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TMBstable: a variant caller controls performance variation across heterogeneous sequencing samples

BRIEFINGS IN BIOINFORMATICS 2024年第3期25卷 bbae159-bbae159页

作者： wang, Shenjie Zhu, Xiaoyan wang, xuwen Liu, Yuqian Zhao, Minchao Chang, Zhili wang, Xiaonan Shao, Yang wang, Jiayin Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian Peoples R China Nanjing Geneseeq Technol Inc Nanjing Jiangsu Peoples R China

In cancer genomics, variant calling has advanced, but traditional mean accuracy evaluations are inadequate for biomarkers like tumor mutation burden, which vary significantly across samples, affecting immunotherapy patient selection and threshold settings. In this study, we introduce TMBstable, an innovative method that dynamically selects optimal variant calling strategies for specific genomic regions using a meta-learning framework, distinguishing it from traditional callers with uniform sample-wide strategies. The process begins with segmenting the sample into windows and extracting meta-features for clustering, followed by using a pre-trained meta-model to select suitable algorithms for each cluster, thereby addressing strategy-sample mismatches, reducing performance fluctuations and ensuring consistent performance across various samples. We evaluated TMBstable using both simulated and real non-small cell lung cancer and nasopharyngeal carcinoma samples, comparing it with advanced callers. The assessment, focusing on stability measures, such as the variance and coefficient of variation in false positive rate, false negative rate, precision and recall, involved 300 simulated and 106 real tumor samples. Benchmark results showed TMBstable's superior stability with the lowest variance and coefficient of variation across performance metrics, highlighting its effectiveness in analyzing the counting-based biomarker. The TMBstable algorithm can be accessed at https://***/hello-json/TMBstable for academic usage only.

关键词： sequencing data analysis variant calling counting-based biomarker tumor mutation burden immunotherapy meta-learning approach

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The Supercritical N-Butanol Degradation Process for Recycling Photovoltaic Modules

SSRN

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SSRN 2024年

作者： wang, xuwen Liu, Zhifeng Ruan, Haoda Li, Xinyu Zhang, Lei School of Mechanical Engineering Hefei University of Technology Hefei230009 China Anhui Province Key Laboratory of Low Carbon Recycling Technology and Equipment for Mechanical and Electrical Products Hefei230009 China State Key Laboratory of Environmental Adaptability for lndustrial Products Guangzhou510300 China

The rapid advancement of the photovoltaic (PV) industry has elevated the recycling and reuse of decommissioned PV modules to critical challenges, essential for tackling environmental concerns and achieving resource reutilization. To facilitate the efficient degradation of PV modules and recycling of silicon materials, this study proposes a PV module degradation and recycling process based on supercritical n-butanol. The effects of various factors on the degradation rate of PV modules were systematically evaluated using single-factor studies to select an appropriate range of process parameters for optimization. Employing the response surface methodology (RSM) for the meticulous optimization of process parameters, the optimized parameters were as follows: n-butanol solvent volume of 470 ml, temperature of 328 °C for 83 minutes, resulting in a module degradation rate of up to 99 %. A second-order regression model was established to predict degradation efficiency. The high applicability of the fitted model was verified by comparing the actual degradation rates with the model's predicted values. Finally, the superiority of this recycling method was validated by characterizing the solar cells obtained through the supercritical method and comparing these characteristics with those of cells disassembled mechanically. © 2024, The Authors. All rights reserved.

关键词： Recycling

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Parental category B vaccine hesitancy and associated factors in China: an online cross-sectional survey

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EXPERT REVIEW OF VACCINES 2022年第1期21卷 145-153页

作者： Han, Ying wang, Qiang Zhao, Shuangyu wang, Jianli Dong, Shuheng Cui, Tingting Liu, Minqi Shi, Naiyang Yang, Liuqing Han, Yue Xiu, Shixin wang, xuwen Jin, Hui Southeast Univ Sch Publ Hlth Dept Epidemiol & Hlth Stat Nanjing Peoples R China Southeast Univ Sch Publ Hlth Key Lab Environm Med Engn Minist Educ Nanjing Peoples R China Wuxi Ctr Dis Control & Prevent Wuxi Jiangsu Peoples R China

