Annals of Data Science最新文献_第7页

Bayesian Learning of Personalized Longitudinal Biomarker Trajectory 个性化纵向生物标志物轨迹的贝叶斯学习

Annals of Data Science Pub Date : 2023-08-01 DOI: 10.1007/s40745-023-00486-0

Shouhao Zhou, Xuelin Huang, Chan Shen, Hagop M. Kantarjian

{"title":"Bayesian Learning of Personalized Longitudinal Biomarker Trajectory","authors":"Shouhao Zhou, Xuelin Huang, Chan Shen, Hagop M. Kantarjian","doi":"10.1007/s40745-023-00486-0","DOIUrl":"10.1007/s40745-023-00486-0","url":null,"abstract":"<div><p>This work concerns the effective personalized prediction of longitudinal biomarker trajectory, motivated by a study of cancer targeted therapy for patients with chronic myeloid leukemia (CML). Continuous monitoring with a confirmed biomarker of residual disease is a key component of CML management for early prediction of disease relapse. However, the longitudinal biomarker measurements have highly heterogeneous trajectories between subjects (patients) with various shapes and patterns. It is believed that the trajectory is clinically related to the development of treatment resistance, but there was limited knowledge about the underlying mechanism. To address the challenge, we propose a novel Bayesian approach to modeling the distribution of subject-specific longitudinal trajectories. It exploits flexible Bayesian learning to accommodate complex changing patterns over time and non-linear covariate effects, and allows for real-time prediction of both in-sample and out-of-sample subjects. The generated information can help make clinical decisions, and consequently enhance the personalized treatment management of precision medicine.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 3","pages":"1031 - 1050"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46463104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Applications of Reliability Test Plan for Logistic Rayleigh Distributed Quality Characteristic 物流瑞利分布质量特性可靠性试验计划的应用

Annals of Data Science Pub Date : 2023-07-19 DOI: 10.1007/s40745-023-00473-5

Mahendra Saha, Harsh Tripathi, Anju Devi, Pratibha Pareek

引用次数: 0

Patient Questionnaires Based Parkinson’s Disease Classification Using Artificial Neural Network 基于患者问卷的帕金森病人工神经网络分类

Annals of Data Science Pub Date : 2023-07-15 DOI: 10.1007/s40745-023-00482-4

Tarakashar Das, Sabrina Mobassirin, Syed Md. Minhaz Hossain, Aka Das, Anik Sen, Khaleque Md. Aashiq Kamal, Kaushik Deb

{"title":"Patient Questionnaires Based Parkinson’s Disease Classification Using Artificial Neural Network","authors":"Tarakashar Das, Sabrina Mobassirin, Syed Md. Minhaz Hossain, Aka Das, Anik Sen, Khaleque Md. Aashiq Kamal, Kaushik Deb","doi":"10.1007/s40745-023-00482-4","DOIUrl":"10.1007/s40745-023-00482-4","url":null,"abstract":"<div><p>Parkinson’s disease is one of the most prevalent and harmful neurodegenerative conditions (PD). Even today, PD diagnosis and monitoring remain pricy and inconvenient processes. With the unprecedented progress of artificial intelligence algorithms, there is an opportunity to develop a cost-effective system for diagnosing PD at an earlier stage. No permanent remedy has been established yet; however, an earlier diagnosis helps lead a better life. Probably, the three most responsible categories of symptoms for Parkinson’s Disease are tremors, rigidity, and body bradykinesia. Therefore, we investigate the 53 unique features of the Parkinson’s Progression Markers Initiative dataset to determine the significant symptoms, including three major categories. As feature selection is integral to developing a generalized model, we investigate including and excluding feature selection. Four feature selection methods are incorporated—low variance filter, Wilcoxon rank-sum test, principle component analysis, and Chi-square test. Furthermore, we utilize machine learning, ensemble learning, and artificial neural networks (ANN) for classification. Experimental evidence shows that not all symptoms are equally important, but no symptom can be completely eliminated. However, our proposed ANN model attains the best mean accuracy of 99.51%, 98.17% mean specificity, 0.9830 mean Kappa Score, 0.99 mean AUC, and 99.70% mean F1-score with all the features. The efficiency of our suggested technique on diverse data modalities is demonstrated by comparison with recent publications. Finally, we established a trade-off between classification time and accuracy.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 5","pages":"1821 - 1864"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s40745-023-00482-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46611876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A New Class of Distribution Over Bounded Support and Its Associated Regression Model 一类新的有界支持上分布及其回归模型

Annals of Data Science Pub Date : 2023-07-11 DOI: 10.1007/s40745-023-00483-3

Ishfaq S. Ahmad, Rameesa Jan, Poonam Nirwan, Peer Bilal Ahmad

引用次数: 0

Inception-UDet: An Improved U-Net Architecture for Brain Tumor Segmentation Inception-UDet:一种改进的U-Net结构用于脑肿瘤分割

