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PPII-AEAT: Prediction of protein-protein interaction inhibitors based on autoencoders with adversarial training

Computers in Biology and Medicine 2024年 172卷 108287-108287页

作者： Zhang, Zitong Zhao, Lingling Gao, Mengyao Chen, Yuanlong Wang, Junjie Wang, Chunyu Faculty of Computing Harbin Institute of Technology Harbin150001 China Department of Medical Informatics School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing211166 China

Protein-protein interactions (PPIs) have shown increasing potential as novel drug targets. The design and development of small molecule inhibitors targeting specific PPIs are crucial for the prevention and treatment of related diseases. Accordingly, effective computational methods are highly desired to meet the emerging need for the large-scale accurate prediction of PPI inhibitors. However, existing machine learning models rely heavily on the manual screening of features and lack generalizability. Here, we propose a new PPI inhibitor prediction method based on autoencoders with adversarial training (named PPII-AEAT) that can adaptively learn molecule representation to cope with different PPI targets. First, Extended-connectivity fingerprints and Mordred descriptors are employed to extract the primary features of small molecular compounds. Then, an autoencoder architecture is trained in three phases to learn high-level representations and predict inhibitory scores. We evaluate PPII-AEAT on nine PPI targets and two different tasks, including the PPI inhibitor identification task and inhibitory potency prediction task. The experimental results show that our proposed PPII-AEAT outperforms state-of-the-art methods. © 2024 Elsevier Ltd

关键词： Forecasting

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Explainable Artificial Intelligence for Maize Disease Diagnostics

Explainable Artificial Intelligence for Maize Disease Diagno...

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Computer Science and Data Engineering (CSDE), IEEE Asia-Pacific Conference on

作者： Mubaraka Banadda Ntanda Kimuli Aloysius Solome Nakazzi Othieno Benedict Ernest Mukisa Samuel Owekitiibwa Ggaliwango Marvin Department of Computer Science School of Computing and Informatics Technology (SCIT) Makerere University Kampala Uganda Department of Computer School of Computing and Informatics Technology (SCIT) Makerere University Kampala Uganda

This study focuses on the use of machine learning techniques, specifically Explainable AI and Deep Learning, to diagnose two prevalent diseases in maize crops: maize streak and maize leaf blight. These diseases have been major obstacles for farmers in effectively managing and identifying infections, leading to decreased harvest quantity and quality. By developing and training a customized model using supervised machine learning, combined with Transfer Learning using pretrained models like VGG19, MobileNet, we aim to improve maize disease diagnostics. The accuracy of the generated models was assessed, and the best performing model is selected for further analysis utilizing Explainable AI methods. The final step involves deploying the model on a mobile application, with the web application serving as a platform for visualizing the stored data collected through the mobile app in an external database. The ultimate objective of this research is to equip farmers with the necessary tool to enhance agricultural productivity by enabling precise and efficient diagnosis of maize diseases.

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Gaussian Backbone-Based Spherical Evolutionary Algorithm with Cross-search for Engineering Problems

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Journal of Bionic Engineering 2024年第2期21卷 1055-1091页

作者： Yupeng Li Dong Zhao Ali Asghar Heidari Shuihua Wang Huiling Chen Yudong Zhang College of Computer Science and Technology Changchun Normal UniversityChangchun130032JilinChina School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehranIran School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Biological Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou215123JiangsuChina Key Laboratory of Intelligent Informatics for Safety and Emergency of Zhejiang Province Wenzhou UniversityWenzhou325035China School of Computer Science and Engineering Southeast UniversityNanjing210096China

