检索结果-内蒙古大学图书馆

A hardware-efficient on-implant spike compression processor based on VQ-DAE for brain-implantable microsystems

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2025年 1-10页

作者： Ahmadi-Dastgerdi, Nazanin Hosseini-Nejad, Hossein Alinejad-Rokny, Hamid KN Toosi Univ Technol Fac Elect Engn POB 16315-1355 Tehran *** Iran UNSW Sydney Grad Sch Biomed Engn Biomed Machine Learning Lab BML Sydney NSW 2052 Australia

High-density implantable neural recording microsystems deal with a huge amount of data. Since the wireless transmission of the raw recorded data leads to excessive bandwidth requirements, spike compression approaches have become vital to such systems. The compression processor is designed to be implemented on the implant and so to avoid any tissue damage, the hardware cost of the processor is of great importance. The vector quantization (VQ) algorithm has proven to be effective in compression applications and spike compression systems as well. In this paper, benefiting from the capabilities of the denoising autoencoders (DAE), we propose a solution to enhance the compression performance of the VQ-based approach in terms of both reconstruction accuracy and hardware efficiency. Moreover, we develop a hardware-efficient multi-channel architecture for the proposed VQ-DAE processor. The processor has been implemented in a 180-nm CMOS technology and the validation and verification processes confirm that it provides satisfactory results. It achieves an average signal-to-noise-distortion (SNDR) of 14.51 at a spike compression ratio (SCR) of 30. Operated at a clock frequency of 192 kHz and a supply voltage of 1.8 V, the circuit consumes a power of 4.88 mu W\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu W$$\end{document} and a silicon area of 0.14 mm2 per channel.

关键词： Brain-implantable microsystems Spike compression Vector quantization Denoising autoencoders

来源：评论

学校读者我要写书评

暂无评论

Post-Cardiac arrest outcome prediction using machine learning: A systematic review and meta-analysis

引用

INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS 2025年 193卷 105659-105659页

作者： Zobeiri, Amirhosein Rezaee, Alireza Hajati, Farshid Argha, Ahmadreza Alinejad-Rokny, Hamid Univ Tehran Coll Interdisciplinary Sci & Technol Sch Intelligent Syst Dept Mechatron Tehran Iran Univ New England Fac Sci Agr Business & Law Sch Sci & Technol Armidale NSW 2350 Australia Sch Biomed Engn UNSW Sydney Randwick NSW 2052 Australia UNSW Sydney Sch Biomed Engn Biomed Machine Learning Lab Randwick NSW 2052 Australia

Background: Early and reliable prognostication in post-cardiac arrest patients remains challenging, with various factors linked to return of spontaneous circulation (ROSC), survival, and neurological results. machine learning and deep learning models show promise in improving these predictions. This systematic review and meta-analysis evaluates how effective these approaches are in predicting clinical outcomes at different time points using structured data. Methods: This study followed PRISMA guidelines, involving a comprehensive search across PubMed, Scopus, and Web of Science databases until March 2024. Studies aimed at predicting ROSC, survival (or mortality), and neurological outcomes after cardiac arrest through the application of machine learning or deep learning techniques with structured data were included. Data extraction followed the guidelines of the CHARMS checklist, and the bias risk was evaluated using PROBAST tool. Models reporting the AUC metric with 95 % confidence intervals were incorporated into the quantitative synthesis and meta-analysis. Results: After extracting 2,753 initial records, 41 studies met the inclusion criteria, yielding 97 machine learning and 16 deep learning models. The pooled AUC for predicting favorable neurological outcomes (CPC 1 or 2) at hospital discharge was 0.871 (95 % CI: 0.813 - 0.928) for machine learning models and 0.877 (95 % CI: 0.831-0.924) across deep learning algorithms. For survival prediction, this value was found to be 0.837 (95% CI: 0.757-0.916). Considerable heterogeneity and high risk of bias were observed, mainly attributable to inadequate management of missing data and the absence of calibration plots. Most studies focused on pre-hospital factors, with age, sex, and initial arrest rhythm being the most frequent features. Conclusion: Predictive models utilizing AI-based approaches, including machine and deep learning models exhibit enhanced effectiveness compared to previous regression algorithms, but sign

关键词： Cardiac arrest Outcome prediction machine learning Deep learning Structured data

