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检索条件"任意字段=IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology"

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SeqFRT: Towards Effective Adaption of Foundation Model via Sequence Feature Reconstruction in computational Pathology

SeqFRT: Towards Effective Adaption of Foundation Model via S...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Chengfei Cai Jun Li Mingxin Liu Yiping Jiao Jun Xu School of Automation Nanjing University of Information Science and Technology China College of Information Engineering Taizhou University China Institute for AI in Medicine School of Artificial Intelligence Nanjing University of Information Science and Technology China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Given the intricate situation of modelling gigapixel images, the usage of multiple instance learning (MIL) framework has recently increased to support clinical practice, encompassing cancer diagnosis, subtyping, survival prediction and other tasks. In current practice, most state-of-the-art MIL proposals typically apply a frozen pre-trained CNN or a pathological foundation model for feature extraction. While this paradigm lacks the capability for sequence feature fine-tuning within the downstream-specific tasks, which hinders the continuous performance promotion in Whole Slide Images (WSIs) Analysis. To address this issue, we propose a Sequence Feature Reconstruction Transformer (SeqFRT) for optimizing feature extraction of the foundation model, which can capture more discriminative features within pathological instance sequences. The proposed model comprises three main modules: 1) an offline foundation model as the pathological feature extractor; 2) a sequence position optimization architecture which aims at refining the correlations between instances in both sequential ordering and transpositional ordering; 3) a sequence sparsity enhancement strategy is designed to reconstruct the sequence feature and extract the latent representations instead of redundant information. Extensive experiments on six benchmark datasets for three computational pathology tasks demonstrated our model’s superiority over the state-of-the-art MIL methods. The source code is available at https://***/caicai2526/SeqFRT-MIL.

关键词： Pathology Visualization Foundation models computational modeling Benchmark testing Feature extraction Transformers Data mining Image reconstruction Cancer

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Artificial intelligence Models for the Automating Papanicolaou Test Reading: A Systematic Review Toward Understanding Clinical Application 18th

Artificial Intelligence Models for the Automating Papanicola...

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18th International conference on Practical Applications of computational biology and bioinformatics, PACBB 2024

作者： Andrés, Cardona-Mendoza Ana-Belén, Gil-González Díaz-Báez, David Sandra, Perdomo-Lara Alejandro, Ramos-Casallas Cellular and Molecular Immunology Group-INMUBO El Bosque University Bogotá Colombia BISITE Research Group University of Salamanca Salamanca37007 Spain Unidad de Investigación Básica Oral-UIBO El Bosque University Bogotá Colombia

ISBN: (纸本)9783031878725

Cervical cancer, a cancer prevalent among women worldwide, can be managed with early detection and diagnosis. This paper, leveraging the advancements in artificial intelligence (AI) techniques, presents a systematic literature review (SLR) for the application of Deep Learning (DL) and Machine Learning (ML) models for segmenting and classifying single cells and whole-slide imaging (WSI) in Papanicolaou (Pap) tests. The study underscores the use of Convolutional Neural Networks (CNN) for feature extraction and classification, as well as other ML models such as support vector machines (SVM) and Random Forests for classification. However, the focus of most studies on segmenting and classifying individual cervical cells neglects the crucial aspect of whole-slide image (WSI) classification, primarily because of the scarcity of accessible datasets. The study concludes that the number of studies proposing methodological approaches for automated Pap test reading has significantly increased since 2019, underscoring the need for robust solutions to enhance the sensitivity and efficiency of the Pap test, thereby leading to improved rates of early detection of cervical cancer. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Tensor decomposition to identify context-aware spatial neighborhoods in the tumor microenvironment

Tensor decomposition to identify context-aware spatial neigh...

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Annual International conference of the ieee Engineering in Medicine and biology Society (EMBC)

作者： Reva Kulkarni Gauri Patel Joel Eliason Arvind Rao Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA Department of Biomedical Engineering University of Michigan Ann Arbor MI USA Department of Computational Medicine and Bioinformatics Department of Radiation Oncology University of Michigan Ann Arbor MI USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The tumor microenvironment (TME) is a complex ecosystem composed of diverse cell types whose spatial interactions play an important role in tumor progression. Many cell types have characteristic biomarkers that can be detected at single cell resolution through multiplex tissue imaging methods. There exist several image analysis tools to quantify such interactions, but they calculate summary statistics one image at a time. In this study, we use a scalable canonical polyadic (CP) tensor decomposition method to probe the spatial heterogeneity of cell type distributions simultaneously across samples. We use a publicly available colorectal cancer (CRC) dataset capturing 56 cell-surface markers from 35 patients across 136 images. Through analysis of tensor decomposition factors, we are able to identify tensor components corresponding to key cellular neighborhoods, such as bulk tumor and tumor edge. Wilcoxon rank sum tests on nine component decomposition identified components that varied significantly across Crohn’s-like reaction (CLR) and diffuse inflammatory infiltration (DII) patient groups, including granulocyte enrichment in DII. Survival analysis identified distinct components associated with high tumor level and poor patient prognosis.

