphInstruct, which comprehensively includes 21 classical graph reasoning tasks, providing diverse graph generation pipelines and detailed reasoning steps. Based on GraphInstruct, we further construct GraphLM through efficient instruction-tuning, which shows prominent graph understanding capability. In order to enhance the LLM with graph reasoning capability as well, we propose a step mask training strategy, and construct a model named GraphLM+. As one of the pioneering efforts to enhance the graph understanding and reasoning abilities of LLMs, extensive experiments have demonstrated the superiority of GraphLM and GraphLM+ over other LLMs. We look forward to more researchers exploring the potential of LLMs in the graph data mining domain through GraphInstruct. Our code for generating GraphInstruct is released publicly at: https://***/CGCL-codes/GraphInstruct. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational linguistics

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csl-MTFL: Multi-task Feature Learning with Joint Correlation Structure Learning for Alzheimer’s Disease Cognitive Performance Prediction 9th

csl-MTFL: Multi-task Feature Learning with Joint Correlatio...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Liang, Wei Zhang, Kai Cao, Peng Liu, Xiaoli Yang, Jinzhu Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China DAMO Academy Alibaba Group Hangzhou China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada

ISBN: (纸本)9783031466700

Alzheimer’s disease (AD) is a common chronic neurodegenerative disease and the accurate prediction of the clinical cognitive performance is important for diagnosis and treatment. Recently, multi-task feature learning (MTFL) methods with sparsity-inducing regularization have been widely investigated on cognitive performance prediction tasks. Although they have proved to achieve improved performance compared with single-task learning, the major challenges are still not fully resolved. They involve how to capture the non-linear correlation among the tasks or features, and how to introduce the learned correlation for guiding the MTFL learning. To resolve these challenges, we introduce a correlation structure learning method through self-attention learning and sequence learning for jointly capturing the complicated but more flexible relationship for features and tasks, respectively. Moreover, we develop a dual graph regularization to encode the inherent correlation and an efficient optimization algorithm for solving the nonsmooth objective function. Extensive results on the ADNI dataset demonstrate that the proposed joint training framework outperforms existing methods and achieves state-of-the-art prediction performance of AD. Specifically, the proposed algorithm achieves an nMSE (normalized Mean Squared Error)/wR (weighted R-value) of 3.808/0.438, obtaining a relative improvement of 3.84 % /7.35 % compared with the MTFL method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Neurodegenerative diseases

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Towards Time-Variant-Aware Link Prediction in Dynamic Graph Through Self-supervised Learning 9th

Towards Time-Variant-Aware Link Prediction in Dynamic Graph...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Wen, Guangqi Cao, Peng Jin, Zhiyong Song, Ruoxian Liu, Xiaoli Yang, Jinzhu Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China DAMO Academy Alibaba Group Hangzhou China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada

ISBN: (纸本)9783031466731

Dynamic graph link prediction is a challenging problem because the graph topology and node attributes vary at different times. A purely supervised learning scheme for the dynamic graph data usually leads to poor generalization due to insufficient supervision. As a promising solution, self-supervised learning can be introduced to dynamic graph analysis tasks. However, the self-supervised learning paradigm for dynamic graph learning has not been sufficiently investigated due to the complicated properties of the evolving graphs. We assume that the dynamic graphs consist of three independent types of key factors, i.e., graph time-variant information, time-invariant information and noise. Based on this assumption, we propose the Self-supervised Decoupling for Dynamic Graph (SDDG) framework for explicitly decoupling the representation which characterizes these three factors, thus enhancing the interpretability of the learned representation and link prediction performance. More specifically, we design an encoder-decoder architecture to sufficiently exploit the dynamic graph itself with multiple regularizations, so that the time-variant embedding of dynamic graph data can be effectively decoupled from the perspectives of node and structure for time-variant-aware link prediction. Experiments on five benchmark link prediction tasks demonstrate the significant improvement of our SDDG over the state-of-the-art methods. For example, SDDG achieves 98.2% and 97.3% Top-1 AUC on Reddit-B and Reddit-T, outperforming the DDGCL model, by 1.9% and 1.8%, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Supervised learning

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Optimizing Urban Transport: Integrating Dbscan Clustering and Dijkstra's Algorithm for Enhanced Route Planning

SSRN

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SSRN 2024年

作者： Reguieg, Seddik Dahane, Miloud Collart-Dutilleul, Simon Mami, Mohammed Amine ESTAS -Evaluation of Automated Transport Systems and their Safety- Laboratory COSYS -Components and Systems- Department Gustave Eiffel University Lille France RIIR -Industrial Computing and Networks Research- Laboratory Computer Science Department University of Oran 1 Ahmed Ben Bella Oran Algeria

The paper presumes to bring about the development of a new technique for the optimization of urban transport in the city of Oran, Algeria, using graph theory, reinforced by the Dijkstra algorithm, all in a bid to help the transportation network become more efficient within the city. It thus involves rigorous data collection and management of the lines of urban transport and bus stops which are geo-referenced. Clustering takes place between the stops taking into consideration the geography and the data in traffic using the DBSCAN method of clustering to simplify the network for further analysis. When the time comes to derive the shortest path within this structured network, the Dijkstra algorithm comes to play. In this case, it makes not only the route optimization process much easier but also brings down the travel times from one place to another, which has a big boost to overall system efficiency. The integration of these techniques represents a step change in urban transport planning and can provide key insights and tools to practitioners and decision-makers in this field that may find applications in other cities facing the same challenges. © 2024, The Authors. All rights reserved.

