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Cross-Attention Based Multi-Resolution Feature Fusion Model for Self-Supervised Cervical OCT Image Classification

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第4期20卷 2541-2554页

作者： Wang, Qingbin Chen, Kaiyi Dou, Wanrong Ma, Yutao Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Cent China Normal Univ Sch Comp Sci Wuhan 430079 Peoples R China Cent China Normal Univ Hubei Prov Key Lab Artificial Intelligence & Smart Wuhan 430079 Peoples R China

Cervical cancer seriously endangers the health of the female reproductive system and even risks women's life in severe cases. Optical coherence tomography (OCT) is a non-invasive, real-time, high-resolution imaging technology for cervical tissues. However, since the interpretation of cervical OCT images is a knowledge-intensive, time-consuming task, it is tough to acquire a large number of high-quality labeled images quickly, which is a big challenge for supervised learning. In this study, we introduce the vision Transformer (ViT) architecture, which has recently achieved impressive results in natural image analysis, into the classification task of cervical OCT images. Our work aims to develop a computer-aided diagnosis (CADx) approach based on a self-supervised ViT-based model to classify cervical OCT images effectively. We leverage masked autoencoders (MAE) to perform self-supervised pre-training on cervical OCT images, so the proposed classification model has a better transfer learning ability. In the fine-tuning process, the ViT-based classification model extracts multi-scale features from OCT images of different resolutions and fuses them with the cross-attention module. The ten-fold cross-validation results on an OCT image dataset from a multi-center clinical study of 733 patients in China indicate that our model achieved an AUC value of 0.9963 +/- 0.0069 with a 95.89 +/- 3.30% sensitivity and 98.23 +/- 1.36 % specificity, outperforming some state-of-the-art classification models based on Transformers and convolutional neural networks (CNNs) in the binary classification task of detecting high-risk cervical diseases, including high-grade squamous intraepithelial lesion (HSIL) and cervical cancer. Furthermore, ourmodel with the cross-shaped voting strategy achieved a sensitivity of 92.06% and specificity of 95.56% on an external validation dataset containing 288 three-dimensional (3D) OCT volumes from118 Chinese patients in a different new hospital. This result

关键词： Cervical cancer cross-attention optical coherence tomography self-supervised learning vision transformer

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scBKAP: A Clustering Model for Single-Cell RNA-Seq Data Based on Bisecting K-Means

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第3期20卷 2007-2015页

作者： Wang, Xiaolin Gao, Hongli Qi, Ren Zheng, Ruiqing Gao, Xin Yu, Bin Qingdao Univ Sci & Technol Coll Math & Phys Qingdao 266061 Shandong Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China Cent South Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China King Abdullah Univ Sci & Technol KAUST Computat Biosci Res Ctr CBRC Thuwal 23955 Saudi Arabia Qingdao Univ Sci & Technol Coll Informat Sci & Technol Sch Data Sci Qingdao 266061 Shandong Peoples R China Univ Sci & Technol China Sch Data Sci Hefei 230027 Anhui Peoples R China

Advances in single-cell RNA sequencing (scRNA-seq) technologies allow researchers to analyze the genome-wide transcription profile and to solve biological problems at the individual-cell resolution. However, existing clustering methods on scRNA-seq suffer from high dropout rate and curse of dimensionality in the data. Here, we propose a novel pipeline, scBKAP, the cornerstone of which is a single-cell bisecting K-means clustering method based on an autoencoder network and a dimensionality reduction model MPDR. Specially, scBKAP utilizes an autoencoder network to reconstruct gene expression values from scRNA-seq data to alleviate the dropout issue, and the MPDR model composed of the M3Drop feature selection algorithm and the PHATE dimensionality reduction algorithm to reduce the dimensions of reconstructed data. The dimensionality-reduced data are then fed into the bisecting K-means clustering algorithm to identify the clusters of cells. Comprehensive experiments demonstrate scBKAP's superior performance over nine state-of-the-art single-cell clustering methods on 21 public scRNA-seq datasets and simulated datasets. The source codes and datasets are available at https://***/YuBinLab-QUST/scBKAP/ and https://***/10.24433/CO.4592131.v1.