关键词： Multiplexing Tensors Image analysis Image edge detection Imaging Spatial resolution Prognostics and health management Probes Engineering in medicine and biology Tumors

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Using Optimal Transport to Improve Spherical Harmonic Quantification of Complex Biological Shapes

Using Optimal Transport to Improve Spherical Harmonic Quanti...

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2022 ieee International conference on bioinformatics and Biomedicine, BIBM 2022

作者： Wang, Zexuan Yang, Wenxi Ryan, Katharine Garai, Sumita Auerbach, Benjamin M Shen, Li University of Pennsylvania Applied Mathematics and Computational Science Philadelphia United States Sacred Heart University Fairfield Connecticut Department of Biology United States University of Pennsylvania Department of Biostatistics Epidemiology & Informatics Philadelphia United States The University of Tennessee Department of Anthropology KnoxvilleTN United States Department of Ecology and Evolutionary Biology The University of Tennessee Knoxville United States

ISBN: (纸本)9781665468190

The knowledge of the anatomical shape of both gross and microscopic structures is the key to understanding the effects of disease processes on cellular structure. Geometric morphometric methods, such as Procrustes superimposition, and Spherical Harmonics (SPHARM), have been used to capture the biological shape variation and group differences in morphology. Previous SPHARM-MAT techniques use the CALD algorithm to parameterize the mesh surface. It starts from initial mapping and performs local and global smoothing methods alternately to control the area and length distortions simultaneously. However, this parameterization may not be sufficient in complex morphological cases. To bridge this gap, we propose SPHARM-OT, an enhanced SPHARM surface modeling method using optimal transport (OT) for spherical parameterization. First, the genus 0 3D objects are conformally mapped onto a sphere. Then the optimal transport theory via spherical power diagram is introduced to minimize the area distortion. This new algorithm can effectively reduce the area distortion and lead to a better reconstruction result. We demonstrate the effectiveness of the method by applying it to the human sphenoidal paranasal sinuses. © 2022 ieee.

关键词： Principal component analysis

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A mathematical model of the innate immunological response to Brucella infection

A mathematical model of the innate immunological response to...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： João Víctor Costa De Oliveira Carlos Cristiano Hasenclever Borges Leonardo Augusto De Almeida Marcelo Lobosco Graduate Program on Computational Modeling Federal University of Juiz de Fora Juiz de Fora Brazil Laboratory of Molecular Biology of Microorganisms Federal University of Alfenas Alfenas Brazil

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Brucellosis is a highly contagious zoonotic disease caused by bacteria from the genus Brucella. It affects various animal species, with significant economic consequences for live-stock producers. The disease causes reproductive losses, such as abortions and reduced fertility, leading to decreased live-stock production. Additionally, infected animals may experience reduced milk production and weight gain, further impacting the economic viability of livestock operations. Furthermore, the potential for zoonotic transmission to humans poses risks to public health. In this study, we propose a new mathematical model that describes the innate immune response to brucellosis, focusing on the interactions between antigens and the innate immune system. The model is formulated as a system of five partial differential equations that represent healthy tissue, bacteria, cytokines, resting and mature antigen-presenting cells, and is solved using the finite difference method. To validate our model, we compare the initial numerical results with experimental data from the literature, achieving an absolute error of 0.0017.

