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arXiv 2023年

作者： Liu, Lin Wang, Xinbo Wang, Yuhao Institute of Natural Sciences MOE-LSC School of Mathematical Sciences CMA-Shanghai SJTU-Yale Joint Center for Biostatistics and Data Science Shanghai Jiao Tong University China Department of Bioinformatics and Biostatistics School of Life Sciences SJTU-Yale Joint Center for Biostatistics and Data Science Shanghai Jiao Tong University China Institute for Interdisciplinary Information Sciences Tsinghua University China Shanghai Artificial Intelligence Laboratory Shanghai Qi Zhi Institute China

Treatment effect estimation under unconfoundedness is a fundamental task in causal inference. In response to the challenge of analyzing high-dimensional datasets collected in substantive fields such as epidemiology, genetics, economics, and social sciences, various methods for treatment effect estimation with high-dimensional nuisance parameters (the outcome regression and the propensity score) have been developed in recent years. However, it is still unclear what is the necessary and sufficient sparsity condition on the nuisance parameters such that we can estimate the treatment effect at 1/√n-rate. In this paper, we propose a new Double-Calibration strategy that corrects the estimation bias of the nuisance parameter estimates computed by regularized high-dimensional techniques and demonstrate that the corresponding Doubly-Calibrated estimator achieves 1/√n-rate as long as one of the nuisance parameters is sparse with sparsity below √n/log p, where p denotes the ambient dimension of the covariates, whereas the other nuisance parameter can be arbitrarily complex and completely misspecified. The Double-Calibration strategy can also be applied to settings other than treatment effect estimation, e.g. regression coefficient estimation in the presence of a diverging number of controls in a semiparametric partially linear model, and local average treatment effect estimation with instrumental variables. Copyright © 2023, The Authors. All rights reserved.

关键词： Regression analysis

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

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Procedia Computer science 2023年 218卷 24-34页

作者： Seifedine Kadry Rubén González Crespo Enrique Herrera-Viedma Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Andalusian Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai 602105 Tamil Nadu India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores.

关键词： Breast cancer Thermal imaging Firefly algorithm Feature optimization Classification

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Federated Learning in Healthcare:A Benchmark Comparison of Engineering and Statistical Approaches for Structured data Analysis

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Health data science 2024年第1期4卷 45-57页

作者： Siqi Li Di Miao Qiming Wu Chuan Hong Danny D’Agostino Xin Li Yilin Ning Yuqing Shang Ziwen Wang Molei Liu Huazhu Fu Marcus Eng Hock Ong Hamed Haddadi Nan Liu Centre for Quantitative Medicine Duke-NUS Medical SchoolSingaporeSingapore Department of Biostatistics and Bioinformatics Duke UniversityDurhamNCUSA Department of Biostatistics Columbia University Mailman School of Public HealthNew YorkNYUSA Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingaporeSingapore Programme in Health Services and Systems Research Duke-NUS Medical SchoolSingaporeSingapore Health Services Research Centre Singapore Health ServicesSingaporeSingapore Department of Emergency Medicine Singapore General HospitalSingaporeSingapore Department of Computing Imperial College LondonLondonEnglandUK Institute of Data Science National University of SingaporeSingaporeSingapore

Background:Federated learning(FL)holds promise for safeguarding data privacy in healthcare *** the term“FL”was originally coined by the engineering community,the statistical field has also developed privacy-preserving algorithms,though these are less *** goal was to bridge this gap with the ffrst comprehensive comparison of FL frameworks from both ***:We assessed 7 FL frameworks,encompassing both engineering-based and statistical FL algorithms,and compared them against local and centralized modeling of logistic regression and least absolute shrinkage and selection operator(Lasso).Our evaluation utilized both simulated data and real-world emergency department data,focusing on comparing both estimated model coefffcients and the performance of model ***:The ffndings reveal that statistical FL algorithms produce much less biased estimates of model ***,engineering-based methods can yield models with slightly better prediction performance,occasionally outperforming both centralized and statistical FL ***:This study underscores the relative strengths and weaknesses of both types of methods,providing recommendations for their selection based on distinct study ***,we emphasize the critical need to raise awareness of and integrate these methods into future applications of FL within the healthcare domain.