关键词： Clustering algorithms Data models Pipelines Dimensionality reduction Clustering methods Standards Sequential analysis Autoencoder network bisecting K-means clustering Index Terms MPDR dimensionality reduction scRNA-seq

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Estimation of Discriminative Multimodal Brain Network Connectivity Using Message-Passing-Based Nonlinear Network Fusion

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第4期20卷 2398-2406页

作者： Chen, Nan Guo, Man Li, Yongchao Hu, Xiping Yao, Zhijun Hu, Bin Lanzhou Univ Sch Informat Sci & Engn Gansu Prov Key Lab Wearable Comp Lanzhou 730000 Gansu Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 510275 Guangdong Peoples R China Chinese Acad Sci Joint Res Ctr Cognit Neurosensor Technol Lanzhou U Gansu Prov Key Lab Wearable Comp Lanzhou 730000 Gansu Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci CAS Ctr Excellence Brain Sci & Intelligence Techno Shanghai 200083 Peoples R China Lanzhou Univ Minist Educ Engn Res Ctr Open Source Software & Real Time Syst Lanzhou 730000 Gansu Peoples R China

Effective estimation of brain network connectivity enables better unraveling of the extraordinary complexity interactions of brain regions and helps in auxiliary diagnosis of psychiatric disorders. Considering different modalities can provide comprehensive characterizations of brain connectivity, we propose the message-passing-based nonlinear network fusion (MP-NNF) algorithm to estimate multimodal brain network connectivity. In the proposed method, the initial functional and structural networks were computed from fMRI and DTI separately. Then, we update every unimodal network iteratively, making it more similar to the others in every iteration, and finally converge to one unified network. The estimated brain connectivities integrate complementary information from multiple modalities while preserving their original structure, by adding the strong connectivities present in unimodal brain networks and eliminating the weak connectivities. The effectiveness of the method was evaluated by applying the learned brain connectivity for the classification of major depressive disorder (MDD). Specifically, 82.18% classification accuracy was achieved even with the simple feature selection and classification pipeline, which significantly outperforms the competing methods. Exploration of brain connectivity contributed to MDD identification suggests that the proposed method not only improves the classification performance but also was sensitive to critical disease-related neuroimaging biomarkers.

关键词： Terms-Multimodal brain network connectivity nonlinear network fusion Major depressive disorder

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Impacts of Anthropogenic Noise on the House Wren’s Song: An XAI Approach to Bioacoustic Insights

Impacts of Anthropogenic Noise on the House Wren’s Song: An...

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Trustworthy, Explainable and Responsible computational intelligence (CITREx), ieee symposium on

作者： Larissa De Andrade Silva Juan G. Colonna Bernardo B. Gatto João Marcelo Protázio ICOMP - Institute of Computing Federal University of Amazonas Brazil MTI - Ltd Laboratory of Mathematical-Statistical Modeling (LAMEM) ICEN - UFPA Brazil

ISBN: (数字)9798331520151

ISBN: (纸本)9798331520168

This paper presents an automated explainable AI (XAI) method for characterizing variations in the calls of the House Wren (Troglodytes aedon musculus) under the influence of anthropogenic noise. We propose an End-to-End (E2E) Deep Learning model utilizing the pre-trained Biophony CNN architecture alongside Kapre, a Keras Audio Preprocessor. The outputs are interpreted through SHAP (SHapley Additive exPlanations) values, enabling the identification of fine-grained bioacoustic changes in vocalizations that differ between urban and rural environments. Our contributions include (i) the application of SHAP to bioacoustics data to interpret the impact of environmental noise on Wren’s vocalizations and (ii) novel visualizations using SHAP values, including the SHAP Spectrogram, SHAP Spectrum, and SHAP Time. These methods provide a transparent and detailed understanding of how urban noise affects bird vocalizations, bridging gaps in traditional statistical and signal processing approaches and offering a foundation for future studies in computational bioacoustics.

关键词： Deep learning Time-frequency analysis Explainable AI Biological system modeling computational modeling Noise Data visualization Predictive models bioinformatics Spectrogram

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Artificial Neural Networks for the Rapid Prediction of Possible Ferroptosis Inducers Using the GPx4 Enzyme

Artificial Neural Networks for the Rapid Prediction of Possi...

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

作者： Jesus Antonio Alvarado-Huayhuaz Miquéias Amorim Santos Silva Elvis Mauricio Jimenez Peña Joel Claudio Rengifo Maraví Angella M. Cordova-Muñoz Karina dos Santos Machado Facultad de Ciencias Universidad Nacional de Ingeniería Lima Peru Centro de Ciências Computacionais Universidade Federal do Rio Grande Rio Grande Brazil Departamento de Fisicoquímica Universidad Nacional Mayor de San Marcos Lima Peru Departamento de Química Universidad Nacional Agraria La Molina Lima Peru Departamento de Farmacología Universidad Nacional de Trujillo Trujillo Peru

