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Multi-View Kernel Learning for Identification of Disease Genes

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第3期20卷 2278-2290页

作者： Shah, Ekta Maji, Pradipta Indian Stat Inst Machine Intelligence Unit Biomed Imaging & Bioinformat Lab Kolkata 700108 India

Gene expression data sets and protein-protein interaction (PPI) networks are two heterogeneous data sources that have been extensively studied, due to their ability to capture the co-expression patterns among genes and their topological connections. Although they depict different traits of the data, both of them tend to group co-functional genes together. This phenomenon agrees with the basic assumption of multi-view kernel learning, according to which different views of the data contain a similar inherent cluster structure. Based on this inference, a new multi-view kernel learning based disease gene identification algorithm, termed as DiGId, is put forward. A novel multi-view kernel learning approach is proposed that aims to learn a consensus kernel, which efficiently captures the heterogeneous information of individual views as well as depicts the underlying inherent cluster structure. Some low-rank constraints are imposed on the learned multi-view kernel, so that it can effectively be partitioned into $k$k or fewer clusters. The learned joint cluster structure is used to curate a set of potential disease genes. Moreover, a novel approach is put forward to quantify the importance of each view. In order to demonstrate the effectiveness of the proposed approach in capturing the relevant information depicted by individual views, an extensive analysis is performed on four different cancer-related gene expression data sets and PPI network, considering different similarity measures.

关键词： Diseases Kernel Gene expression Proteins Clustering algorithms Soft sensors biology Clustering disease gene identification multi-view kernel learning unsupervised learning

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Block Search Stochastic Simulation Algorithm (BlSSSA): A Fast Stochastic Simulation Algorithm for Modeling Large Biochemical Networks

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第4期19卷 2111-2123页

作者： Ghosh, Debraj De, Rajat K. Indian Stat Inst Machine Intelligence Unit Kolkata 700108 W Bengal India

Stochastic simulation algorithms are extensively used for exploring stochastic behavior of biochemical pathways/networks. computational cost of these algorithms is high in simulating real biochemical systems due to their large size, complex structure and stiffness. In order to reduce the computational cost, several algorithms have been developed. It is observed that these algorithms are basically fast in simulating weakly coupled networks. In case of strongly coupled networks, they become slow as their computational cost become high in maintaining complex data structures. Here, we develop Block Search Stochastic Simulation Algorithm (BlSSSA). BlSSSA is not only fast in simulating weakly coupled networks but also fast in simulating strongly coupled and stiff networks. We compare its performance with other existing algorithms using two hypothetical networks, viz., linear chain and colloidal aggregation network, and three real biochemical networks, viz., B cell receptor signaling network, FceRI signaling network and a stiff 1,3-Butadiene Oxidation network. It has been shown that BlSSSA is faster than other algorithms considered in this study.

关键词： Stochastic processes Biological system modeling Mathematical model computational modeling computational efficiency Indexes Statistics Gillespie algorithm B cell receptor signaling network FceRI signaling network Stiff network Colloidal aggregation network and stochastic modeling

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Multi-Modality Fusion & Inductive Knowledge Transfer Underlying Non-Sparse Multi-Kernel Learning and Distribution Adaption

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第4期20卷 2387-2397页

作者： Zhang, Yuanpeng Xia, Kaijian Jiang, Yizhang Qian, Pengjiang Cai, Weiwei Qiu, Chengyu Lai, Khin Wee Wu, Dongrui Nantong Univ Med Nursing Sch Dept Med Informat Nantong 226001 Jiangsu Peoples R China Univ Malaya Fac Engn Dept Biomed Engn Kuala Lumpur 50603 Malaysia Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Huazhong Univ Sci & Technol Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Hubei Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Cent South Univ Forestry & Technol Sch Logist & Transportat Changsha 410004 Peoples R China AiTech Artificial Intelligence Res Inst Changsha 410000 Peoples R China

