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Machine Learning-Enhanced Molecular Dynamics Analysis of Residue Variant Effects on Human ACE2 and Furin Protein Interactions with SARS-CoV-2 Spike

Machine Learning-Enhanced Molecular Dynamics Analysis of Res...

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

作者： Felipe James de Almeida Vasquez Arthur Scorsolini Fares Silvana Giuliatti Department of Genetics Faculty of Medicine of Ribeirão Preto University of São Paulo São Paulo Ribeirão Preto

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

The interaction between the Spike glycoprotein and its host cell receptors ACE2 and Furin determines the replication rates of SARS-CoV-2. Given the emergence of variants, the exact mechanisms of these interactions are not yet fully understood. This study utilizes molecular modeling, protein interaction, and molecular dynamics (MD) simulations to investigate the stability and interactions of the Wild-type (WT) and variants of ACE2-Spike-Furin complexes. MD simulations produce a vast amount of data that can be challenging to interpret. Machine learning (ML) techniques were used to reduce the complexity of the data and assess the impact of residues in these interactions. The aim is to better understand the impact of residues on these interactions, with a specific focus on the interaction region between Spike and Furin. The results demonstrate that the modeled complexes remained stable and provided valuable data for ML models. The Random Forest model proves particularly effective in identifying significant residues in each variant, revealing 500,000 residues pairs involved in the Spike-Furin interaction. These results highlight the importance of not only the active interaction sites but also the subtle interactions that are distant from the active site. By utilizing ML, critical residues can be identified, potentially leading to targets for therapeutic interventions.

关键词： Proteins Data models Stability analysis Coronaviruses Complexity theory bioinformatics Random forests computational intelligence computational biology

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EURYALE: A versatile Nextflow pipeline for taxonomic classification and functional annotation of metagenomics data

EURYALE: A versatile Nextflow pipeline for taxonomic classif...

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

作者： João Vitor F. Cavalcante Iara Dantas de Souza Diego A. A. Morais Rodrigo J. S. Dalmolin Bioinformatics Multidisciplinary Environment Federal University of Rio Grande do Norte Natal Brazil Department of Biochemistry Federal University of Rio Grande do Norte Natal Brazil

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

EURYALE is a Nextflow pipeline designed for the sensitive taxonomic classification and flexible functional annotation of metagenomic shotgun sequences. It provides a comprehensive solution for preprocessing, assembly, alignment, taxonomic classification, and functional annotation of metagenomic data. EURYALE builds upon the Snakemake-based pipeline MEDUSA. EURYALE inherits the tools present in MEDUSA, selected based on rigorous benchmarks for performance, accuracy, and sensitivity. The new pipeline has been developed with the nf-core pipeline template, which focuses on modularity, allowing a high degree of parameterization. EURYALE provides easier resource management, enforcing strict memory and CPU requirements based on its Nextflow configuration. It has also become more versatile, as it can be executed using Docker and Singularity, which further extends its usability across various platforms. It can also natively take advantage of computational infrastructures such as SLURM and Amazon Web Services. EURYALE inherits the sensitivity in taxonomic classification and flexibility in functional annotation of its predecessor, combined with improved versatility.

关键词： Metagenomics Schedules Sensitivity Data analysis Annotations Web services Pipelines Memory management Resource management Usability

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Welcome to ieee CIBCB 2023

CIBCB 2023 - 20th IEEE Conference on Computational Intellige...