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Cancer affects 20 million people worldwide; therefore, accelerated and urgent pathways are needed for the search for new drugs. The reactivation of cell death induced by the accumulation of iron-dependent lipid peroxides (Ferroptosis) is a potential mechanism for cancer treatment. One pathway to trigger Ferroptosis involves the inactivation of the glutathione-dependent peroxidase (GPx4) protein. Molecules with high affinity for iron are also being studied against cancer through chelation therapy. Therefore, the rapid detection of such molecules in molecular databases is of interest. Catechols exhibit a high iron-chelating power and can be synthesized by microorganisms as a chemical strategy for acquiring Fe 3+ . They can also be found in natural products. Recently, the natural product database in Peru (PeruNPDB) has been published where we found catechols; however, these have not yet been studied in GPx4. To expedite the search for catechols with affinity for the active site of GPx4, in large databases, we have developed a prediction model for scoring based on artificial neural networks. Our initial database consisted of the 83 catechols and 1,024 molecular descriptors. The target is the docking score obtained by ligand-receptor GPx4 coupling with the Autodock Vina software. Emphasizing the application of recursive feature elimination to improve the model’s performance and provides both the model and the procedure to be applied and improved by the scientific community. Our program enables the experimenter to rapidly filter molecules with high affinity specifically for the active site of GPx4, in the search for potential inhibitors.

关键词： Training Databases computational modeling Cancer treatment Artificial neural networks Predictive models Software Compounds Cancer Software development management

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An Extensive Examination of Discovering 5-Methylcytosine Sites in Genome-Wide DNA Promoters Using Machine Learning Based Approaches

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第1期19卷 87-94页

作者： Nguyen, Trinh-Trung-Duong The-Anh Tran Le, Nguyen-Quoc-Khanh Dinh-Minh Pham Ou, Yu-Yen Yuan Ze Univ Dept Comp Sci & Engn Chungli 32003 Taiwan Viettel Cyber Secur Hanoi 550000 Vietnam Taipei Med Univ Coll Med Artificial Intelligence Med Taipei 106 Taiwan Taipei Med Univ Res Ctr Artificial Intelligence Med Taipei 106 Taiwan Vietnam Acad Sci & Technol Inst Biotechnol Hanoi 100000 Vietnam

It is well-known that the major reason for the rapid proliferation of cancer cells are the hypomethylation of the whole cancer genome and the hypermethylation of the promoter of particular tumor suppressor genes. Locating 5-methylcytosine (5mC) sites in promoters is therefore a crucial step in further understanding of the relationship between promoter methylation and the regulation of mRNA gene expression. High throughput identification of DNA 5mC in wet lab is still time-consuming and labor-extensive. Thus, finding the 5mC site of genome-wide DNA promoters is still an important task. We compared the effectiveness of the most popular and strong machine learning techniques namely XGBoost, Random Forest, Deep Forest, and Deep Feedforward Neural Network in predicting the 5mC sites of genome-wide DNA promoters. A feature extraction method based on k-mers embeddings learned from a language model were also applied. Overall, the performance of all the surveyed models surpassed deep learning models of the latest studies on the same dataset employing other encoding scheme. Furthermore, the best model achieved AUC scores of 0.962 on both cross-validation and independent test data. We concluded that our approach was efficient for identifying 5mC sites of promoters with high performance.

关键词： Natural language processing k-mers embedding DNA 5-methylcytosine site prediction promoter advanced machine learning classifiers

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Use Chou's 5-Steps Rule With Different Word Embedding Types to Boost Performance of Electron Transport Protein Prediction Model

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第2期19卷 1235-1244页

作者： Nguyen, Trinh-Trung-Duong Ho, Quang-Thai Le, Nguyen-Quoc Khanh Dinh-Phan, Van Ou, Yu-Yen Yuan Ze Univ Dept Comp Sci & Engn Chungli 32003 Taiwan Taipei Med Univ Coll Med Profess Master Program Artificial Intelligence Me Taipei 106 Taiwan Taipei Med Univ Res Ctr Artificial Intelligence Med Taipei 106 Taiwan Univ Danang Univ Econ Da Nang 550000 Vietnam

Living organisms receive necessary energy substances directly from cellular respiration. The completion of electron storage and transportation requires the process of cellular respiration with the aid of electron transport chains. Therefore, the work of deciphering electron transport proteins is inevitably needed. The identification of these proteins with high performance has a prompt dependence on the choice of methods for feature extraction and machine learning algorithm. In this study, protein sequences served as natural language sentences comprising words. The nominated word embedding-based feature sets, hinged on the word embedding modulation and protein motif frequencies, were useful for feature choosing. Five word embedding types and a variety of conjoint features were examined for such feature selection. The support vector machine algorithm consequentially was employed to perform classification. The performance statistics within the 5-fold cross-validation including average accuracy, specificity, sensitivity, as well as MCC rates surpass 0.95. Such metrics in the independent test are 96.82, 97.16, 95.76 percent, and 0.9, respectively. Compared to state-of-the-art predictors, the proposed method can generate more preferable performance above all metrics indicating the effectiveness of the proposed method in determining electron transport proteins. Furthermore, this study reveals insights about the applicability of various word embeddings for understanding surveyed sequences.

