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ai in drug development:advances in response,combination therapy,repositioning,and molecular design

science China(Information sciences) 2025年

作者： Ren QI Shujia LIU Xingqi HUI Alexey K.SHAYTAN Bin LIU School of Computer Science and Technology Beijing Institute of Technology Department of Biology Lomonosov Moscow State University International Laboratory of Bioinformatics AI and Digital Sciences Institute Faculty of Computer ScienceHSE University SMBU-MSU-BIT Joint Laboratory on Bioinformatics and Engineering Biology Shenzhen MSU-BIT University Zhongguancun Academy

Artificial intelligence (ai) is revolutionizing the field of drug development, particularly in addressing key challenges such as drug response prediction, drug combination design, drug repositioning, and drug molecule generation. Traditional drug discovery is hindered by long timelines, high costs, and low success rates, necessitating innovative technologies to accelerate the process. ai technologies, such as deep learning, graph neural networks, and generative models, have demonstrated significant potential in enhancing the accuracy of drug response predictions, optimizing drug combination strategies,identifying opportunities for drug repositioning, and generating drug molecules with specific biological activities. These advancements not only accelerate the drug development process but also open up new possibilities for precision *** review discusses the latest applications and developments of ai in drug discovery, highlighting the breakthroughs and challenges ai addresses in drug development. By summarizing the current research progress, this study provides theoretical support and practical guidance for further applications of ai in drug development.

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Low-Resource Named Entity Recognition: Can One-vs-All AUC Maximization Help? 23

Low-Resource Named Entity Recognition: Can One-vs-All AUC Ma...

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23rd IEEE International Conference on Data Mining, ICDM 2023

作者： Nguyen, Ngoc Dang Tan, Wei Du, Lan Buntine, Wray Beare, Richard Chen, Changyou Monash University Department of Data Science and Ai Melbourne Australia VinUniversity College of Engineering and Computer Science Hanoi Viet Nam Faculty of Medicine Peninsula Clinical School Melbourne Australia University at Buffalo Department of Cs and Engineering New York United States

ISBN: (纸本)9798350307887

Named entity recognition (NER), a task that identifies and categorizes named entities such as persons or organizations from text, is traditionally framed as a multi-class classification problem. However, this approach often overlooks the issues of imbalanced label distributions, particularly in low-resource settings, which is common in certain NER contexts, like biomedical NER (bioNER). To address these issues, we propose an innovative reformulation of the multi-class problem as a one-vs-all (OVA) learning problem and introduce a loss function based on the area under the receiver operating characteristic curve (AUC). To enhance the efficiency of our OVA-based approach, we propose two training strategies: one groups labels with similar linguistic characteristics, and another employs meta-learning. The superiority of our approach is confirmed by its performance, which surpasses traditional NER learning in varying NER settings. © 2023 IEEE.

关键词： Natural language processing systems

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A Comprehensive Examination of Consensus Algorithms and Their Impact on Blockchain Scalability

A Comprehensive Examination of Consensus Algorithms and Thei...

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Innovations in Communications, Electrical and computer engineering (ICICEC), International Conference on

作者： M Veera Brahmam Y Laxmi Prasanna V T Ram Pavan Kumar M Kanneboina Ashok K V S S R Murthy S Sindhura Computer Science & Engineering (Data Science) Bapatla Engineering College Bapatla India Computer Science & Engineering (AI&ML) Anurag Engineering College Ananthagiri Suryapet India Computer Science Kakaraparti Bhavanarayana College Vijayawada Andhra Pradesh India School of Engineering Mallareddy University Hyderabad Telangana India Computer Science & Engineering SRKR Engineering College(A) Bhimavaram India Computer Science & Engineering NRI Institute of Technology Agiripalli Vijayawada India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

This paper goes one step ahead into a suggested testbed design with which empirical analysis can be carried out. Deep, extended consensus methods and their relative impact on blockchain scalability are analyzed. Our experimental configuration is based on two mining nodes and a master node, and to be precise, it is a blockchain network consisting of five physical nodes arranged in such a way so as to use Proof of Work (PoW) and Round Robin as separate consensus procedures. Architecture even ensures safe communication through Java-based remote clients running RPC commands with a JSON base. The performance of each consensus algorithm was tested using a series of scalability tests in order to find the transaction throughput and execution times under a different transaction load. According to the results shown, as the transactions are transmitted in bigger quantities, Round Robin outperforms PoW by way of the number of transactions processing and throughput time. In particular, it presented a comparative advantage relative to Proof of Work in terms of efficiency by processing transactions much faster and maintaining higher throughput levels. Overall, these results highlight the significance of properly selecting the consensus algorithm in the quest for blockchain performance optimization, especially under scenarios where fast transaction processing is paramount. In a nutshell, this study will conclude with the trade-off between efficiency and decentralization of blockchains within applications and recommend Round Robin as a feasible solution if speed in transaction matters.