With the development of sensors, more and more multimodal data are accumulated, especially in biomedical and bioinformatics fields. Therefore, multimodal data analysis becomes very important and urgent. In this study, we combine multi-kernel learning and transfer learning, and propose a feature-level multi-modality fusion model with insufficient training samples. To be specific, we firstly extend kernel Ridge regression to its multi-kernel version under the l(p)-norm constraint to explore complementary patterns contained in multimodal data. Then we use marginal probability distribution adaption to minimize the distribution differences between the source domain and the target domain to solve the problem of insufficient training samples. Based on epilepsy EEG data provided by the University of Bonn, we construct 12 multi-modality & transfer scenarios to evaluate our model. Experimental results show that compared with baselines, our model performs better on most scenarios.

关键词： Multi-kernel learning transfer learning multi-modality fusion EEG manifold regularization

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Remote Pulse Rate Variability Measurement with Adaptive Slope Sum Function 11

Remote Pulse Rate Variability Measurement with Adaptive Slop...

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11th International Conference on bioinformatics and computational biology (ICBCB)

作者： Kehang Yang Peixi Li Zhejiang Gongshang Univ Sussex Artificial Intelligence Inst Hangzhou Peoples R China

ISBN: (纸本)9798350397871

Compared to Electrocardiography (ECG) and contact Photoplethysmography (PPG), the signal obtained from remote Photoplethysmography (rPPG) is much more noisy due to the body movement, light variation, sensor noise, low scanning frequency of the cameras, etc. As a result, the measurement of Pulse Rate Variability (PRV) based on rPPG is a more challenging task than measuring PRV and Heart Rate Variability (HRV) from contact PPG and ECG, because it requires precise peak detection on the noisy rPPG signal. To address this issue, some researchers used slope sum function (SSF) to enhance the rising trend of the signal and reduce the downward trend of it, so that the signal became more clear and easier for peak detection. This method is relatively effective, but it has some disadvantages. Firstly, the window size of the SSF is set manually. Secondly, there is a shift between the peaks on the new signal generated by the SSF and the original signal. Thirdly, the parameter is fixed for the entire dataset, which is not ideal for real-life scenario where there are sudden changes in signal. In this paper, we proposed a novel algorithm which adaptively and automatically set the window size of SSF using continuous wavelet transform. We experimentally showed that the proposed method improved the remote measurement of PRV compared to the existing methods in the rPPG framework. We believe that our contribution is conducive to practicalization and commercialization of the related research.

关键词： Remote Photoplethysmography Peak Detection Pulse Rate Variability Slope Sum Function Continuous Wavelet Transform

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Knowledge Graph-Based Convolutional Network Coupled With Sentiment Analysis Towards Enhanced Drug Recommendation

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2024年第4期21卷 983-994页

作者： Saadat, Hajira Shah, Babar Halim, Zahid Anwar, Sajid Ghulam Ishaq Khan Inst Engn Sci & Technol Fac Comp Sci & Engn Machine Intelligence Res Grp MInG Topi 23640 Khyber Pakhtunk Pakistan Zayed Univ Coll Technol Innovat Abu Dhabi 4783 U Arab Emirates Inst Management Sci Ctr Excellence Informat Technol Peshawar 25000 Khyber Pakhtunk Pakistan

Recommending appropriate drugs to patients based on their history and symptoms is a complex real-world problem. Knowing whether a drug is useful without its consumption by a variety of people followed by proper evaluation is a challenge. Modern-day recommender systems can assist in this provided they receive large data to learn. Public reviews on various drugs are available for knowledge sharing. These reviews assist in recommending the best and most appropriate option to the user. The explicit feedback underpins the entire recommender system. This work develops a novel knowledge graph-based convolutional network for recommending drugs. The knowledge graph is coupled with sentiment analysis extracted from the public reviews on drugs to enhance drug recommendations. For each drug that has been used previously, sentiments have been analyzed to determine which one has the most effective reviews. The knowledge graph effectively captures user-item relatedness by mining its associated attributes. Experiments are performed on public benchmarks and a comparison is made with closely related state-of-the-art works. Based on the obtained results, the current work performs better than the past contributions by achieving up to 98.7% Area Under Curve (AUC) score.