关键词： Economics computational modeling Biological system modeling Partial differential equations Production Bacteria Mathematical models Numerical models Public healthcare Diseases

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Enhancing Dental Implant Risk Prediction with an Interpretable Multi-Instance Learning Model

Enhancing Dental Implant Risk Prediction with an Interpretab...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Yuqian Liu Almonzer Salah Nooraldaim Zhengfa Xue Jiayin Wang School of Computer Science and Technology Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Variations in clinical and biological factors often lead to differing risks of dental implant failure among patients, even when undergoing similar procedures. Traditional predictive models often oversimplify outcomes into binary classifications, lacking the interpretability needed for accurate risk assessment and effective patient stratification. To address this challenge, this paper presents a novel multi-instance learning (MIL) framework that incorporates a Hosmer-Lemeshow-based loss function for implant failure risk assessment based on patient-specific clinical features. The framework also identifies the optimal threshold for key features, such as bone density, to enable robust patient stratification. The effectiveness label for each patient was constructed first and sampled patients into 600 and 1,000 groups. Results demonstrate that the proposed framework captures inter-patient variability with improved statistical calibration and enhanced risk stratification, offering practical insights to guide clinical decision-making. This study highlights the scalability and versatility of the proposed framework, bridging computational methodologies and practical applications in personalized implantology. Furthermore, the approach provides a transparent and effective tool for risk assessment, with potential applications in broader clinical stratification problems.

关键词： Accuracy Scalability computational modeling Decision making Implants Predictive models biology Dentistry Calibration Risk management

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26th International conference on Research in computational Molecular biology, RECOMB 2022

26th International Conference on Research in Computational M...

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26th International conference on Research in computational Molecular biology, RECOMB 2022

ISBN: (纸本)9783031047480

The proceedings contain 40 papers. The special focus in this conference is on Research in computational Molecular biology. The topics include: NetMix2: Unifying Network Propagation and Altered Subnetworks;multi-modal Genotype and Phenotype Mutual Learning to Enhance Single-Modal Input Based Longitudinal Outcome Prediction;Fast, Flexible, and Exact Minimum Flow Decompositions via ILP;co-linear Chaining with Overlaps and Gap Costs;the Complexity of Approximate Pattern Matching on de Bruijn Graphs;proTranslator: Zero-Shot Protein Function Prediction Using Textual Description;single-Cell Multi-omic Velocity Infers Dynamic and Decoupled Gene Regulation;ultrafast and Interpretable Single-Cell 3D Genome Analysis with Fast-Higashi;semi-supervised Single-Cell Cross-modality Translation Using Polarbear;diffDomain Enables Identification of Structurally Reorganized Topologically Associating Domains;joint Inference of Repeated Evolutionary Trajectories and Patterns of Clonal Exclusivity or Co-occurrence from Tumor Mutation Trees;fast and Optimal Sequence-to-Graph Alignment Guided by Seeds;CLMB: Deep Contrastive Learning for Robust Metagenomic Binning;Unsupervised Cell Functional Annotation for Single-Cell RNA-Seq;a Novel Matrix Factorization Model for Interpreting Single-Cell Gene Expression from Biologically Heterogeneous Data;tractable and Expressive Generative Models of Genetic Variation Data;Concert: Genome-Wide Prediction of Sequence Elements That Modulate DNA Replication Timing;CORSID Enables de novo Identification of Transcription Regulatory Sequences and Genes in Coronaviruses;Learning Probabilistic Protein-DNA Recognition Codes from DNA-Binding Specificities Using Structural Mappings;transcription Factor-Centric Approach to Identify Non-recurring Putative Regulatory Drivers in Cancer;uncertainty Quantification Using Subsampling for Assembly-Free Estimates of Genomic Distance and Phylogenetic Relationships;SOPHIE: Viral Outbreak Investigation and Transmission History Rec

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Implementation of Grey Scale Normalization in Machine Learning & Artificial intelligence for bioinformatics using Convolutional Neural Networks 6

Implementation of Grey Scale Normalization in Machine Learni...

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6th International conference on Inventive Computation Technologies (ICICT)

作者： Kothari, Divya Patel, Mayank Sharma, Ajay Kumar Geetanjali Inst Tech Studies Udaipur Rajasthan India

ISBN: (纸本)9781728185019

Machine Learning is a trending field nowadays and is very well known as an application of Artificial intelligence (AI). Machine learning makes use of secure arithmetical algorithms to construct computers assignment in a constructive way without being unequivocally programmed. The algorithms acquire freedom of a participated value and estimate output for this by utilizing definite arithmetical methods. The key function of machine learning is to create smart machines that can visualize and work related to human beings. Artificial intelligence has been witnessing a massive improvement in bridging the gap among the capabilities of humans and technologies. Similarly, one of the characteristics of the field were tried to combine the outstanding effects. A Convolution Neural Network (CNN) is a sort of Deep Learning algorithm which can obtain an input image, assign consequence to different aspects in the image and capable to differentiate one from the other. The pre-processing requirement in a CNN is lesser as compared to other classification algorithms. CNN has the capability to find out these filter's characteristics. bioinformatics is a term that is a mixture of two terms bio and informatics. Bio means associated with biology and informatics means in a sequence of information. Thus bioinformatics is an area that deals with handing out and accepting biological statistics using the computational and arithmetical approaches. Machine Learning has added up to of applications in the area of bioinformatics. Machine Learning finds its submission in the subsequent subfields of bioinformatics. The aim of this article is to perform a grayscale normalization of a selected image and thereafter to reduce the effect of illumination differences. Normalization is considered so that CNN works in a faster manner. Different models are available but Keras model is selected to perform this task. Keras supports the style of data preparation for image statistics via the Image Data Generator gro