关键词： Technological innovation Scalability Consensus algorithm computer architecture Throughput Proof of Work Blockchains Round robin Optimization

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Machine Learning Algorithms for Advanced Rainfall Prediction 23

Machine Learning Algorithms for Advanced Rainfall Prediction

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Vivek Saxena Uday Pratap Singh Bersha Kumari Ansh Khandelwal Department of Computer Science & Engineering Poornima Institute of Engineering & Technology India Department of AI & DS Poornima Institute of Engineering and Technology India Department of Computer Science Poornima Institute of Engineering and Technology India

ISBN: (纸本)9798400709418

Rainfall forecast is essential in water resource management, agricultural planning, and disaster preparedness. Traditional rainfall forecasting systems have accuracy and lead time constraints. The rise of machine learning (ml) techniques provides a viable route for addressing these issues. This review paper examines current advancements in the application of machine learning for forecasting rainfall. It investigates the difficulties connected with rainfall forecasting, such as the complex and nonlinear nature of precipitation systems, data scarcity, and the requirement for real-time forecasting. The research investigates several machine learning models, data sources, and pre-processing approaches used to improve prediction accuracy. It also covers the incorporation of satellite data, weather radar data, and climate models into ml-based rainfall forecast systems. A comprehensive review of model performance in various geographic locations and climatic situations is offered, along with an assessment of limits and future development potential. This paper intends to assist academics, meteorologists, and policymakers in utilizing ml to provide more accurate and dependable rainfall forecasts, hence improving water resource management and disaster mitigation.

关键词： Keywords— Rainfall forecast

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From canonical correlation analysis to self-supervised graph neural networks 21

From canonical correlation analysis to self-supervised graph...

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Hengrui Zhang Qitian Wu Junchi Yan David Wipf Philip S. Yu Department of Computer Science University of Illinois at Chicago Department of Computer Science and Engineering Shanghai Jiao Tong University AWS Shanghai AI Lab

ISBN: (纸本)9781713845393

We introduce a conceptually simple yet effective model for self-supervised representation learning with graph data. It follows the previous methods that generate two views of an input graph through data augmentation. However, unlike contrastive methods that focus on instance-level discrimination, we optimize an innovative feature-level objective inspired by classical Canonical Correlation Analysis. Compared with other works, our approach requires none of the parameterized mutual information estimator, additional projector, asymmetric structures, and most importantly, negative samples which can be costly. We show that the new objective essentially 1) aims at discarding augmentation-variant information by learning invariant representations, and 2) can prevent degenerated solutions by decorrelating features in different dimensions. Our theoretical analysis further provides an understanding for the new objective which can be equivalently seen as an instantiation of the Information Bottleneck Principle under the self-supervised setting. Despite its simplicity, our method performs competitively on seven public graph datasets.

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Examination System Automation Using Natural Language Processing

Examination System Automation Using Natural Language Process...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： R Praveen Kumar N. Muthukumaran Cheruku Hanisha Pohar Rithvik M.Saipavan Goud Department of Computer Science and Engineering (AI&ML) Vignan Institute of Technology and Science Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

Numerous examination portals have been created and distributed over several servers for a number of objectives, including the administration of tests in connection withadmission exams, Olympic competitions at national and worldwide levels, and the administration of placement tests. It turns out that practically all portals are set up for multiple-choice examinations, as we found. Our goal here is not to work on current technology, but rather to work on technology that is very uncommon. The descriptive online examination method is discussed here. Simple to use, multiple choice questions are composed of a question, many possible answers to the same question as well as a field to record the correct response in the database. Descriptive queries, on the other hand, do not fall within this category. To award marks to replies, it incorporates or employs Natural Language Processing (NLP) ideas. The model's responsibility is to lead procedure on the response string so it can give the right stamps to the examinee's responses. The data is generally assembled utilizing an enlightening web-based appraisal procedure. It is likewise inspected, and the made model honors right stamps to the inquiry's answers. With respect to the front-end, HTml 5, CS S 3, Bootstrap, and JavaS cript are utilized. The back-end was created in Python using Django, NLTK for Natural Language Processing, and SQLite version 3 as the database.

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Prediction of Hypertension Disease Using Machine Learning Techniques: Case Study from Jordan 25

Prediction of Hypertension Disease Using Machine Learning Te...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alazaidah, Raed Samara, Ghassan Katrawi, Anwar Hadi, Wael Al-Safarini, Maram Y. Al-Mamoori, Farah Elrashidi, Ali Alqaraleh, Muhyeeddin Alzoubi, Haneen Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Faculty of It Department of Computer Science Zarqa Jordan Petra University Faculty of It Information Security Department Amman Jordan Zarqa University Faculty of Pharmacy Department of Pharmaceutical Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of It Department of Software Engineering Zarqa Jordan Applied Science Private University Facultty of Information Technology Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

Hypertension is one of the most common diseases in Jordan. It is one of the main reasons of death among Jordanian adult citizens. Worldwide, nearly 13% of all deaths are due to Hypertension with nearly 8 million deaths yearly. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ml) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that Random Forest, Random Committee, and MultilayerPerceptron are the best classification models to handle the task of the early prediction of Hypertension in Jordan. © 2024 IEEE.