关键词： Drugs Recommender systems Support vector machines Sentiment analysis Knowledge engineering Motion pictures Radio frequency Convolutional network Recommender system drugs user-item knowledge graph sentiment analysis adverse drug reaction

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SLPA-Net: A Real-Time Recognition Network for Intelligent Stomata Localization and Phenotypic Analysis

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2024年第3期21卷 372-382页

作者： Yang, Xiao-Hui Wang, Ye-Tong Wu, Ming-Hui Li, Fan Zhou, Cheng-Long Yang, Li-Jun Zheng, Chen Li, Yong Li, Zhi Guo, Si-Yi Song, Chun-Peng Henan Univ Henan Engn Res Ctr Artificial Intelligence Theory Sch Math & Stat Kaifeng 475004 Peoples R China Henan Univ Med Imaging Res Inst Huaihe Hosp Kaifeng 475004 Peoples R China Cent South Univ Sch Automat Changsha 410000 Peoples R China Henan Univ State Key Lab Crop Stress Adaptat & Improvement Kaifeng 475004 Peoples R China

Plant stomatal phenotype traits play an important role in improving crop water use efficiency, stress resistance and yield. However, at present, the acquisition of phenotype traits mainly relies on manual measurement, which is time-consuming and laborious. In order to obtain high-throughput stomatal phenotype traits, we proposed a real-time recognition network SLPA-Net for stomata localization and phenotypic analysis. After locating and identifying stomatal density data, ellipse fitting is used to automatically obtain phenotype data such as apertures. Aiming at the problems of small stomata and high similarity to background, we introduced ECANet to improve the accuracy of stoma and aperture location. In order to effectively alleviate the unbalance problem in bounding box regression, we replaced the Loss function with a more effective Focal EIoU Loss. The experimental results show that SLPA-Net has excellent performance in the migration generalization and robustness of stomata and apertures detection and identification, as well as the correlation between stomata phenotype data obtained and artificial data.

关键词： Apertures Feature extraction Crops Training Task analysis Stress Software Stomata detection phenotypic analysis deep learning real-time detection

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Equivariant Interaction-Aware Graph Network for Predicting the Binding Affinity of Protein-Ligand

IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATIC...

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ieee TRANSACTIONS ON computational biology AND bioinformatics 2025年第2期22卷 855-866页

作者： Min, Xiaoping Zou, Jiajun Xie, Jun Yang, Qianli Liao, Yiyang Ying, Junjie Jin, Xiaocheng Lu, Xiaoli Zhang, Jun Yu, Hai Ge, Shengxiang Xia, Ningshao Xiamen Univ Sch Informat Xiamen 361005 Peoples R China Xiamen Univ Inst Artificial Intelligence Xiamen 361005 Peoples R China Xiamen Univ Informat & Networking Ctr Xiamen 361005 Peoples R China Xiamen Univ Natl Inst Diagnost & Vaccine Dev Infect Dis Xiamen 361005 Peoples R China

The success of drug discovery relies on predicting the binding affinity of protein-ligand. Applying deep learning to this field can expedite the process and reduce resource consumption. Recently, researchers have employed graph neural networks for predicting protein-ligand binding affinitiy, showcasing remarkable performance. However, this is largely attributed to the natural representation of biomolecule by graph neural networks, rather than a rational modeling of interactions within protein-ligand complex. In this regard, we have developed an Equivariant Interaction-aware Graph Network (EIGN), capable of learning 3D geometric structural information of complex while perceiving interactions related to protein-ligand binding affinity between nodes. Specifically, we designed distance-inspired edge-gated attention layer for inter-node interactions within the complex, uniformly learning interactions within and between molecules. To precisely simulate interactions between nodes, we considered local structural information around nodes when interactions occur. Leveraging equivariant convolutional layer to harness the advantages of learning geometric structure and drawing insights from existing work, we developed EIGN. Demonstrated on two benchmark sets, EIGN presents exceptional performance and generalization, highlighting the importance of accurate interaction modeling in drug discovery.