关键词： Machine Learning Artificial intelligence Convolution Neural Network Application Program Interface

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NFB-Checker: An AI/ML-Powered Microorganism Nitrogen Fixation Susceptibility Prediction from Gene Collection

NFB-Checker: An AI/ML-Powered Microorganism Nitrogen Fixatio...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Tuyen Do Shiva Aryal Bichar Dip Shrestha Gurung Diing D.M. Agany Nick Klein Ruanbao Zhou Rajesh Sani Etienne Z. Gnimpieba Biomedical Engineering Department University of South Dakota Sioux Falls SD USA Math and Science Department Oglala Lakota College Kyle SD USA Department of Biology and Microbiology South Dakota State University Brookings SD USA Department of Chemical South Dakota School of Mines and Technology Rapid City SD USA Department of Biological Engineering South Dakota School of Mines and Technology Rapid City SD USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Nitrogen fixation is a crucial process involved in many aspects of the ecological life cycle. However, few organismsparticularly kingdom bacteria-are known to fix N 2 in a form that could be used in agribusiness. The ability to identify an organism as an N 2 fixer is a novel case for more identification and application. This study employs functional genes and an XGBoost machine learning model to predict an organism's ability to fix nitrogen, focusing on key orthologs like nifH, nifD, and nifK, essential in the nitrogenase enzyme complex. The model, trained on a dataset of 923 N 2 fixers and 981 non-fixers, achieved an accuracy of 92.6% and an F1 score of 0.92. This research highlights the potential of machine learning in identifying genetic markers for N 2 fixation, providing an efficient alternative to traditional methods, and offers new insights for ecological and agricultural research. The inclusion of specific orthologs enhances the model's predictive accuracy, demonstrating the importance of targeted genetic markers in computational biology.

关键词： Adaptation models Accuracy Microorganisms Biological system modeling computational modeling Scalability Machine learning Predictive models Genetics Nitrogen

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A Novel Co-evolutionary Particle Swarm Optimization Method for Biomarker Selection and Disease Classification

A Novel Co-evolutionary Particle Swarm Optimization Method f...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Tao Li Shunxi Zhang Haoyue Ma Jianyu Li Jiucheng Xu College of Computer and Information Engineering Henan Normal University Xinxiang China Engineering Lab of Intelligence Business & Internet of Things Xinxiang China College of Artificial Intelligence Nankai University Tianjin China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Extensive collection of gene expression data has provided abundant resources for the identification of biomarker genes and disease classification. However, there are still many challenges in processing high-dimensional microarray data, such as balancing classification accuracy with feature subset size and computational costs. To address the issues, a novel co-evolutionary particle swarm optimization method is proposed for biomarker selection and disease classification (NCPSO). Firstly, a population initialization strategy based on gene weight is provided. This strategy accurately calculates the importance of genes to optimize the search space of candidate genes. Secondly, a co-evolutionary search method based on gene information transfer is designed. This method applies gene knowledge transfer search mechanism and gene weight update mechanism to two equally sized subpopulations, respectively. During the evolutionary search process, the gene information transfer and interaction are conducted to accelerate the acquisition of the globally optimal gene subset. Finally, the dynamic evolution supervision of gene individuals is provided. This process can real-time detect the impact of mutated genes on classification performance during the evolution process, thereby enhancing the classification accuracy of gene selection. To validate the effectiveness of the NCPSO algorithm, five state-of-the-art methods on six high-dimensional microarray datasets are compared and the experimental results demonstrate that NCPSO outperforms the comparison methods in terms of classification accuracy, selected gene subset size, and computational time.

关键词： Accuracy Search methods Heuristic algorithms Prevention and mitigation Real-time systems Classification algorithms Gene expression Particle swarm optimization Knowledge transfer Cancer

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