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检索条件"任意字段=International Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2013"

共 1098 条记录，以下是11-20 订阅

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Determining HPV Status in Patients with Oropharyngeal Cancer from 3D CT Images Using Radiomics: Effect of Sampling methods 10th

Determining HPV Status in Patients with Oropharyngeal Cancer...

引用

10th international Work-conference on bioinformatics and Biomedical Engineering (IWBBIO)

作者： Sarac, Kubra Guvenis, Albert Bogazici Univ TR-34684 Istanbul Turkiye

ISBN: (纸本)9783031349591;9783031349607

Non-invasive detection of human papillomavirus (HPV) status is important for the treatment planning of patients with oropharyngeal cancer (OPC). In this work, three-dimensional (3D) head and neck computed tomography (CT) scans are utilized to identify HPV infection status in patients with OPC by applying radiomics and several resampling methods to handle highly imbalanced data. 1142 radiomic features were obtained from the segmented CT images of 238 patients. The features used were selected through correlation coefficient analysis, feature importance analysis, and backward elimination. The fifty most important features were chosen. Six different sampling methods, which are Synthetic Minority Oversampling Technique (SMOTE), Support Vector Machine Synthetic Minority Oversampling Technique (SVMSMOTE), Adaptive Synthetic Sampling Method (ADASYN), NearMiss, Condensed Nearest Neighbors (CNN), and Tomek's Link, were performed on the training set for each of the positive and negative HPV classes. Two different machine learning (ML) algorithms, a Light Gradient Boosting Machine (LightGBM) and Extreme Gradient Boosting (XGBoost), were applied as predictive classification models. Model performances were assessed separately on 20% of the data. Oversampling methods displayed better performance than undersampling methods. The best performance was seen in the combination of SMOTE and XGBoost algorithms, which had an area under the curve (AUC) of 0.93 (95% CI: 82-99) and an accuracy of 90% (95% CI: 78-96). Our work demonstrated a reasonable accuracy in the forecast of HPV status using 3D imbalanced and small datasets. Further work is needed to test the algorithms on larger, balanced, and multi-institutional data.

关键词： HPV Status Radiomics Resampling Machine Learning

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Application of Machine Learning and Large Language Model Module for Analyzing Gut Microbiota Data 20th

Application of Machine Learning and Large Language Model Mod...

引用

20th international conference on Intelligent Computing (ICIC)

作者： Cao, Jianhua Xu, Xiaoyu Zhang, Xiaomeng Tianjin Univ Sci & Technol Coll Artificial Intelligence Tianjin 300457 Peoples R China

ISBN: (纸本)9789819756889;9789819756896

In this paper, we aim to explore the applications of machine learning and large language model for analyzing gut microbiota data, particularly attempting to investigate large language model module to intelligently conduct data analysis just through prompts. The data of gut microbiota is from 16S rRNA sequencing result of obese mouse for obesity experimental research, and our primary task focus on identifying differentially expressed genes and uncovering biomarker microbiota associated with obesity. Statistical methods, including diversity analysis and principal component analysis, have been firstly conducted, revealing significant differences between experimental and control groups. Then different machine learning algorithms including random forest, SVM-RFE, Lasso, and XGBoost have been selected to discover distinguished genes through feature importance ranking. Several types of microbial genes have been identified by both of the machine learning methods, and these biomarkers exhibit significant abundance changes between groups. Some of them have been confirmed with related research literature, and the remains are worthy of attention for more investigation. Furthermore, we have developed a large language model module named Chat2GM, which is based on the LangChain framework, python Flask, and API of OpenAI's GPT model, particularly for this data analysis task. Upon uploading the original microbiota data, data exploration and analysis can be conducted merely by using prompts. It has been demonstrated to achieve satisfactory performance in completing the aforementioned analysis tasks with precise natural language instructions, indicating that applications powered by large language models with more expertise knowledge have great potential in gut microbiota research field.

关键词： Gut microbiota Machine learning Large language models LangChain framework

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Improving Deep Learning Model for Drug Synergy Prediction via Topological Features

Improving Deep Learning Model for Drug Synergy Prediction vi...