关键词： Proteins Three-dimensional displays Atoms Graph neural networks Training Feature extraction Data mining computational biology Euclidean distance Drugs Drug discovery protein-ligand binding affinity prediction protein-ligand interaction equivariant graph neural network

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Comparing Interpretable AI Approaches for the Clinical Environment: an Application to COVID-19

Comparing Interpretable AI Approaches for the Clinical Envir...

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ieee Conference on computational intelligence in bioinformatics and computational biology (ieee CIBCB)

作者： Onari, Mohsen Abbaspour Nobile, Marco S. Grau, Isel Fuchs, Caro Zhang, Yingqian Boer, Arjen-Kars Scharnhorst, Volkher Eindhoven Univ Technol Informat Syst Grp Eindhoven Artificial Intelligence Syst Inst Eindhoven Netherlands Ca Foscari Univ Environm Sci Informat & Stat Venice Italy Eindhoven Univ Technol Informat Syst Grp Eindhoven Netherlands Catharina Hosp Clin Chem Eindhoven Netherlands Eindhoven Univ Technol Biomed Engn Eindhoven Netherlands

ISBN: (数字)9781665484626

ISBN: (纸本)9781665484626

Machine Learning (ML) models play an important role in healthcare thanks to their remarkable performance in predicting complex phenomena. During the COVID-19 pandemic, different ML models were implemented to support decisions in the medical settings. However, clinical experts need to ensure that these models are valid, provide clinically useful information, and are implemented and used correctly. In this vein, they need to understand the logic behind the models to be able to trust them. Hence, developing transparent and interpretable models has increasing relevance. In this work, we applied four interpretable ML models including logistic regression, decision tree, pyFUME, and RIPPER to classify suspected COVID-19 patients based on clinical data collected from blood samples. After preprocessing the data set and training the models, we evaluate the models based on their predictive performance. Then, we illustrate that interpretability can be achieved in different ways. First, SHAP explanations are built from logistic regression and decision trees to obtain the features' importance. Then, the potential of pyFUME and RIPPER in providing inherent interpretability are reflected. Finally, potential ways to achieve trust in future studies are briefly discussed.

关键词： Machine Learning Explainable Artificial intelligence Interpretability Trust COVID-19

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Welcome to ieee CIBCB 2024

21st IEEE Conference on Computational Intelligence in Bioinf...

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21st ieee Conference on computational intelligence in bioinformatics and computational biology, CIBCB 2024 2024年

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A comprehensive study of databases to assess the reliability of metagenomic tools

A comprehensive study of databases to assess the reliability...

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ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Inês Branco Martins Jorge Miguel Silva João Rafael Almeida IEETA/DETI LASI University of Aveiro Aveiro Portugal

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

The advancement of metagenomics is closely tied to bioinformatic tools. These tools, which are essential for taxonomic classification and functional annotation, derive their reliability from the databases they use. However, the challenge arises when comparing these tools through literature, as their evaluations often occur within specific datasets. This practice makes it difficult to compare them directly with state-of-the-art tools, as it obscures how they might perform across a broader range of data. In this study, we evaluate the suitability of different databases for assessing the viability of metagenomic tools. By assessing these databases and comparing them to general databases, we aim to provide researchers with valuable insights into selecting the most appropriate database for their metagenomic studies, enhance the reliability and reproducibility of metagenomic technologies, and overcome challenges such as resolving low-abundance species, distinguishing closely related species, or handling environmental samples with high diversity.

关键词： Metagenomics Accuracy Microorganisms Databases Taxonomy Reproducibility of results Reliability bioinformatics computational intelligence computational biology

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