来源：评论

学校读者我要写书评

暂无评论

SFGCN: Synergetic fusion-based graph convolutional networks approach for link prediction in social networks

引用

INFORMATION FUSION 2025年 114卷

作者： Lee, Sang-Woong Tanveer, Jawad Rahmani, Amir Masoud Alinejad-Rokny, Hamid Khoshvaght, Parisa Zare, Gholamreza Alamdari, Pegah Malekpour Hosseinzadeh, Mehdi Gachon Univ Sch Comp Pattern Recognit & Machine Learning Lab Seongnam 13120 South Korea Sejong Univ Dept Comp Sci & Engn Seoul 05006 South Korea Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Yunlin Taiwan UNSW Sydney Grad Sch Biomed Engn UNSW BioMed Machine Learning Lab BML Sydney NSW 2052 Australia UNSW Sydney Tyree Inst Hlth Engn IHealthE Sydney NSW 2052 Australia Duy Tan Univ DTU AI & Data Sci Hub DAIDASH Da Nang Vietnam Duy Tan Univ Sch Comp Sci Da Nang Vietnam

Accurate Link Prediction (LP) in Social Networks (SNs) is crucial for various practical applications, such as recommendation systems and network security. However, traditional techniques often struggle to capture the intricate and multidimensional nature of these networks. This paper presents a novel approach, the Synergetic Fusion-based Graph Convolutional Networks (SFGCN), designed to enhance LP accuracy in SNs. The SFGCN model utilizes a fusion architecture that combines structural features and other attribute data through early, intermediate, and late fusion mechanisms to create improved node and edge representations. We thoroughly evaluate our SFGCN model on seven real-world datasets, encompassing citation networks, co-purchase networks, and academic publication domains. The results demonstrate its superiority over baseline GCN architectures and other selected LP methods, achieving a 6.88 % improvement in accuracy. The experiments show that our model captures the complex interactions and dependencies within SNs, providing a comprehensive understanding of their underlying dynamics. The approach mentioned can be effectively applied in the domain of SN analysis to enhance the accuracy of LP results. This method not only improves the precision of predictions but also enhances the adaptability of the model in diverse SN scenarios.

关键词： Link Prediction Social networks Graph convolutional networks machine learning Fusion model Graph neural network Network data analysis

来源：评论

学校读者我要写书评

暂无评论

Diagnostic Performance of Artificial Intelligence-Based Methods for Tuberculosis Detection: Systematic Review

引用

JOURNAL OF MEDICAL INTERNET RESEARCH 2025年 27卷 e69068页

作者： Hansun, Seng Argha, Ahmadreza Bakhshayeshi, Ivan Wicaksana, Arya Alinejad-Rokny, Hamid Fox, Greg J. Liaw, Siaw-Teng Celler, Branko G. Marks, Guy B. UNSW Sydney Sch Clin Med South West Sydney UNSW Med & Hlth High St Sydney NSW 2052 Australia Woolcock Inst Med Res Woolcock Vietnam Res Grp Sydney Australia UNSW Sydney Grad Sch Biomed Engn Sydney Australia UNSW Sydney Tyree Inst Hlth Engn Sydney Australia UNSW Sydney Ageing Future Inst Sydney NSW Australia UNSW Sydney Grad Sch Biomed Engn Biomed Machine Learning Lab Sydney Australia Univ Multimedia Nusantara Informat Dept Tangerang Indonesia Univ Sydney Fac Med & Hlth NHMRC Clin Trials Ctr Sydney Australia UNSW Sydney Sch Populat Hlth Sydney Australia UNSW Sydney Sch Clin Med Sydney Australia UNSW Sydney Sch Elect Engn & Telecommun Biomed Syst Res Lab Sydney Australia Burnet Inst Melbourne Australia