Annals of Data Science Pub Date : 2023-07-01 DOI: 10.1007/s40745-023-00480-6

Ilyasse Aboussaleh, Jamal Riffi, Adnane Mohamed Mahraz, Hamid Tairi

{"title":"Inception-UDet: An Improved U-Net Architecture for Brain Tumor Segmentation","authors":"Ilyasse Aboussaleh, Jamal Riffi, Adnane Mohamed Mahraz, Hamid Tairi","doi":"10.1007/s40745-023-00480-6","DOIUrl":"10.1007/s40745-023-00480-6","url":null,"abstract":"<div><p>Brain tumor segmentation is an important field and a sensitive task in tumor diagnosis. The treatment research in this area has helped specialists in detecting the tumor’s location in order to deal with it in its early stages. Numerous methods based on deep learning, have been proposed, including the symmetric U-Net architectures, which revealed great results in the medical imaging field, precisely brain tumor segmentation. In this paper, we proposed an improved U-Net architecture called Inception U-Det inspired by U-Det. This work aims at employing the inception block instead of the convolution one used in the bi-directional feature pyramid neural (Bi-FPN) network during the skip connection U-Det phase. Furthermore, a comparison study has been performed between our proposed approach and the three known architectures in medical imaging segmentation; U-Net, DC-Unet, and U-Det. Several segmentation metrics have been computed and then taken into account in these methods, by means of the publicly available BraTS datasets. Thus, our obtained results have showed promising results in terms of accuracy, dice similarity coefficient (DSC), and intersection–union ratio (IOU). Moreover, the proposed method has achieved a DSC of 87.9%, 85.5%, and 83.9% on BraTS2020, BraTS2018, and BraTS2017, respectively, calculated from the best fold in fourfold cross-validation employed in the present approach.</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 3","pages":"831 - 853"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45364813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stable Variable Selection for High-Dimensional Genomic Data with Strong Correlations 为具有强相关性的高维基因组数据选择稳定的变量

Annals of Data Science Pub Date : 2023-06-29 DOI: 10.1007/s40745-023-00481-5

Reetika Sarkar, Sithija Manage, Xiaoli Gao

{"title":"Stable Variable Selection for High-Dimensional Genomic Data with Strong Correlations","authors":"Reetika Sarkar, Sithija Manage, Xiaoli Gao","doi":"10.1007/s40745-023-00481-5","DOIUrl":"10.1007/s40745-023-00481-5","url":null,"abstract":"<div><p>High-dimensional genomic data studies are often found to exhibit strong correlations, which results in instability and inconsistency in the estimates obtained using commonly used regularization approaches including the Lasso and MCP, etc. In this paper, we perform comparative study of regularization approaches for variable selection under different correlation structures and propose a two-stage procedure named rPGBS to address the issue of stable variable selection in various strong correlation settings. This approach involves repeatedly running a two-stage hierarchical approach consisting of a random pseudo-group clustering and bi-level variable selection. Extensive simulation studies and high-dimensional genomic data analysis on real datasets have demonstrated the advantage of the proposed rPGBS method over some of the most used regularization methods. In particular, rPGBS results in more stable selection of variables across a variety of correlation settings, as compared to some recent methods addressing variable selection with strong correlations: Precision Lasso (Wang et al. in Bioinformatics 35:1181–1187, 2019) and Whitening Lasso (Zhu et al. in Bioinformatics 37:2238–2244, 2021). Moreover, rPGBS has been shown to be computationally efficient across various settings.\u0000</p></div>","PeriodicalId":36280,"journal":{"name":"Annals of Data Science","volume":"11 4","pages":"1139 - 1164"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135049935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On Progressively Censored Generalized X-Exponential Distribution: (Non) Bayesian Estimation with an Application to Bladder Cancer Data 关于渐进截尾广义X指数分布：（非）贝叶斯估计及其在癌症数据中的应用

Annals of Data Science Pub Date : 2023-06-15 DOI: 10.1007/s40745-023-00477-1

Kousik Maiti, Suchandan Kayal, Aditi Kar Gangopadhyay

引用次数: 0

A Survey on Differential Privacy for Medical Data Analysis 医疗数据分析中的差异隐私调查

Annals of Data Science Pub Date : 2023-06-10 DOI: 10.1007/s40745-023-00475-3

WeiKang Liu, Yanchun Zhang, Hong Yang, Qinxue Meng

引用次数: 0

Naïve Bayes Classifier Model for Detecting Spam Mails Naïve垃圾邮件检测的贝叶斯分类器模型

Annals of Data Science Pub Date : 2023-06-09 DOI: 10.1007/s40745-023-00479-z

Shrawan Kumar, Kavita Gupta, Manya Gupta