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CIBCB 2023 - 20th ieee Conference on computational intelligence in bioinformatics and computational biology 2023年

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Potential circRNA-Disease Association Prediction Using DeepWalk and Nonnegative Matrix Factorization

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第5期20卷 3154-3162页

作者： Qiao, Li-Juan Gao, Zhen Ji, Cun-Mei Liu, Zhi-Hao Zheng, Chun-Hou Wang, Yu-Tian Qufu Normal Univ Sch Cyber Sci & Engn Qufu 273165 Peoples R China Anhui Univ Sch Artifial Intelligence Hefei 230601 Peoples R China

Circular RNAs (circRNAs) are a category of noncoding RNAs that exist in great numbers in eukaryotes. They have recently been discovered to be crucial in the growth of tumors. Therefore, it is important to explore the association of circRNAs with disease. This paper proposes a new method based on DeepWalk and nonnegative matrix factorization (DWNMF) to predict circRNA-disease association. Based on the known circRNA-disease association, we calculate the topological similarity of circRNA and disease via the DeepWalk-based method to learn the node features on the association network. Next, the functional similarity of the circRNAs and the semantic similarity of the diseases are fused with their respective topological similarities at different scales. Then, we use the improved weighted K-nearest neighbor (IWKNN) method to preprocess the circRNA-disease association network and correct nonnegative associations by setting different parameters K-1 and K-2 in the circRNA and disease matrices. Finally, the L-2,L-1-norm, dual-graph regularization term and Frobenius norm regularization term are introduced into the nonnegative matrix factorization model to predict the circRNA-disease correlation. We perform cross-validation on circR2Disease, circRNADisease, and MNDR. The numerical results show that DWNMF is an efficient tool for forecasting potential circRNA-disease relationships, outperforming other state-of-the-art approaches in terms of predictive performance.

关键词： CircRNA-disease association circular RNAs DeepWalk disease nonnegative matrix factorization

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Semi-Supervised Deep Learning for Cell Type Identification From Single-Cell Transcriptomic Data

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第2期20卷 1492-1505页

作者： Dong, Xishuang Chowdhury, Shanta Victor, Uboho Li, Xiangfang Qian, Lijun Texas A&M Univ Syst Prairie View A&M Univ Ctr Excellence Res Educ Big Mil Data Intelligence Prairie View TX 77446 USA Texas A&M Univ Syst Prairie View A&M Univ CCSB Dept Elect & Comp Engn Prairie View TX 77446 USA

Cell type identification from single-cell transcriptomic data is a common goal of single-cell RNA sequencing (scRNAseq) data analysis. Deep neural networks have been employed to identify cell types from scRNAseq data with high performance. However, it requires a large mount of individual cells with accurate and unbiased annotated types to train the identification models. Unfortunately, labeling the scRNAseq data is cumbersome and time-consuming as it involves manual inspection of marker genes. To overcome this challenge, we propose a semi-supervised learning model "SemiRNet" to use unlabeled scRNAseq cells and a limited amount of labeled scRNAseq cells to implement cell identification. The proposed model is based on recurrent convolutional neural networks (RCNN), which includes a shared network, a supervised network and an unsupervised network. The proposed model is evaluated on two large scale single-cell transcriptomic datasets. It is observed that the proposed model is able to achieve encouraging performance by learning on the very limited amount of labeled scRNAseq cells together with a large number of unlabeled scRNAseq cells.

关键词： Sequential analysis Gene expression Computer architecture Recurrent neural networks Microprocessors Task analysis Training Single-cell sequencing semi-supervised learning recurrent convolutional neural networks joint optimization

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Evaluating the Segmentation Performance of Gross Volume Tumor in Cervical Cancer Using MRI Images

Evaluating the Segmentation Performance of Gross Volume Tumo...

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

作者： Nazar Zaki Anusuya Krishnan Rafat Damseh Wenjian Qin Ayham Zaitouny Isaias Ghebrehiwet College of IT United Arab Emirates University Al Ain UAE Shenzhen Institute of Advanced Technology Chinese Academy of Science China College of Science United Arab Emirates University Al Ain UAE

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Cervical Cancer (CC) is the most prevalent gynecologic malignancy worldwide. Tumor segmentation in CC is a crucial step for radiotherapy treatment and planning. The clinical practice involves laborious slice-by-slice segmentation of the primary tumor using simultaneous assessments from several image modalities, and ignores spatial ambiguity in tumor delineation. This work evaluates the performance of a state-of-the-art Landing AI for automated 3D medical image segmentation applied to Gross Tumor Volume (GTV) in CC from Magnetic Resonance Imaging (MRI) scans. Our work provides a novel in-house labeled dataset with a systematic assessment of the segmented lesions after network training on various voxel spacing MRI images. The segmentation performance was assessed using the dice coefficient. We demonstrated that training on MRI images to optimize Landing AI achieves an improved dice score of 0.92, outperforming other MRI models.