Background: Tuberculosis (TB) remains a significant health concern, contributing to the highest mortality among infectious diseases worldwide. However, none of the various TB diagnostic tools introduced is deemed sufficient on its own for the diagnostic pathway, so various artificial intelligence (AI)-based methods have been developed to address this issue. Objective: We aimed to provide a comprehensive evaluation of AI-based algorithmsfor TB detection across various data modalities. Methods: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) 2020 guidelines, we conducted a systematic review to synthesize current knowledge on this topic. Our search across 3 major databases (Scopus, PubMed, Association for Computing machinery [ACM] Digital Library) yielded 1146 records, of which we included 152 (13.3%) studies in our analysis. QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies version 2) was performed for the risk-of-bias assessment of all included studies. Results: Radiographic biomarkers (n=129, 84.9%) and deep learning (DL;n=122, 80.3%) approaches were predominantly used, with convolutional neural networks (CNNs) using Visual Geometry Group (VGG)-16 (n=37, 24.3%), ResNet-50 (n=33, 21.7%), and DenseNet-121(n=19, 12.5%) architectures being the most common DL approach. The majority of studies focused on model development (n=143, 94.1%) and used a single modality approach (n=141, 92.8%). AI methods demonstrated good performance in all studies: meanaccuracy=91.93%(SD 8.10%, 95% CI 90.52%-93.33%;median 93.59%, IQR 88.33%-98.32%), mean area under the curve (AUC)=93.48% (SD 7.51%, 95% CI 91.90%-95.06%;median 95.28%, IQR 91%-99%), mean sensitivity=92.77% (SD 7.48%, 95% CI 91.38%-94.15%;median 94.05% IQR 89%-98.87%), and mean specificity=92.39% (SD 9.4%, 95% CI 90.30%-94.49%;median 95.38%, IQR 89.42%-99.19%). AI performance across different biomarker types showed meanaccuraciesof 92.45% (SD 7.83%), 89.03% (SD 8.49%), and 84.21% (S

关键词： AI artificial intelligence deep learning diagnostic performance machine learning PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analysis QUADAS-2 Quality Assessment of Diagnostic Accuracy Studies version 2 systematic literature review tuberculosis detection

来源：评论

学校读者我要写书评

暂无评论

A New Ensemble Transfer learning Approach With Rejection Mechanism for Tuberculosis Disease Detection

引用

IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2025年第4期9卷 433-446页

作者： Hansun, Seng Argha, Ahmadreza Alinejad-Rokny, Hamid Alizadehsani, Roohallah Gorriz, Juan M. Liaw, Siaw-Teng Celler, Branko G. Marks, Guy B. UNSW Sydney South West Sydney UNSW Med & Hlth Sch Clin Med Sydney NSW 2033 Australia Woolcock Inst Med Res Vietnam Res Grp Sydney NSW 2113 Australia Tyree Inst Hlth Engn Grad Sch Biomed Engn Sydney NSW 2033 Australia UNSW Sydney Ageing Future Inst Sydney NSW 2033 Australia UNSW Sydney Grad Sch Biomed Engn Biomed Machine Learning Lab Sydney NSW 2033 Australia Deakin Univ Inst Intelligent Syst Res & Innovat Burwood Vic 3125 Australia Univ Granada Dept Signal Theory Networking & Commun Granada 18012 Spain UNSW Sydney Sch Populat Hlth Sydney NSW 2033 Australia UNSW Sydney Sch Elect Engn & Telecommun Biomed Syst Res Lab Sydney NSW 2033 Australia

Transfer learning (TL) is a strategic solution to handle vast data volume requirements in deep learning (DL). It transfers knowledge learned from a large base dataset, as a pretrained model (PTM), to a new domain. In this study, we introduce an ensemble of classifiers trained on features extracted from some intermediate layers of a PTM for Tuberculosis (TB) detection task. We use different EfficientNet variants: EfficientNet-B0-EfficientNet-B3, as the PTM. Moreover, we introduce a rejection mechanism and implement post-hoc calibration methods to enhance the reliability and trustworthiness of the developed models. Additionally, we conduct analyses on domain-shift distribution, a topic rarely discussed in the context of TB detection. Through a fivefold cross-validation on two prominent chest X-ray datasets, the Montgomery County (MC) and Shenzhen (SZ), our ensemble approach achieved competitive results with accuracies of 94.89% (MC) and 92.75% (SZ). The incorporation of the devised rejection mechanism resulted in enhanced model accuracy, albeit with a coverage tradeoff. In domain-shift experiments, the proposed approach achieved an accuracy of 83.57% (63% coverage) when applying the MC-trained model on SZ, and an accuracy of 88.50% (82% coverage) when applying the SZ-trained model on MC.