Background Vaccine hesitancy is one of the top ten health threats. We aimed to (1) assess parental Category B vaccines hesitancy and associated sociodemographic factors in China, and (2) explore the association between attitude toward vaccines and self-reported hesitant behavior. Methods A cross-sectional study was performed through a web-based anonymous online questionnaire survey between 9 February and 7 April 2021. Chinese parents aged >= 18 years with one child aged < 6 years were included to assess vaccine hesitancy using the vaccine-hesitancy scale (VHS). Structural equation model was used to determine relationships between variables. Results Of 2952 Chinese parents included in the analysis, 17.5% were highly hesitant in Category B vaccines. Parents who were younger, less educated, engaged in health-related occupations, and had been vaccinated against influenza in the past year were more hesitant when vaccinating their children (P < 0.001). VHS score accuracy to identify vaccine-hesitant behavior was acceptable, and the optimal cutoff was 37.50 (with 61.96% parental vaccine hesitancy). Parents who lack confidence or believe vaccines were risk were more likely to show vaccine hesitant behavior (P < 0.001). Conclusions In China, effective interventions need to be implemented to eliminate parental Category B vaccines hesitancy.

关键词： Vaccine hesitancy Parents Structural equation model Online survey China

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Delays in routine childhood vaccinations and their relationship with parental vaccine hesitancy: a cross-sectional study in Wuxi, China

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EXPERT REVIEW OF VACCINES 2022年第1期21卷 135-143页

作者： wang, Qiang Xiu, Shixin Yang, Liuqing Han, Ying Huang, Jinxin Cui, Tingting Shi, Naiyang Liu, Minqi wang, xuwen Lu, Bing Jin, Hui Lin, Leesa Southeast Univ Sch Publ Hlth Dept Epidemiol & Hlth Stat 87 Dingjiaqiao Nanjing 210009 Peoples R China Southeast Univ Sch Publ Hlth Key Lab Environm Med Engn Minist Educ Nanjing Peoples R China Wuxi Ctr Dis Control & Prevent Dept Immunizat Planning Wuxi Jiangsu Peoples R China London Sch Hyg & Trop Med Dept Infect Dis Epidemiol London England Hong Kong Sci Pk Lab Data Discovery Hlth D24H Hong Kong Peoples R China

Objectives This study aimed to examine childhood vaccination delay, explore the association between vaccination delay and parental vaccine hesitancy, and assess childhood vaccination delays during the coronavirus disease (COVID)-19 pandemic in China. Methods This cross-sectional survey was conducted in Wuxi City. Participants were recruited from local vaccination clinics. Questionnaires were used to collect information about socio-demographics, vaccine hesitancy, and immunization clinic evaluations. Vaccination records were obtained from the Jiangsu Information Management System of Vaccination Cases. Results Overall, 2728 participants were included. The coverage for seven category A vaccines (Expanded Program on Immunization (EPI)) was more than 95% at 24 months. The proportion of children vaccinated in a timely manner was the highest for the first dose of the hepatitis B vaccine (91.6%) and the lowest for the Bacillus-Calmette-Guerin vaccine (44.6%). More than 50% of the planned vaccinations were delayed in February and March 2020. The Vaccine Hesitancy Scale scores were not associated with vaccination delay (P = 0.842). Children's vaccination delays were negatively associated with parents who reported convenient access to clinics and satisfaction with immunization services (P = 0.020, P = 0.045). Conclusions EPI is highly successful in China. Despite vaccination delays due to the COVID-19 pandemic, coverage was recovered after lockdown restrictions were eased.

关键词： Childhood vaccination delay timeliness vaccine hesitancy COVID-19 pandemic

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Attribute Reduction Method Using Adaptive Genetic Algorithm and Particle Swarm Optimization 2021

Attribute Reduction Method Using Adaptive Genetic Algorithm ...