关键词： Training Image segmentation Three-dimensional displays Systematics Magnetic resonance imaging Radiation therapy Planning Lesions Artificial intelligence Cervical cancer

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Investigating Fairness with FanFAIR: is Pre-processing Useful Only for Performances?

Investigating Fairness with FanFAIR: is Pre-processing Usefu...

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2025 ieee symposium on computational intelligence in Health and Medicine, CIHM 2025

作者： Rispoli, Michele Nobile, Marco S. Manzoni, Luca D’Onofrio, Alberto Confalonieri, Marco Salton, Francesco Confalonieri, Paola Ruaro, Barbara Gallese, Chiara Department of Mathematics Informatics and Geosciences University of Trieste Italy Department of Environmental Sciences Informatics and Statistics Ca’ Foscari University of Venice Italy Pulmonology Unit University Hospital of Cattinara Trieste Italy Department of Medical Surgical and Health Sciences University of Trieste Italy Department of Law University of Turin Italy Bicocca Bioinformatics Biostatistics and Bioimaging research center Monza Italy

ISBN: (纸本)9798331508333

Artificial intelligence, and Machine Learning systems in general, are becoming pervasive in our society, from the industry to the public administration. AI can often provide a very efficient means to support decision-making, but it can represent a danger for high-risk applications such as bio-medicine and healthcare. In particular, biased datasets might lead to inaccurate or discriminatory ML systems, undermining the accuracy of their predictions and putting patients’ health at risk. FanFAIR is a python tool that provides the community with a semiautomatic tool for datasets’ fairness assessment. FanFAIR is designed to integrate qualitative considerations – such as ethics, human rights assessment, and data protection – with quantitative indicators of dataset’s fairness, such as balance, the presence of invalid entries, or outliers. In this work, we extend FanFAIR to deal with categorical data, and introduce a new algorithm for outlier detection in the presence of missing values. We then provide a case study on the data collected from COVID patients admitted to pneumology departments in Italy. We show how the successive steps of data cleaning and variable selection improve the indicators provided by FanFAIR. This shows that data cleaning procedures are not only necessary to improve the performance of the machine learning algorithm using the data for learning, but are also a way to improve (a measure of) fairness. Hence, the proposed case study provides an example in which performance and fairness are not in contrast, like it is commonly believed to be, but they improve together. © 2025 ieee.

关键词： Health risks

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Neural Architecture Search for Genomic Sequence Data

Neural Architecture Search for Genomic Sequence Data

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

作者： Amadeu Scheppach Hüseyin Anil Gündüz Emilio Dorigatti Philipp C. Münch Alice C. McHardy Bernd Bischl Mina Rezaei Martin Binder Department of Statistics LMU Munich Munich Germany Department of Statistics LMU Munich and Munich Center for Machine Learning Munich Germany Department for Computational Biology of Infection Research Helmholtz Centre for Infection Research Braunschweig Germany

Deep learning has enabled outstanding progress on bioinformatics datasets and a variety of tasks, such as protein structure prediction, identification of regulatory regions, genome annotation, and interpretation of the noncoding genome. The layout and configuration of neural networks used for these tasks have mostly been developed manually by human experts, which is a time-consuming and error-prone process. Therefore, there is growing interest in automated neural architecture search (NAS) methods in bioinformatics. In this paper, we present a novel search space for NAS algorithms that operate on genome data, thus creating extensions for existing NAS algorithms for sequence data that we name Genome-DARTS, Genome-P-DARTS, Genome-BONAS, Genome-SH, and Genome-RS. Moreover, we introduce two novel NAS algorithms, CWP-DARTS and EDPDARTS, that build on and extend the idea of P-DARTS. We evaluate the presented methods and compare them to manually designed neural architectures on a widely used genome sequence machine learning task to show that NAS methods can be adapted well for bioinformatics sequence datasets. Our experiments show that architectures optimized by our NAS methods outperform manually developed architectures while having significantly fewer parameters.