关键词： Feature extraction Accuracy Medical diagnostic imaging Australia Residual neural networks Integrated circuit modeling Computer architecture Convolutional neural networks (CNNs) deep learning (DL) domain-shift EfficientNets ensemble rejection mechanism transfer learning (TL) tuberculosis rejection mechanism transfer learning (TL) tuberculosis

来源：评论

学校读者我要写书评

暂无评论

A comprehensive survey of golden jacal optimization and its applications

引用

COMPUTER SCIENCE REVIEW 2025年 56卷

作者： Hosseinzadeh, Mehdi Tanveer, Jawad Rahmani, Amir Masoud Alanazi, Abed Zaidi, Monji Mohamed Aurangzeb, Khursheed Alinejad-Rokny, Hamid Porntaveetus, Thantrira Lee, Sang-Woong Duy Tan Univ Sch Comp Sci Da Nang Vietnam Jadara Univ Jadara Univ Res Ctr Irbid Jordan Sejong Univ Dept Comp Sci & Engn Seoul 05006 South Korea Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Yunlin Taiwan Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Sci POB 151 Al Kharj 11942 Saudi Arabia King Khalid Univ Coll Engn Dept Elect Engn Abha 61421 Saudi Arabia King Khalid Univ Ctr Engn & Technol Innovat Abha 61421 Saudi Arabia King Saud Univ Coll Comp & Informat Sci Dept Comp Engn POB 51178 Riyadh 11543 Saudi Arabia UNSW Sydney Grad Sch Biomed Engn UNSW BioMed Machine Learning Lab BML Sydney NSW 2052 Australia Chulalongkorn Univ Ctr Excellence Genom & Precis Dent Geriatr Dent & Special Patients Care Int Program Clin Res CtrDept PhysiolFac Dent Bangkok Thailand Gachon Univ Pattern Recognit & Machine Learning Lab Seongnam 13120 South Korea

In recent decades, there has been an increasing interest from the research community in various scientific and engineering fields, including robotic control, signal processing, image processing, feature selection, classification, clustering, and other issues. Many optimization problems are inherently complicated and complex. They cannot be solved by traditional optimization methods, such as mathematical programming, because most conventional optimization methods focus on evaluating first derivatives. On the other hand, metaheuristic algorithms have high ability and adaptability in finding near-optimal solutions in a reasonable time for different optimization problems due to parallel search and balance between exploration and exploitation. This study discusses the basic principles and mechanisms of the GJO algorithm and its challenges. This review aims to provide valuable insights into the potential of the GJO algorithm for real-world and scientific optimization tasks. In this paper, a complete review of the Golden Jackal Optimization (GJO) algorithm for various optimization problems is done. The GJO algorithm is one of the metaheuristic algorithms invented in 2022 and inspired by the life of natural jackals. This paper's complete classification of GJO in hybrid, improved, binary, multi-objective, and optimization problems is done. The analysis shows that the percentage of studies conducted in the four fields of hybrid, improved variants of GJO (binary, multi-objective), and optimization are 11 %, 44 %, 9 %, and 36 %, respectively. Studies have shown that this algorithm performs well in real-world challenges. GJO is a powerful tool for solving scientific and engineering problems flexibly.

关键词： Optimization problems Metaheuristic algorithms Golden Jackal Optimization Improved

来源：评论

学校读者我要写书评

暂无评论

PLXDC1⁺ Tumor-Associated Pancreatic Stellate Cells Promote Desmoplastic and Immunosuppressive Niche in Pancreatic Ductal Adenocarcinoma

引用

ADVANCED SCIENCE 2025年第0期 e2415756页

作者： Du, Yanhua Zhao, Yizhou Li, Judong Wang, Jiaxin You, Shenglan Zhang, Yao Zhang, Li Yang, Jihong Alinejad-Rokny, Hamid Cheng, Shujie Shao, Chenghao Zou, Duowu Ye, Youqiong Shanghai Jiao Tong Univ Ruijin Hosp Sch Med Ctr Immune Related DisShanghai Inst ImmunolDept Shanghai Peoples R China Shanghai Jiao Tong Univ Yale Inst Immune Metab State Key Lab Syst Med Canc Sch Med Shanghai 20025 Peoples R China Naval Med Univ Changzheng Hosp Dept Pancreat Biliary Surg Shanghai 200003 Peoples R China Hebei Univ Affiliated Hosp Dept Hepatobiliary Surg Hebei Key Lab Gen Surg Digital Med Baoding 071000 Peoples R China UNSW Sydney Sch Biomed Engn UNSW BioMed Machine Learning Lab BML Sydney NSW 2052 Australia