关键词： Graph theory

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BTC-Shadow: an analysis and visualization system for exposing implicit behaviors in Bitcoin transaction graphs

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Frontiers of computer science 2023年第6期17卷 167-169页

作者： Ding BAO Wei REN Yuexin XIANG Weimao LIU Tianqing ZHU Yi REN Kim-Kwang Raymond CHOO School of Computer Science China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Intelligent Geo-Information Processing Wuhan 430074China NSFOCUS Technologies Group Co. Ltd.Beijing 100089China School of Computing Sciences University of East AngliaNorwich NR47TJUK Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX 78249-0631USA

1 Introduction A Bitcoin ledger comprises a sizable number of transaction records,which can be utilized to make it easier to track and analyze the traits and patterns of cryptocurrency-related *** facilitate the visual analysis of Bitcoin,numerous tools with various aims have been *** example,MiningVis[1]and SuPoolVisor[2]are the analytics systems for Bitcoin mining pools,as well as[3-5]focus on the Bitcoin transaction graphs ***,due to our previous research requirements for Bitcoin transaction graphs,none of the available tools can provide exploring the features of connection related to the address and observe significant visual ***,using these tools is challenging to navigate to abnormal node clusters effortlessly from large node groups and then analyze the local interlink characteristics between transaction nodes with interactive analytics.

关键词： analytic implicit utilized

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Design and Implementation of a Compact CPW-Fed UWB Monopole Antenna 2

Design and Implementation of a Compact CPW-Fed UWB Monopole ...

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2nd International Conference on Advanced Electrical Engineering, ICAEE 2022

作者： Mokhtari, Mokhtar Mansoul, Ali Challal, Mouloud Oussaid, Rachid Usthb Telecom Division Cdta Faculty of Electronics and Computer Science Algiers Algeria Telecom Division Cdta Algiers Algeria Institute of Electrical and Electronic Engineering University m'Hamed Bougara - Boumerdes Signals and Systems Laboratory Boumerdes Algeria Usthb Faculty of Electronics and Computer Sciences Algiers Algeria

ISBN: (数字)9781665417419

ISBN: (纸本)9781665417419

In this paper, a compact ultra-wideband patch antenna is presented and discussed. The antenna, printed on Rogers RT5580, consists of leaf-shaped radiating element excited through tapered coplanar waveguide feed line, and semi-elliptical partial ground plane. It covers a frequency range starting from 2.8 to 15.1 GHz. The antenna prototype is fabricated, and a good agreement is obtained between the experimental and simulation results indicating that the proposed structure, having a reasonably compact size of 20x32 mm2 is suitable for a wide variety of applications due to its large operating frequency. © 2022 IEEE.

关键词： Monopole antennas

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eVision: Respiratory Syncytial Virus (RSV) Seasonal Trend Forecasting

eVision: Respiratory Syncytial Virus (RSV) Seasonal Trend Fo...

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Biomedical Engineering International Conference (BMEiCON)

作者： Divya Syal Juan Zuluaga Navid Shaghaghi Department of Computer Science & Engineering (CSEN) and Information Systems & Analytics (ISA) Ethical Pragmatic and Intelligent Computing (EPIC) Research Laboratory Santa Clara University (SCU) Santa Clara California USA

ISBN: (数字)9798331505431

ISBN: (纸本)9798331505448

Respiratory Syncytial Virus (RSV) is a flu-like illness annually responsible for 100K-160K hospitalizations and 6K-10K deaths in adults above the age of 65 as well as 58K-80K hospitalizations and 100–300 deaths in children under 5 years of age in the United States. A live prediction of the rate of growth of RSV cases during the RSV season can empower healthcare organizations to best prepare for RSV peaks and ultimately reduce the RSV mortality rate without over-budgeting, be it pharmaceutical companies producing too many perishable testing kits, vaccines and medications or hospitals keeping too many beds and staffing in reserve and thus away from other needed patients. This study adds a new RSV forecasting model to eVision, the collection of disease-forecasting Long Short-Term Memory (LSTM) neural networks, developed by Santa Clara University's EPIC (Ethical, Pragmatic, and Intelligent computing) Research lab. This model leverages data from the Centers of Disease Control and Prevention (CDC) and Google Trends between 2021–2024 to predict the trend of RSV cases through the typical RSV season and beyond in the United States. Given the lack of extensive RSV data - which has only been collected by the CDC on a national level since 2021 - and how easily RSV is mistaken for other diseases, this model has a limitation on the number of weeks ahead in which its prediction can maintain a high accuracy for. This model successfully predicts percent positive RSV Polymerase Chain Reaction (PCR) tests with 94.45 % forecasting accuracy three weeks in advance at the national level, for the 2023–2024 RSV season.

关键词： Accuracy computer viruses Computational modeling Biological system modeling Buildings Predictive models Market research Data models Forecasting Long short term memory

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