关键词： Prediction models

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An Adaptive Deep Learning Framework for Prediction of Agricultural Yield 2

An Adaptive Deep Learning Framework for Prediction of Agricu...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Rao, K Rama Swapna Kumari, Manthena Eklarker, Ravindra Shaker Reddy, Pundru Chandra Muley, Kirti Burugari, Vijay Kumar Geethanjali College of Engineering and Technology Electronics and Communication Engineering Telangana Hyderabad India Hyderabad India Vardhaman College of Engineering Department of Ai &ds Telangana Hyderabad India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India Bharati Vidyapeeth's Institute of Management and Information Technology Master of Computer Applications Navi Mumbai India Koneru Lakshmaiah Education Foundation Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350317558

Agriculture and its large yields are vital to human survival. Recently, machine learning and deep learning have been applied to determine agricultural output. This study estimated apple production utilizing a unique deep-learning(DL)-based technique. A study-designed convolutional neural network (CNN) classified golden and royal gala apple photos. Classifying CNN-extracted pictures with traditional machine-learning(ml) methods was a hybrid application. Datasets A is used to evaluate the performance of the proposed model. Based on Dataset A accuracy findings, the CNN-SVM hybrid model performed best. CNN-SVM and CNN-Gradient Boosting hybrid models performed best and accurately for Dataset A. The more balanced distribution of train, test, and validation sizes, the suggested hybrid CNN model, and the model's assessment made dataset C the best choice. The hybrid model was 99.70% accurate for Dataset A. Precision, recall, f-measure, and Cohen kappa were 99%. The study found that hybrid models could accurately predict apple fruit yield using golden and royal gala photos. © 2024 IEEE.

关键词： DL Ensemble-Methods ml Smart-farms Yield-Prediction

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Classification of Various Diseases in Tomato Crops using Neural Network Approaches

Classification of Various Diseases in Tomato Crops using Neu...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Kodem Sravan U Sadhana Sallauddin Mohmmad Bobby K Simon K Sridhar School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of CSE(AI&ML) CVR College of Engineering Hyderabad Telangana India Department of Computer Science and Engineering Hyderabad Institute of Technology and Management Hyderabad Telangana India Vaageswari College of Engineering Karimnager Telangana India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Tomatoes are an important crop worldwide, playing a substantial role in the agriculture sector. However, they are sensitive to various diseases that can affect yield, quality of crops, and former economic stability. This research focused on developing a system for classifying tomato diseases based on leaf images using machine learning and image processing techniques. Our research utilized convolutional neural networks (CNNs) to predict and classify tomato diseases, employing a dataset from Kaggle containing five disease classes and one healthy leaf class. Our optimized models were tested, showing inconsistent accuracy in detecting and classifying five different tomato diseases. Apart from that, MobileNet and VGG-19 models showed prominent performance and achieved an accuracy of 93.87% compared to other models such as VGG16 and a Customized CNN.

关键词： Technological innovation Accuracy Biological system modeling Image processing Neural networks Crops Stability analysis Convolutional neural networks Protection Diseases

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Wandering Strategy–Sand Cat Swarm Optimization-Based Energy Efficient Underwater Wireless Sensor Network

Wandering Strategy–Sand Cat Swarm Optimization-Based Energy...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： M I Habelalmateen Sravankumar B Keirolona Safana Seles J Rekha Phadke Tabitha Indupalli The Islamic university Najaf Iraq CSE (AI and ML) Vaagdevi College of Engineering Warangal India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Dept of ECE Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering GRIET Hyderabad Telangana India

Underwater Wireless Sensor Network (UWSN) includes a numerous number of sensors positioned in oceans and rivers to observe in underwater environment. Though, underwater sensors are limited for energy and that is tedious for replacing a battery, resulted in energy efficient is main problem. For clustering and routing, in this research proposed a Wandering Strategy – Sand Cat Swarm optimization (WS-SCSO) algorithm which enhanced network lifetime. The WS-SCSO algorithm selects the appropriate Cluster Head (CH) for network and then generates a cluster. Then, by using WS-SCSO algorithm identifies a suitable route for nodes that transmits a data. The proposed algorithm enhances a network lifetime with less energy consumption. The evaluation metrics used to evaluate proposed algorithm are alive nodes, dead nodes, total energy consumption and packet delivery ratio. The proposed algorithm attained less energy consumption of 3% at 200 rounds, 7% at 400 rounds, 18% at 600, 31% at 800 rounds and 55% at 1000 rounds which is effective than previous optimization algorithms like Improved Metaheuristics based Clustering with Multihop Routing protocol (IMCMR-UWSN).

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