Pancreatic stellate cells (PSCs) contribute to pancreatic ductal adenocarcinoma (PDAC) progression and therapeutic resistance, yet their detailed functions remain unclear. This study combined RNA sequencing and assay for transposase-accessible chromatin using sequencing (ATAC-seq) on sorted PSCs from adjacent normal and PDAC tissues to investigate their transcriptional and epigenetic activation. PSCs heterogeneity and functions are characterized through bulk, single-cell, and spatial transcriptomes, as well as in situ sequencing. The clinical relevance of PSCs in immunotherapy is assessed using an in-house immune-checkpoint blockade (ICB) treatment cohort. Findings showed that stress and hypoxia signaling activated PSCs in PDAC. Three common PSCs (CPSCs) and four tumor-associated PSCs (TPSCs) are identified, each with distinct functions. CPSCs differentiated into CCL19+ TPSCs in immune-enriched regions, MYH11+ TPSCs in the stromal region, and PLXDC1+ TPSCs, which exhibited cancer-associated myofibroblasts (myCAFs) phenotype linked to poor prognosis. Notably, PLXDC1+ TPSCs, located near aggressive LRRC15+ myCAFs and SPP1+ macrophages, formed a desmoplastic and immunosuppressive niche around the tumor boundary, promoting CD8 T cell exhaustion. Single-cell transcriptomics of PDAC patients treated with ICB revealed that PLXDC1+ TPSCs correlated with poor immunotherapy efficacy. Overall, this study provides key insights into PSCs in PDAC and potential therapeutic targets.

关键词： activation, heterogeneity immunotherapy resistance pancreatic stellate cells (PSCs) PLXDC1(+) tumor-associated PSCs single-cell and spatial transcriptomes

来源：评论

学校读者我要写书评

暂无评论

CNVDeep: deep association of copy number variants with neurocognitive disorders

引用

BMC BIOINFORMATICS 2024年第1期25卷 283页

作者： Rahaie, Zahra Rabiee, Hamid R. Alinejad-Rokny, Hamid Sharif Univ Technol Dept Comp Engn BCB Grp DML Tehran Iran UNSW Sydney Sch Biomed Engn UNSW Biomed Machine Learning Lab BML Sydney Australia

BackgroundCopy number variants (CNVs) have become increasingly instrumental in understanding the etiology of all diseases and phenotypes, including Neurocognitive Disorders (NDs). Among the well-established regions associated with ND are small parts of chromosome 16 deletions (16p11.2) and chromosome 15 duplications (15q3). Various methods have been developed to identify associations between CNVs and diseases of interest. The majority of methods are based on statistical inference techniques. However, due to the multi-dimensional nature of the features of the CNVs, these methods are still immature. The other aspect is that regions discovered by different methods are large, while the causative regions may be much *** this study, we propose a regularized deep learning model to select causal regions for the target disease. With the help of the proximal [20] gradient descent algorithm, the model utilizes the group LASSO concept and embraces a deep learning model in a sparsity framework. We perform the CNV analysis for 74,811 individuals with three types of brain disorders, autism spectrum disorder (ASD), schizophrenia (SCZ), and developmental delay (DD), and also perform cumulative analysis to discover the regions that are common among the NDs. The brain expression of genes associated with diseases has increased by an average of 20 percent, and genes with homologs in mice that cause nervous system phenotypes have increased by 18 percent (on average). The DECIPHER data source also seeks other phenotypes connected to the detected regions alongside gene ontology analysis. The target diseases are correlated with some unexplored regions, such as deletions on 1q21.1 and 1q21.2 (for ASD), deletions on 20q12 (for SCZ), and duplications on 8p23.3 (for DD). Furthermore, our method is compared with other machine learning *** model effectively identifies regions associated with phenotypic traits using regularized deep learning. Rather than attempt

关键词： Copy number variant Association Deep learning Proximal gradient descent Group LASSO Autism Schizophrenia Developmental delay

来源：评论

学校读者我要写书评

暂无评论

Myocarditis Diagnosis: A Method using Mutual learning-Based ABC and Reinforcement learning 22

Myocarditis Diagnosis: A Method using Mutual Learning-Based ...