关键词： holds utilized slightly

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer

Predicting Microsatellite Instability from Histology Images ...

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IEEE Symposium on bioinformatics and Bioengineering (BIBE)

作者： Yun-Chi Chen Tung Chao Wimaya Nitya Phandita Tzu-Yu Sun Huai-Zhi Wang Yi-Hsien Hsieh Le-Yin Hsu Che Lin Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University (NTU) Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan Data Science Degree Program NTU Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients.

关键词： Attention mechanisms Histopathology Computational modeling Precision medicine Small satellites Immunotherapy Colorectal cancer Predictive models Transformers Genetics

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Efficacy of Warfarin Therapy Guided by Pharmacogenetics: A Real-World Investigation Among Han Taiwanese

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Clinical Therapeutics 2023年第7期45卷 662-670页

作者： Liu, Ting-Yuan Hsu, Hsing-Yu You, Ying-Shu Hsieh, Yow-Wen Lin, Tzu-Ching Peng, Chun-Wei Huang, Hsin-Yi Chang, Shih-Sheng Tsai, Fuu-Jen Million-Person Precision Medicine Initiative Department of Medical Research China Medical University Hospital Taichung Taiwan Department of Pharmacy China Medical University Hospital Taichung Taiwan Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei Taiwan Artificial Intelligence and Data Science National Chung Hsing University Taichung Taiwan Division of Cardiovascular Medicine China Medical University Hospital Taichung Taiwan School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Research China Medical University Hospital Taichung Taiwan School of Chinese Medicine China Medical University Taichung Taiwan Division of Pediatric Genetics Children's Hospital of China Medical University Taichung Taiwan Department of Biotechnology and Bioinformatics Asia University Taichung Taiwan

Purpose: The anticoagulation activity of warfarin in populations with CYP2C9, VKORC1, and CYP4F2 variants differs between individuals and is correlated with poor international normalized ratio (INR) control. Pharmacogenetics-guided warfarin dosing has been successfully developed for patients with genetic variations in recent years. However, few real-world data have been used to investigate the INR and warfarin dosage and the time to target INR. This study examined the largest collection of genetic and clinical real-world data related to warfarin to provide further evidence supporting the benefits of pharmacogenetics in clinical outcomes. Methods: We retrieved a total of 69,610 INR-warfarin records after the index date from 2,613 patients in the China Medical University Hospital database between January 2003 and December 2019. Each INR reading was obtained from the latest laboratory data after the hospital visit date. Patients with a history of malignant neoplasms or pregnancy before the index date were excluded, as were patients without data on INR measurements after the fifth day of prescription, genetic information, or gender variables. The primary outcomes were the INR and warfarin dosage during days 7, 14, 28, 56, and 84 after prescription. The secondary outcome was the time required to reach the INR ranges of 1.5 to 3.0 and >4.0. Findings: A total of 59,643 INR-warfarin records from 2188 patients were retrieved. The average INR was higher for homozygous carriers of the minor allele at CYP2C9 and VKORC1 during the first 7 days (1.83 [1.03] [CYP2C9*1] and 2.46 [1.44] [CYP2C9*3], P < 0.001;1.39 [0.36] [rs9923231 G/G], 1.55 [0.79] [rs9923231 G/A], and 1.96 [1.13] [rs9923231 A/A], P < 0.001) than for the wild-type allele. These patients with variants required lower warfarin doses than those with the wild-type allele during the first 28 days. CYP4F2 variant patients seemed to require higher doses of warfarin than those in the wild-type group;however, no significant d

关键词： Han Taiwanese international normalized ratio Kaplan-Meier estimation pharmacogenetics warfarin

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Thoughts on Non-IID data Impact in Healthcare with Federated Learning Medical Blockchain

Thoughts on Non-IID Data Impact in Healthcare with Federated...