In recent years,with the increasing demand for social production,engineering design problems have gradually become more and more *** novel and well-performing meta-heuristic algorithms have been studied and developed to cope with this *** them,the Spherical Evolutionary Algorithm(SE)is one of the classical representative methods that proposed in recent years with admirable optimization ***,it tends to stagnate prematurely to local optima in solving some specific ***,this paper proposes an SE variant integrating the Cross-search Mutation(CSM)and Gaussian Backbone Strategy(GBS),called *** this study,the CSM can enhance its social learning ability,which strengthens the utilization rate of SE on effective information;the GBS cooperates with the original rules of SE to further improve the convergence effect of *** objectively demonstrate the core advantages of CGSE,this paper designs a series of global optimization experiments based on IEEE CEC2017,and CGSE is used to solve six engineering design problems with *** final experimental results fully showcase that,compared with the existing well-known methods,CGSE has a very significant competitive advantage in global tasks and has certain practical value in real ***,the proposed CGSE is a promising and first-rate algorithm with good potential strength in the field of engineering design.

关键词： Meta-heuristic algorithms Engineering optimization Spherical evolution algorithm Global optimization

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Swa-Bhasha: Romanized Sinhala to Sinhala Reverse Transliteration using a Hybrid Approach

Swa-Bhasha: Romanized Sinhala to Sinhala Reverse Translitera...

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Advanced Research in computing (ICARC), International Conference on

作者： T.G.D.K. Sumanathilaka Ruvan Weerasinghe Y.H.P.P. Priyadarshana School of Computing Informatics Institute of Technology Colombo Sri Lanka School of Computing University of Colombo Colombo Sri Lanka

With the social and technological revolution, the usage of social media platforms and instant message services strengthens native language compatibility in the digital arena. The Sinhala and the Romanized Sinhala became the prominent typing languages among the general Sri Lankan community. Informal short-hand-based typing and short net acronyms were used for easier Sinhala typing. But Typing Romanized Sinhala using ad-hoc transliterations and getting the expected output in native Sinhala is less accurate and time-consuming. Therefore, this study aims to introduce a novel reverse transliterator which can back transliterate and suggest Romanized Sinhala to Sinhala words. The Transliterator has been modelled using the Statistical approach with Trigram and Rule-based model for back transliteration purposes and Knowledge-based with a Trie data structure for suggesting purposes. The proposed solution is capable of transliterating both formal and informal shorthand Romanized Sinhala. This hybrid model used in the study is capable of efficient transliteration with the word level accuracy of 0.84. This proposed model can be used in digital platforms to enhance the usability of native Sinhala communication in a much more efficient way.

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An Optimized Neural Network with Bat Algorithm for DNA Sequence Classification

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Computers, Materials & Continua 2022年第10期73卷 493-511页

作者： Muhammad Zubair Rehman Muhammad Aamir Nazri Mohd.Nawi Abdullah Khan Saima Anwar Lashari Siyab Khan Faculty of Computing and Information Technology Sohar UniversitySohar311Sultanate of Oman Soft Computing&Data Mining Centre(SMC) Faculty of Computer Science&Information TechnologyUniversiti Tun Hussein Onn Malaysia(UTHM)Batu Pahat86400Malaysia School of Electronics Computing and MathematicsUniversity of DerbyDerbyDE221GBUnited Kingdom Institute of Computer Sciences and Information Technology The University of Agriculture25120PeshawarPakistan College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia

Recently, many researchers have used nature inspired metaheuristicalgorithms due to their ability to perform optimally on complex problems. Tosolve problems in a simple way, in the recent era bat algorithm has becomefamous due to its high tendency towards convergence to the global optimummost of the time. But, still the standard bat with random walk has a problemof getting stuck in local minima. In order to solve this problem, this researchproposed bat algorithm with levy flight random walk. Then, the proposedBat with Levy flight algorithm is further hybridized with three differentvariants of ANN. The proposed BatLFBP is applied to the problem ofinsulin DNA sequence classification of healthy homosapien. For classificationperformance, the proposed models such as Bat levy flight Artificial NeuralNetwork (BatLFANN) and Bat levy Flight Back Propagation (BatLFBP) arecompared with the other state-of-the-art algorithms like Bat Artificial NeuralNetwork (BatANN), Bat back propagation (BatBP), Bat Gaussian distribution Artificial Neural Network (BatGDANN). And Bat Gaussian distributionback propagation (BatGDBP), in-terms of means squared error (MSE) andaccuracy. From the perspective of simulations results, it is show that theproposed BatLFANN achieved 99.88153% accuracy with MSE of 0.001185,and BatLFBP achieved 99.834185 accuracy with MSE of 0.001658 on *** on WL10 the proposed BatLFANN achieved 99.89899% accuracy withMSE of 0.00101, and BatLFBP achieved 99.84473% accuracy with MSE of0.004553. Similarly, on WL15 the proposed BatLFANN achieved 99.82853%accuracy with MSE of 0.001715, and BatLFBP achieved 99.3262% accuracywith MSE of 0.006738 which achieve better accuracy as compared to the otherhybrid models.