引用

IEEE Joint 22nd International Symposium on Computational Intelligence and Informatics / 8th IEEE International Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics (CINTI-MACRo)

作者： Danaei, Saba Bostani, Arsam Moravvej, Seyed Vahid Mohammadi, Fardin Alizadehsani, Roohallah Shoeibi, Afshin Alinejad-Rokny, Hamid Nahavandi, Saeid Adiban Inst Higher Educ Semnan Iran Urmia Univ Dept Mech Engn Biosyst Nazlu Iran Univ Tehran Dept Exercise Physiol & Hlth Sci Tehran Iran Internship UNSW BioMed Machine Learning Lab Sydney NSW 2052 Australia Deakin Univ IISRI Waurn Ponds Vic 3216 Australia Univ New South Wales UNSW Sydney UNSW Data Sci Hub Sydney NSW 2052 Australia UNSW Sydney Grad Sch Biomed Engn BioMed Machine Learning Lab Sydney NSW 2052 Australia

ISBN: (纸本)9798350398823

Myocarditis occurs when the heart muscle becomes inflamed and inflammation occurs when your body's immune system responds to infections. It can be diagnosed using cardiac magnetic resonance image (MRI), a non-invasive imaging technique with the possibility of operator bias. This paper proposes a hybrid method of deep reinforcement learning-based algorithms and meta-heuristics algorithms. A mutual learning-based artificial bee colony (ML-ABC) is employed for initial weight, which adjusts the candidate food source generated with the higher fitness between two individuals determined by a mutual learning factor. Moreover, a sequential decision-making process investigates the imbalanced classification issue, in which a convolutional neural network (CNN) is used as the foundation for policy architecture. At first, initial weights are produced using the ML-ABC algorithm. After that, the agent receives a sample at each phase and classifies it, obtaining environmental rewards. The minority class receives more rewards than the majority class. Eventually, the agent discovers an ideal strategy with the aid of a specific reward function and a beneficial learning environment. We evaluate our proposed approach on the Z-Alizadeh Sani myocarditis dataset based on standard criteria and demonstrate that the proposed method gives superior myocarditis diagnosis performance.

关键词： Myocarditis diagnosis MRI Deep reinforcement learning Classification ABC algorithm

来源：评论

学校读者我要写书评

暂无评论

Blood-based transcriptomic signature panel identification for cancer diagnosis: benchmarking of feature extraction methods

引用

BRIEFINGS IN BIOINFORMATICS 2022年第5期23卷 bbac315-bbac315页

作者： Vijayan, Abhishek Fatima, Shadma Sowmya, Arcot Vafaee, Fatemeh UNSW Sydney Sch Biotechnol & Biomol Sci BABS AI Enhanced Biomed Lab VafaeeLab Sydney NSW Australia UNSW Fac Engn Governance Sydney NSW Australia Sch Comp Sci & Engn Comp Vis Lab Machine Learning Sydney NSW Australia UNSW Data Sci Hub Sydney NSW Australia Sch BABS AI Enhanced Biomed Lab Bardoli India

Liquid biopsy has shown promise for cancer diagnosis due to its minimally invasive nature and the potential for novel biomarker discovery. However, the low concentration of relevant blood-based biosources and the heterogeneity of samples (i.e. the variability of relative abundance of molecules identified), pose major challenges to biomarker discovery. Moreover, the number of molecular measurements or features (e.g. transcript read counts) per sample could be in the order of several thousand, whereas the number of samples is often substantially lower, leading to the curse of dimensionality. These challenges, among others, elucidate the importance of a robust biomarker panel identification or feature extraction step wherein relevant molecular measurements are identified prior to classification for cancer detection. In this work, we performed a benchmarking study on 12 feature extraction methods using transcriptomic profiles derived from different blood-based biosources. The methods were assessed both in terms of their predictive performance and the robustness of the biomarker panels in diagnosing cancer or stratifying cancer subtypes. While performing the comparison, the feature extraction methods are categorized into feature subset selection methods and transformation methods. A transformation feature extraction method, namely partial least square discriminant analysis, was found to perform consistently superior in terms of classification performance. As part of the benchmarking study, a generic pipeline has been created and made available as an R package to ensure reproducibility of the results and allow for easy extension of this study to other datasets (https://***/Vafaeelab/bloodbased-pancancer-diagnosis).

关键词： feature extraction feature selection liquid biopsy biomarker discovery transcriptomics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：