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7th International Conference on Computing and Artificial Intelligence, ICCAI 2021

作者： Wu, Shangzhi Zhang, Fan wang, xuwen Xia, Ning Northwest Normal University College of Computer Science and Engineering Lanzhou China

ISBN: (纸本)9781450389501

Attribute reduction is one of the core contents in rough set knowledge discovery, how to find the minimal attribute reduction, proposes an attribute reduction method using adaptive Genetic Algorithm(GA) and Particle Swarm Optimization(PSO). On the basis of the condition attributes for decision attribute support degree, the method reset the fitness function, can dynamically adjust the parameters of the function, thus to ensure the obtained results for minimal reduction, combined with the genetic algorithm of adaptive crossover and mutation operation, to ensure that the particles in the feasible solution can be fully retain and use, the method to strengthen the local search ability at the same time, but also keep the global search ability. Experiments results show that the method has obvious advantages in solving the minimum attribute reduction. © 2021 ACM.

关键词： Genetic algorithms

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RMComBat: A Batch Effect Correction Algorithm for Repeated Measurement Sequencing Data to Prevent Overcorrection

RMComBat: A Batch Effect Correction Algorithm for Repeated M...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yuqian Liu Zhaoxing Wei Jiayin wang Xiaoyan Zhu Ruoyu Liu xuwen wang Shenjie wang Xin Lai School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Department of Respiratory and Critical Care Medicine The Second Affiliated Hospital of Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Batch effects, caused by non-biological variations such as differences in laboratory conditions, reagent lots, or personnel, are a substantial source of noise in gene expression data. Accurately correcting these effects is crucial for valid biological inferences. However, the majority of existing batch effect correction algorithms are prone to overcorrection, where biologically meaningful signals are mistakenly identified as noise, especially in repeated measurement studies where time is confounded with batch. The failure to accurately distinguish between batch-related and biologically relevant variation leads to a loss of critical biological information. This paper presents RMComBat, an enhancement of the widely-used ComBat framework, which addresses this limitation by replacing the general linear model with a linear mixed-effects model. RMComBat incorporates subject-specific random intercepts to correct for sample correlation and enhance the preservation of biological signals. We tested RMComBat and several popular algorithms on simulated and real repeated measurement gene expression datasets, evaluating their performance through visual inspections and quantitative metrics. Results indicate that although most algorithms can reduce batch effects, they often do so at the cost of removing true biological signals. RMComBat demonstrates superior performance in preventing overcorrection, providing a more balanced and biologically informative correction in repeated measurement studies, so making it a valuable tool for improving the accuracy of gene expression analyses.

关键词： Visualization Sequential analysis Biological system modeling Noise Biomedical measurement Inspection Time measurement Gene expression Personnel Noise measurement

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An Enhanced Multiple Correction Method with Limited Independent Effective SNPs

An Enhanced Multiple Correction Method with Limited Independ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xin Lai Xiaohai Yang Jiayin wang Xiaoyan Zhu Yuqian Liu Ruoyu Liu xuwen wang Shenjie wang School of Computer Science and Technology Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

In genome-wide association studies (GWAS) and candidate gene studies (CGS), appropriate multiple testing correction methods can effectively address linkage disequilibrium (LD) blocks and are crucial for controlling the family-wise error rate (FWER) while ensuring the reliability of results. In our recent research, we observed that when the number of independent effective Single Nucleotide Polymorphisms (SNPs) is relatively small, existing multiple testing correction methods struggle to effectively control the FWER at 0.05 and lack robustness. This study proposes an enhanced method that builds upon the Moskvina and Schmidt approach by incorporating additional SNP correlation information, allowing for precise control of the FWER at 0.05 with limited independent effective SNPs. We also leverage Monte Carlo integration on a GPU to accelerate the computation of significance thresholds. Our method was evaluated through both simulation studies and real genotype datasets, demonstrating superior performance and robustness compared to existing methods.