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DIVERSE: Bayesian Data IntegratiVE Learning for Precise Drug ResponSE Prediction

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第4期19卷 2197-2207页

作者： Paltun, Betul Guvenc Kaski, Samuel Mamitsuka, Hiroshi Aalto Univ Dept Comp Sci Espoo 02150 Finland Univ Manchester Dept Comp Sci Manchester M13 9PL Lancs England Kyoto Univ Bioinformat Ctr Inst Chem Res Uji 6110011 Japan

Detecting predictive biomarkers from multi-omics data is important for precision medicine, to improve diagnostics of complex diseases and for better treatments. This needs substantial experimental efforts that are made difficult by the heterogeneity of cell lines and huge cost. An effective solution is to build a computational model over the diverse omics data, including genomic, molecular, and environmental information. However, choosing informative and reliable data sources from among the different types of data is a challenging problem. We propose DIVERSE, a framework of Bayesian importance-weighted tri- and bi-matrix factorization(DIVERSE3 or DIVERSE2) to predict drug responses from data of cell lines, drugs, and gene interactions. DIVERSE integrates the data sources systematically, in a step-wise manner, examining the importance of each added data set in turn. More specifically, we sequentially integrate five different data sets, which have not all been combined in earlier bioinformatic methods for predicting drug responses. Empirical experiments show that DIVERSE clearly outperformed five other methods including three state-of-the-art approaches, under cross-validation, particularly in out-of-matrix prediction, which is closer to the setting of real use cases and more challenging than simpler in-matrix prediction. Additionally, case studies for discovering new drugs further confirmed the performance advantage of DIVERSE.

关键词： Drugs Cancer bioinformatics Data models Bayes methods Chemicals Proteins Personalized medicine drug response prediction machine learning Bayesian methods data integration

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A Machine Learning algorithm for Predicting Translation Initiation Start Positions

A Machine Learning algorithm for Predicting Translation Init...

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

作者： Stefanos Digenis Dimitris Grigoriadis Marios Miliotis Artemis G. Hatzigeorgiou Hellenic Pasteur Institute Department of Computer Science and Biomedical Informatics University of Thessaly Greece

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Translation Initiation Start (TIS) sites are specific regions in genes, where the protein synthesis starts. In this study, the specific problem of RNA sequence classification is addressed through the deployment of convolutional neural networks (CNNs) employing features extracted from genomic sequences. Translation initiation is a crucial step in protein synthesis, indicating the sites where the translation mechanism takes place and such, accurate prediction of its start sites is a significant step towards deeper understanding and harnessing of its complex characteristics. To this aim, a state-of-the-art deep learning model has been developed which predicts the presence/absence of translation initiation start sites within RNA sequences. The proposed deep learning model leverages the power of CNNs to effectively capture relevant features in RNA sequences. Trained on a comprehensive dataset of manually annotated translation initiation sites, the model learns to discern key patterns and motifs associated with initiation starts. Through rigorous evaluation, a model comprising sequence, conservation and hexamer features exhibits a remarkably high accuracy rate of 93.75%. This research holds promise for various biological applications, including gene annotation, drug target identification, and understanding the mechanisms of protein synthesis. The ability to reliably predict translation initiation start sites using deep learning provides a valuable tool for computational biologists and experimental researchers alike.

关键词： Deep learning Proteins Accuracy RNA computational modeling Biological system modeling Genomics Predictive models Feature extraction bioinformatics

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