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IEEE International Conference on Cognitive Machine Intelligence (CogMI)

作者： Zon-Yin Shae Kun-Yi Chen Chi-Yu Chang Yuan-Yu Tsai Che-Yi Chou William I. Baskett Chi-Ren Shyu Jeffrey J.P. Tsai Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of M-Commerce and Multimedia Applications Asia University Taichung Taiwan Division of Nephrology Asia University Hospital Taichung Taiwan Institute for Data Science and Informatics University of Missouri Columbia USA Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan

We share the common hypothesis/belief that the more aggregated good quality training data, the better the performance that can be attained by the resulting Artificial Intelligence (AI) model. However, this common belief, in general, is not true in the medical area, since healthcare data sets sourced from different hospitals are often not identically distributed (Non-IID). This imposes severe technical challenges for effectively aggregating the individual hospital data sets together. In this vision paper, instead of offering complete solutions, we will discuss some questions and food for thought with the goal of aiding effective data aggregation and improving federated learning (FL) AI model performance: (1) benchmark and measure the Non-IID degree of medical data sets. (2) include the Non-IID degree metrics in the FL data aggregation mechanism. (3) search for the optimal global model creation strategy among a group of many medical data sets. (4) investigate FL performance better than the centralized learning. This paper will discuss these questions by outlining a visionary approach for exploring a medical blockchain FL mechanism to effectively aggregate medical data across multiple healthcare systems to serve large populations with broad demographics.

关键词： Measurement Federated learning Hospitals Sociology Training data data aggregation data models

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LSD 4.0:an improved database for comparative studies of leaf senescence

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Molecular Horticulture 2022年第1期2卷 303-306页

作者： Jie Cao Yang Zhang Shuya Tan Qi Yang Hou‑Ling Wang Xinli Xia Jingchu Luo Hongwei Guo Zhang Zhang Zhonghai Li National Engineering Research Center for Tree Breeding and Ecological Restoration College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijing 100083China National Genomics Data Center Beijing 100101China China National Center for Bioinformation Chinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China College of Life Sciences Peking UniversityBeijing 100871China Center for Bioinformatics Peking UniversityBeijing 100871China Department of Biology Institute of Plant and Food ScienceSouthern University of Science and Technology(SUSTech)Shenzhen 518055GuangdongChina

Leaf senescence is the final stage of leaf development and involves the active degradation and dynamic transfer of its cellular components to newly growing and storage tissues,which contributes to plant fitness(Gan and Amasino,1997;Lim et al.,2007).The genetic modification of leaf senescence has emerged as a promising strategy for improving nutritional traits and stress tolerance in plants(Rivero et al.,2007).Efforts to dissect the molecular mechanisms underpinning leaf senescence reveal that it is a highly coordinated process regulated by a large number of senescence-associated genes(SAGs)(Guo and Gan,2005;Lim et al.,2007).Functional studies of these SAG genes through reverse genetics strategies and identification of senescence-altered mutants through forward genetic screening have deepened the understanding of leaf senescence(Guo et al.,2021).

关键词： traits senescence emerged

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RePhine: An Integrative Method for Identification of Drug Response-related Transcriptional Regulators

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Genomics, Proteomics & bioinformatics 2021年第4期19卷 534-548页

作者： Xujun Wang Zhengtao Zhang Wenyi Qin Shiyi Liu Cong Liu Georgi ZGenchev Lijian Hui Hongyu Zhao Hui Lu SJTU-Yale Joint Center for Biostatistics and Data Science Department of Bioinformatics and BiostatisticsSchool of Life Science and BiotechnologyShanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Cell Biology CAS Center for Excellence in Molecular Cell ScienceShanghai Institute of Biochemistry and Cell BiologyShanghai 200031China Department of Bioengineering University of Illinois at ChicagoIL 60607USA Department of Genetics School of MedicineYale UniversityNew HavenCT 06511USA Department of Medical Informatics Columbia UniversityNew YorkNY 10032USA Bulgarian Institute for Genomics and Precision Medicine Sofia 1000Bulgaria Department of Biostatistics Yale School of Public Health.New HavenCT 06511USA Institute of Science and Technology for Brain-Inspired Intelligence Fudan UniversityShanghai 200433China Center for Biomedical informatics Shanghai Children’s HospitalShanghai 200040China