关键词： DNA sequence classification bat algorithm levy flight back propagation neural network hybrid artificial neural networks(HANN)

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Industry 4.0 (IR 4.0) for Pharmaceutical Industry: An Exploratory Review 1

Industry 4.0 (IR 4.0) for Pharmaceutical Industry: An Explor...

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1st International Visualization, informatics and technology Conference, IVIT 2022

作者： Mukhlas, Amalia Talip, Bazilah A. Che Mustapha, Jawahir Ismail, Shahrinaz Universiti Kuala Lumpur Institute of Information Technology Informatics and Analytics Malaysian Kuala Lumpur Malaysia Albukhary International University School of Computing and Informatics Alor Setar Kedah Malaysia

ISBN: (纸本)9781665486484

The pharmaceutical industry facing challenges despite digital transformation applying for the benefit of the industry. The companies and the industrial process need to be ready and adapt to the rapid changes or else be left behind by the competitors. The study provides a preliminary understanding of IR 4.0 within the pharmaceutical industry domain. The findings highlight the discussion of IR 4.0 technology in the pharmaceutical industry and the areas in the technology in which the technology has been applied. The research recommends the IR 4.0 technology practices in the operation and process, the elements of technology in the areas, challenges of technology implementation, relatable concepts and theories facilitating the future study. © 2022 IEEE.

关键词： exploratory review IR 4.0 pharmaceutical pharmaceutical industry pharmaceutical technology technology

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Learning Order Forest for Qualitative-Attribute Data Clustering 27

Learning Order Forest for Qualitative-Attribute Data Cluster...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zhao, Mingjie Feng, Sen Zhang, Yiqun Li, Mengke Lu, Yang Cheung, Yiu-Ming School of Computer Science and Technology Guangdong University of Technology Guangzhou China Shenzhen China School of Computer Science and Software Engineering Shenzhen University Shenzhen China Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China Department of Computer Science Hong Kong Baptist University Hong Kong

ISBN: (纸本)9781643685489

Clustering is a fundamental approach to understanding data patterns, wherein the intuitive Euclidean distance space is commonly adopted. However, this is not the case for implicit cluster distributions reflected by qualitative attribute values, e.g., the nominal values of attributes like symptoms, marital status, etc. This paper, therefore, discovered a tree-like distance structure to flexibly represent the local order relationship among intra-attribute qualitative values. That is, treating a value as the vertex of the tree allows to capture rich order relationships among the vertex value and the others. To obtain the trees in a clustering-friendly form, a joint learning mechanism is proposed to iteratively obtain more appropriate tree structures and clusters. It turns out that the latent distance space of the whole dataset can be well-represented by a forest consisting of the learned trees. Extensive experiments demonstrate that the joint learning adapts the forest to the clustering task to yield accurate results. Comparisons of 10 counterparts on 12 real benchmark datasets with significance tests verify the superiority of the proposed method. Source code of the proposed method is available at [39]. © 2024 The Authors.

关键词： Adversarial machine learning

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Research on the Application of LSTM-SA-AdaBoost Hybrid Model in Stock Forecasting 5

Research on the Application of LSTM-SA-AdaBoost Hybrid Model...