关键词： Couplings Monte Carlo methods Correlation Error analysis Graphics processing units Genomics Robustness Bioinformatics Genotypes

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Comma: A Collaborative Medical Text Annotation Platform

Comma: A Collaborative Medical Text Annotation Platform

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Hetong Ma Xiaowei Xu xuwen wang Zhen Guo Jiao Li Chinese Academy of Medical Sciences&Peking Union Medical College Institute of Medical Information/Library Beijing China

High-quality annotated data plays an important role in data-driven or machine-learning-based medical studies. Large amounts of valuable data existed in free text records such as Electronic Health Records. Manual annotation is paramount for intelligent algorithm applications where plentiful structured data could be extracted. With the exponential growth of medical data, large-scale collaborative annotation is required where different annotation tools should be used. We investigated the existing annotation platform and discovered partial common problems that need to be solved, e.g., lack of integrated annotation workflow, absence of reasonable review and review-assisting process, frequent lexicon upload failure, prolonged auto annotation, unified export standards, etc. In response to the above requirements, we developed Comma, a collaborative medical annotation platform that allows English and Chinese annotation. Comma supports functions involving multiple ways with high success rates to upload documents, self-defined structured entity types, customized auto annotation, annotators capability assessment, various collaborative modes, statistics display, self-determined exportation, etc. It could provide a user-friendly interface, diverse functionalities, and fluent interactions for people demanding annotations. Comma is available online at http://***.

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Enabling Adaptive CNV Detection through A Novel Predictive Control Framework

Enabling Adaptive CNV Detection through A Novel Predictive C...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yuqian Liu Jiajing Yuan Xiaoyan Zhu Xin Lai Ruoyu Liu xuwen wang Jiayin wang School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Department of Respiratory and Critical Care Medicine The Second Affiliated Hospital of Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Accurate detection of copy number variations (CNVs) from sequencing data is crucial in many complex traits and diseases research. Although many CNV detection algorithms have been developed, challenges in precisely identifying CNVs persist. The core statistical model of these algorithms cannot self-adjust, which limits their adaptability to heterogeneous samples and reduces detection accuracy. address this challenge, we reframed the CNV detection problem as a quality control issue and incorporated adaptive mechanisms. We developed adapCNV, a novel adaptive CNV detection framework that integrates machine learning with optimization control. This framework enables dynamic adaptation of primary parameters based on sample features. We defined a quantifiable metric, RD fluctuation values, to assess signal characteristics when the algorithm accurately detects CNVs. We then employed machine learning techniques extract features from panel sequencing data, select initial parameter values for samples, and determine optimal RD fluctuation values. By adopting adaptive model predictive control (AMPC), adapCNV performs optimizations within rolling window. It dynamically adjusts the primary parameters based on error feedback from RD fluctuation values. This adaptive control strategy enables dynamic adjustment automatically match the characteristics of panel sequencing samples, significantly enhancing overall detection quality. The performance of this framework was validated with simulated data. Comparative analysis demonstrated that the proposed method outperforms the baseline approach, particularly in detecting small CNVs. The adapCNV framework is particularly suitable for panel sequencing, which may have broad applications in clinical practice. This novel approach from quality control perspective introduces a new paradigm for CNV detection.

关键词： Sequential analysis Fluctuations Machine learning algorithms Accuracy Quality control Machine learning Feature extraction Bioinformatics Optimization Predictive control

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Multi-Objective Policy Monitoring Method for Epidemic Control

Multi-Objective Policy Monitoring Method for Epidemic Contro...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xin Lai Rundong Fan Ruoyu Liu Jiayin wang Xiaoyan Zhu Yuqian Liu xuwen wang Shenjie wang School of Computer Science and Technology Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

In the face of emerging infectious diseases such as COVID-19, timely government intervention is crucial, as swift policy actions can effectively prevent greater losses. However, policymakers often need to balance multiple conflicting objectives. The lack of high-quality data and suitable analytical tools poses significant challenges for policy evaluation, especially in multi-objective decision-making, where accurately assessing the impact of interventions becomes even more difficult. To address this issue, this paper proposes a real-time data-driven policy monitoring method for dynamically tracking the effects of policy interventions. We introduce a new non-parametric one-sided test control chart, leveraging the interpretability and ease of implementation of control charts to monitor risk levels across various policies. Experimental results demonstrate the effectiveness of this method in policy monitoring.

关键词： COVID-19 Epidemics Infectious diseases Government Process control Control charts Stability analysis Real-time systems Biomedical monitoring Monitoring

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