Transcriptional regulators(TRs)participate in essential processes in cancer pathogenesis and are critical therapeutic *** of drug response-related TRs from cell line-based compound screening data is often challenging due to low m RNA abundance of TRs,protein modifications,and other confounders(CFs).In this study,we developed a regression-based pharmacogenomic and Ch IP-seq data integration method(Re Phine)to infer the impact of TRs on drug response through integrative analyses of pharmacogenomic and Ch IP-seq *** Phine was evaluated in simulation and pharmacogenomic data and was applied to pan-cancer datasets with the goal of biological *** simulation data with added noises or CFs and in pharmacogenomic data,Re Phine demonstrated an improved performance in comparison with three commonly used methods(including Pearson correlation analysis,logistic regression model,and gene set enrichment analysis).Utilizing Re Phine and Cancer Cell Line Encyclopedia data,we observed that Re Phinederived TR signatures could effectively cluster drugs with different mechanisms of *** Phine predicted that loss-offunction of EZH2/PRC2 reduces cancer cell sensitivity toward the BRAF inhibitor *** validation confirmed that pharmacological EZH2 inhibition increases the resistance of cancer cells to PLX4720 *** results support that Re Phine is a useful tool for inferring drug response-related TRs and for potential therapeutic *** source code for Re Phine is freely available at https://***/coexps/Re Phine.

关键词： Pharmacogenomics ChIP-seq Transcriptional regulator BRAF inhibitor resistance Drug resistance

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A Convolutional Neural Network-based Ancient Sundanese Character Classifier with data Augmentation

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Procedia Computer science 2021年 179卷 195-201页

作者： Alam Ahmad Hidayat Kartika Purwandari Tjeng Wawan Cenggoro Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

With an increasing interest in the digitization effort of ancient manuscripts, ancient character recognition becomes one of the most important areas in the automated document image analysis. In this regard, we propose a Convolutional Neural Network (CNN)-based classifier to recognize the ancient Sundanese characters obtained from a digital collection of Southeast Asian palm leaf manuscripts. In this work, we utilize two different preprocessing techniques for the dataset. The first technique involves the use of geometric transformations, noise background addition, and brightness adjustment to augment the imbalanced samples to be fed into the classifier. The second technique makes use of the Otsu’s threshold method to binarize the characters and only uses the usual geometric transformations for the data augmentation. The proposed network with different data augmentation processes is trained on the training set and tested on the testing set. Image binarization from the second technique can outperform the performance of the CNN-based classifier upon the first technique by achieving a testing accuracy of 97.74%.

关键词： ancient sundanese characters convolutional neural network data augmentation document image analysis Otsu’s threshold method

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Automatic detection of lung nodule in CT scan slices using CNN segmentation schemes: A study

Automatic detection of lung nodule in CT scan slices using C...

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2022 International Conference on Machine Learning and data Engineering, ICMLDE 2022

作者： Kadry, Seifedine Herrera-Viedma, Enrique Crespo, Rubén González Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Research Institute in Data Science and Computational Intelligence University of Granada Andalusian Granada Spain Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja 26006 Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Tamil Nadu Chennai600119 India

The lung is one of the prime respiratory organs in human physiology, and its abnormality will severely disrupt the respiratory system. Lung Nodule (LN) is one of the abnormalities, and early screening and treatment are necessary to reduce its harshness. The proposed work aims to implement the Convolutional-Neural-Network (CNN) segmentation methodology to extract the LN in various lung CT slices, such as axial, coronal, and sagittal planes. This work consists of the following phases;(i) Image collection and pre-processing, (ii) Ground-truth generation, (iii) CNN-supported segmentation, and (iv) Performance evaluation and validation. In this work, the merit of pre-trained CNN segmentation schemes is verified using (i) One-fold training and (ii) Two-fold training methods. The test images for this study are collected from The Cancer Imaging Archive (TCIA) database. The experimental investigation is executed using Python®, and the outcome of this study confirms that the VGG-SegNet helps to get better values of Jaccard (>88%), Dice (>93%), and Accuracy (>96%) compared to other CNN methods. © 2023 The Authors. Published by Elsevier B.V.

关键词： Computerized tomography

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