关键词： Proteins Natural language processing Feature extraction Predictive models Two dimensional displays Tools Prediction algorithms Electron transport protein machine learning natural language processing support vector machine word embedding

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An Analytical Paradigm for Exploration of Diabetes Using Machine Learning 19

An Analytical Paradigm for Exploration of Diabetes Using Mac...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Chauhan, Ritu Mishra, Aarushi Mani, Ruchi Jakhmola Yafi, Eiad Zuhairi, Megat F. Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University UP India Proteomics and Translational Research Lab Center for Medical Biotechnology Amity University UP India Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Malaysian Institute of Information Technology University of Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (纸本)9798331507817

Machine Learning is an approach in AI which has swiftly emerged as a transformative and powerful tool in the healthcare industry, changing the methods of diagnosing and predicting diseases. In the present situation, the application of different machine learning methods, including deep learning, ensemble techniques, and natural language processing, across various healthcare activities, such as disease prediction, medical imaging analysis, drug discovery, and tailored treatment suggestions, has offered encouraging prospects for improved diagnostics, treatment, and patient results. By utilizing a diverse range of data sources, including electronic health records, medical imaging, and wearable devices, machine learning algorithms have facilitated early detection, precise prognostication, and customized interventions. The application of machine learning algorithms into health databases is the ongoing area of research concerning future modelling predictions. The current study revolves around the utilization of descriptive analytic methods and machine learning algorithms to predict if an individual is diabetic. The effectiveness of four distinct models of machine learning, specifically Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM) and K Nearest Neighbor (KNN), is assessed and contrasted. The respective accuracies of these classifiers are noted as 89.83%, 77.16%, 84.84%, 81.16%. © 2025 ieee.

关键词： Electronic health record

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Mental Health Diagnostics Using Machine Learning: A Clustering and Predictive Modeling Approach 19

Mental Health Diagnostics Using Machine Learning: A Clusteri...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Chauhan, Ritu Mishra, Aarushi Jena, Mehak Yafi, Eiad Zuhairi, Megat F. Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University UP India Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur50250 Malaysia

ISBN: (纸本)9798331507817

The application of machine learning has rapidly advanced as a transformative approach in healthcare, offering significant potential in the analysis of huge amounts of acquired data on Mental health across the world enhancing the treatment of mental health issues. Machine learning algorithms are being utilized to analyse patterns and trends in mental health datasets. This study investigates various machine learning methodologies and descriptive analytics techniques to detect patterns and trends in mental health-related factors, focusing on predicting the likelihood of individuals seeking mental health treatment. We utilized clustering, association rule mining, feature selection, and machine learning classifiers such as Random Forest, Decision Tree, XGBoost, and Logistic Regression. By applying these techniques to a dataset of mental health factors, we identified key predictors influencing mental health outcomes and developed models to enhance predictive accuracy. Our analysis highlighted the effectiveness of machine learning algorithms in extracting meaningful patterns from the dataset, with Random Forest and XGBoost emerging as particularly strong performers with accuracies of 81% and 84% . Other applied algorithms that are Decision Tree, Gradient Boosting and logistic regression have the accuracy of 81%, 77%, and 73% respectively. © 2025 ieee.

关键词： Electronic health record

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Sorting Signed Permutations by Inverse Tandem Duplication Random Losses

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第6期18卷 2177-2188页

作者： Hartmann, Tom Bannach, Max Middendorf, Martin Univ Leipzig Fac Math & Comp Sci Swarm Intelligence & Complex Syst Grp Augustuspl 10 D-04109 Leipzig Germany Univ Lubeck Inst Theoret Comp Sci D-23562 Lubeck Germany

Gene order evolution of unichromosomal genomes, for example mitochondrial genomes, has been modelled mostly by four major types of genome rearrangements: inversions, transpositions, inverse transpositions, and tandem duplication random losses. Generalizing models that include all those rearrangements while admitting computational tractability are rare. In this paper, we study such a rearrangement model, namely the inverse tandem duplication random loss (iTDRL) model, where an iTDRL duplicates and inverts a continuous segment of a gene order followed by the random loss of one of the redundant copies of each gene. The iTDRL rearrangement has currently been proposed by several authors suggesting it to be a possible mechanisms of mitochondrial gene order evolution. We initiate the algorithmic study of this new model of genome rearrangement by proving that a shortest rearrangement scenario that transforms one given gene order into another given gene order can be obtained in quasilinear time. Furthermore, we show that the length of such a scenario, i.e., the minimum number of iTDRLs in the transformation, can be computed in linear time.

关键词： Sorting Genomics computational modeling Approximation algorithms Biological cells Evolution (biology) Biological system modeling Inverse tandem duplication random loss mitochondria genome rearrangements sorting problem distance problem

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