引用

2024 IEEE international conference on bioinformatics and Biomedicine, BIBM 2024

作者： Xiong, Yang Zhang, Zheng Chen, Xian-Gan South-Central Minzu University School of Biomedical Engineering Wuhan China

ISBN: (纸本)9798350386226

In cancer treatment, drug combination therapy has gained significant attention due to its potential for synergistic effects. However, traditional wet-lab screening methods are time-consuming and costly, limiting the scope of drug combination research. In recent years, computational biology approaches have been favored for their ability to efficiently predict drug synergies, but existing methods often overlook the complex network relationships between drug targets and cell line targets, which can affect model accuracy and generalization. To address this issue, we propose a method that combines deep learning algorithms with protein-protein interaction (PPI) network topological features, named DeepSynergyTF. This method improves the performance of deep learning models in predicting drug synergies by utilizing topological features. Specifically, by integrating PPI network datasets, we systematically analyze the distribution patterns and spatial proximity of drug targets and cell line targets, thereby constructing a series of improved network topological features. These features include key quantifiable metrics such as network betweenness centrality, closeness centrality, and network density. Experimental results show that our method significantly outperforms baseline methods, showing improvements across all performance metrics. This work highlights the potential of network topological features in predicting drug synergies. © 2024 IEEE.

关键词： Prediction models

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Evolving a Better Scheduler for Diffusion models 20th

Evolving a Better Scheduler for Diffusion Models

引用

20th Pacific Rim international conference on Artificial Intelligence, PRICAI 2023

作者： Liu, Zheping Song, Andy Sabar, Nasser Li, Wenkai RMIT University MelbourneVIC Australia La Trobe University MelbourneVIC Australia

ISBN: (纸本)9789819970216

AI Generated Content (AIGC) is becoming phenomenally prominent and impactful. One of the key generative algorithms used in AIGC is the diffusion model which is widely used in generative images and audio. In comparison with other generative methods such as GAN (Generative Adversarial Network) and VAE (Variational Auto Encoder), diffusion models can generate samples of higher quality. To further improve diffusion models, especially in terms of sampling speed, we propose an evolutionary algorithm in this paper. That is to enhance the noise scheduler of the diffusion framework, thereby improving both performance and sampling speed. This is the first diffusion model that incorporates evolutionary algorithms. Our experiments show that evolved schedulers can bring concrete improvement in the generative process. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Evolutionary algorithms

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Interpretable Deep Learning models With Concept Whitening Layers

Interpretable Deep Learning Models With Concept Whitening La...

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2023 IEEE international conference on bioinformatics and Biomedicine, BIBM 2023

作者： Boddam, Megana Kim, Wooyoung University of Washington Bothell Division of Computing and Software Systems School of Stem BothellWA United States

ISBN: (纸本)9798350337488

Deep Learning models, such as convolutional neural networks (CNN), are hard to interpret due to their complex, nonlinear, and high-dimensional algorithms. We focused on interpreting CNNs for predicting Hepatocellular Carcinoma (HCC), the most common primary liver cancer, using histopathology images. We used a 50-layer residual neural network (RNN50), an inception CNN, and a recent innovation called concept whitening (CW). The results showed that adding CW layers to CNN models did not significantly affect model accuracy. However, post-hoc analysis methods, including inter-concept similarity rating, intra-concept similarity rating, concept importance rating, and feature vector displays, revealed improved interpretability. © 2023 IEEE.

关键词： Concept Vectors Concept Whitening Convolutional Neural Networks Deep Learning Feature Vectors Interpretive Machine Learning Residual Neural Networks

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Systems for Scalable Graph Analytics and Machine Learning: Trends and methods 24

Systems for Scalable Graph Analytics and Machine Learning: T...

引用

33rd ACM international conference on Information and Knowledge Management (CIKM)

作者： Yan, Da Yuan, Lyuheng Ahmad, Akhlaque Adhikari, Saugat Indiana Univ Bloomington Bloomington IN 47405 USA

ISBN: (纸本)9798400704369

Graph-theoretic algorithms and graph machine learning models are essential tools for addressing many real-life problems, such as social network analysis and bioinformatics. To support large-scale graph analytics, graph-parallel systems have been actively developed for over one decade, such as Google's Pregel and Spark's GraphX, which (i) promote a think-like-a-vertex computing model and target (ii) iterative algorithms and (iii) those problems that output a value for each vertex. However, this model is too restricted for supporting the rich set of heterogeneous operations for graph analytics and machine learning that many real applications demand. In recent years, two new trends emerge in graph-parallel systems research: (1) a novel think-like-a-task computing model that can efficiently support the various computationally expensive problems of subgraph search;and (2) scalable systems for learning graph neural networks. These systems effectively complement the diversity needs of graph-parallel tools that can flexibly work together in a comprehensive graph processing pipeline for real applications, with the capability of capturing structural features. This tutorial will provide an effective categorization of the recent systems in these two directions based on their computing models and adopted techniques, and will review the key design ideas of these systems.