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5th International Conference on Applied Machine Learning, ICAML 2023

作者： Sun, Yu Mutalib, Sofianita Omar, Nasiroh Huang, Mingjing Guangdong University of Science and Technology Guangdong Province Dongguan China School of Computing Sciences College of Computing Informatics and Media Universiti Teknologi Mara Selangor Shah Alam40450 Malaysia School of Electrical Engineering College of Engineering Universiti Teknologi Mara Selangor Shah Alam40450 Malaysia

ISBN: (纸本)9798350341416

To improve the fitting and accuracy of stock prediction, an improved deep neural network combined with AdaBoost model (LSTM-SA-AdaBoost) is proposed. The model feature engineering includes data cleaning, correlation analysis and normalization. The model uses simulated annealing algorithm to optimize the model parameters. The attributes after feature selection will be trained, and the predicted attributes will be predicted and optimized iteratively through the two-layer LSTM network. From the experiment results, it shows that LSTM-SA-AdaBoost algorithm is superior to the unmodified LSTM-AdaBoost model and LSTM-XGBoost model, compared with the single-target feature selection algorithm of LSTM and RNN network models, it has better fitting and better accuracy. © 2023 IEEE.

关键词： Adaptive boosting

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Diverse Consensuses Paired with Motion Estimation-Based Multi-Model Fitting 24

Diverse Consensuses Paired with Motion Estimation-Based Mult...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Yin, Wenyu Lin, Shuyuan Lu, Yang Wang, Hanzi Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University Xiamen China College of Cyber Security College of Information Science and Technology Jinan University Guangzhou China

ISBN: (纸本)9798400706868

Multi-model fitting aims to robustly estimate the parameters of various model instances in data contaminated by noise and outliers. Most previous works employ only a single type of consensus or implicit fusion model to represent the correlation between data points and model hypotheses. This approach often results in unrealistic and incorrect model fitting in the presence of noise and uncertainty. In this paper, we propose a novel method of diverse Consensuses paired with Motion estimation-based multi-Model Fitting (CMMF), which leverages three types of diverse consensuses along with inter-model collaboration to enhance the effectiveness of multi-model fusion. We design a Tangent Consensus Residual Reconstruction (TCRR) module to capture motion structure information of two points at the pixel level. Additionally, we introduce a Cross Consensus Affinity (CCA) framework to strengthen the correlation between data points and model hypotheses. To address the challenge of multi-body motion estimation, we propose a Nested Consensus Clustering (NCC) strategy, which formulates multi-model fitting as a motion estimation problem. It explicitly establishes motion collaboration between models and ensures that multiple models are well-fitted. Extensive quantitative and qualitative experiments are conducted on four public datasets (i.e., AdelaideRMF-F, Hopkins155, KITTI, MTPV62), and the results demonstrate that our proposed method outperforms several state-of-the-art methods. © 2024 ACM.

关键词： Motion estimation

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Knowledge Base Processing Method Based on Text Classification Algorithm 5

Knowledge Base Processing Method Based on Text Classificatio...

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5th IEEE International Conference on Artificial Intelligence in Engineering and technology, IICAIET 2023

作者： Zhong, Baisheng Sainin, Mohd Shamrie Fun, Tan Soo Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Kota Kinabalu Malaysia School of Engineering Guangzhou College of Technology and Businces Guangzhou China Research Centre Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9798350304152

The text classification algorithm's knowledge base processing method utilizes existing data from the knowledge base to guide the construction and training of the classification model. In practical application, the knowledge base processing method has been prove to have a good performance in text classification tasks. The utilization of knowledge base processing method in text classification has led to an average accuracy improvement of more than 17%. Furthermore, this method significantly reduces labeling costs by approximately 70% compared to traditional approaches. Recently, knowledge base processing methods have been widely used in supporting business applications, social media analysis and other fields. This paper proposes a knowledge base method to establish a feature model related to domain speciality and combine it with traditional text classification algorithm, so as to optimize the training and reasoning process of the classification model and improve the accuracy of classification effect. Lastly, we suggested strategies to overcome the shortcoming of the knowledge base method in improving the construction and training of the classification model. © 2023 IEEE.

关键词： Classification (of information)

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