关键词： Graph system vertex structure subgraph graph neural network GNN

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Assessing Tree-Based Phenotype Prediction on the UK Biobank

Assessing Tree-Based Phenotype Prediction on the UK Biobank

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2023 IEEE international conference on bioinformatics and Biomedicine, BIBM 2023

作者： Melendez, Alex Lopez, Cayetana Bonet, David Sant, Gerard Montserrat, Daniel Mas Abante, Jordi Rivas, Manuel Marques, Ferran Ioannidis, Alexander G. Universitat Politècnica de Catalunya Barcelona Spain Stanford University StanfordCA United States Universitat de Barcelona Barcelona Spain

ISBN: (纸本)9798350337488

Precision medicine relies on the ability to identify associations between genomic data and its phenotypic expression in order to provide personalized predictions. Phenotype prediction using statistical models trained on large-scale genomic and phenotypic data is a critical research area at the intersection of machine learning and genomics. Current genotype-to-phenotype models, such as polygenic risk scores, only account for linear relationships, and the use of nonlinear methods is still partially unexplored. In this work, we evaluate the prediction accuracy and scalability of nine nonlinear decision tree-based algorithms, including ensembling and boosting mechanisms, and compare them to linear prediction models. We assess the prediction performance for 24 anthropometric and disease-related phenotypes present in the UK Biobank. By using random feature selection, we explore how accuracy and computational time vary for each method as a function of the number of genetic variants selected. Our results show that tree-based methods, especially gradientboosted trees, can offer superior predictions with computational times comparable to those of linear methods. Thus, models able to capture nonlinear relationships between genotypes and phenotypes merit consideration for integration in upcoming computational systems for personalized medicine. © 2023 IEEE.

关键词： bioinformatics Machine Learning Phenotype Prediction Precision Medicine Tree-based methods

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Research on Deep Learning algorithms for Predicting DNA-Binding Proteins Based on Sequence Information 2

Research on Deep Learning Algorithms for Predicting DNA-Bind...

引用

2nd IEEE international conference on Electrical, Automation and Computer Engineering, ICEACE 2024

作者： Shi, Chongwei University of Michigan Oral and Maxillofacial Surgery MI48109 United States

ISBN: (纸本)9798350368208

DNA-binding proteins play a crucial role in gene regulation and biological functions, making their accurate prediction essential for understanding biological processes and developing new drugs. Traditional DNA-binding protein prediction methods rely on sequence features and machine learning algorithms;however, these approaches often face challenges in accuracy and generalization when dealing with complex sequence information. To address this issue, this study proposes a deep learning-based prediction model that effectively captures both spatial and temporal features of sequences by combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Experimental results demonstrate that the proposed model performs excellently on multiple benchmark datasets, significantly improving prediction accuracy and robustness. This research offers a new approach to DNA-binding protein prediction and highlights the immense potential of deep learning in bioinformatics. © 2024 IEEE.

关键词： Prediction models

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Cancer Prediction using Clinical and Genomic Data Fusion: A Systematic Review 6

Cancer Prediction using Clinical and Genomic Data Fusion: A ...

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6th international conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Julian, Kelvin Nugroho, Kuncahyo Setyo Nirwantono, Rudi Pardamean, Bens Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

This systematic review provides a comprehensive overview of the methods used to integrate genomic and clinical data in cancer prediction. The review includes 19 studies across various cancers, including breast, colorectal, melanoma, lung, pancreatic, and thyroid. The studies employed different methods to combine genomic and clinical data, including weighted polygenic risk scores, genetic and non-genetic risk scores, and different machine learning algorithms. The results show significant improvements in model prediction performance accuracy across multiple studies. The review highlights the potential benefits of integrating genetic and phenotypic information to improve disease risk prediction models and inform personalized healthcare strategies. © 2024 IEEE.

关键词： Lung cancer

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Automatic Protein Sequences Classification Using Machine Learning methods based on N-Gram Model 4

Automatic Protein Sequences Classification Using Machine Lea...

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4th international conference on Machine Learning and Computer Application, ICMLCA 2023

作者： Zou, Chengen Leicester International Institute Dalian University of Technology Dalian China

ISBN: (纸本)9798400709449

Protein sequence classification is a crucial task in the field of bioinformatics as it helps to reveal various types of properties of proteins. Machine learning and deep learning algorithms have great application value in the problem of protein classification prediction. In this study, we propose a novel approach for extracting sequence features based on N-Gram model. We then utilize this approach to train and evaluate machine learning and deep learning algorithms on the same dataset. The experimental results show that the Random Forest method based on N-Gram features significantly outperforms other types of algorithmic models on this dataset, with a further increase in accuracy. © 2023 ACM.

关键词： Convolutional neural networks

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