Author: "Juexiao Zhou" - Searchworks@Jio Institute Digital Library Search Results

42 results on '"Juexiao Zhou"'

1. Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differences

Author: Yuetan Chu, Gongning Luo, Longxi Zhou, Shaodong Cao, Guolin Ma, Xianglin Meng, Juexiao Zhou, Changchun Yang, Dexuan Xie, Dan Mu, Ricardo Henao, Gianluca Setti, Xigang Xiao, Lianming Wu, Zhaowen Qiu, and Xin Gao
Subjects: Science
Abstract: Abstract Pulmonary artery-vein segmentation is critical for disease diagnosis and surgical planning. Traditional methods rely on Computed Tomography Pulmonary Angiography (CTPA), which requires contrast agents with potential health risks. Non-contrast CT, a safer and more widely available approach, however, has long been considered impossible for this task. Here we propose High-abundant Pulmonary Artery-vein Segmentation (HiPaS), enabling accurate segmentation across both non-contrast CT and CTPA at multiple resolutions. HiPaS integrates spatial normalization with an iterative segmentation strategy, leveraging lower-level vessel segmentations as priors for higher-level segmentations. Trained on a multi-center dataset comprising 1073 CT volumes with manual annotations, HiPaS achieves superior performance (dice score: 91.8%, sensitivity: 98.0%) and demonstrates non-inferiority on non-contrast CT compared to CTPA. Furthermore, HiPaS enables large-scale analysis of 11,784 participants, revealing associations between vessel abundance and sex, age, and diseases, under lung-volume control. HiPaS represents a promising, non-invasive approach for clinical diagnostics and anatomical research.
Published: 2025
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2. Unveiling the power of language models in chemical research question answering

Author: Xiuying Chen, Tairan Wang, Taicheng Guo, Kehan Guo, Juexiao Zhou, Haoyang Li, Zirui Song, Xin Gao, and Xiangliang Zhang
Subjects: Chemistry, QD1-999
Abstract: Abstract While the abilities of language models are thoroughly evaluated in areas like general domains and biomedicine, academic chemistry remains less explored. Chemical QA tools also play a crucial role in both education and research by effectively translating complex chemical information into an understandable format. Addressing this gap, we introduce ScholarChemQA, a large-scale QA dataset constructed from chemical papers. Specifically, the questions are from paper titles with a question mark, and the multi-choice answers are reasoned out based on the corresponding abstracts. This dataset reflects typical real-world challenges, including an imbalanced data distribution and a substantial amount of unlabeled data that can be potentially useful. Correspondingly, we introduce a ChemMatch model, specifically designed to effectively answer chemical questions by fully leveraging our collected data. Experiments show that Large Language Models (LLMs) still have significant room for improvement in the field of chemistry. Moreover, ChemMatch significantly outperforms recent similar-scale baselines: https://github.com/iriscxy/chemmatch .
Published: 2025
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3. Pre-trained multimodal large language model enhances dermatological diagnosis using SkinGPT-4

Author: Juexiao Zhou, Xiaonan He, Liyuan Sun, Jiannan Xu, Xiuying Chen, Yuetan Chu, Longxi Zhou, Xingyu Liao, Bin Zhang, Shawn Afvari, and Xin Gao
Subjects: Science
Abstract: Abstract Large language models (LLMs) are seen to have tremendous potential in advancing medical diagnosis recently, particularly in dermatological diagnosis, which is a very important task as skin and subcutaneous diseases rank high among the leading contributors to the global burden of nonfatal diseases. Here we present SkinGPT-4, which is an interactive dermatology diagnostic system based on multimodal large language models. We have aligned a pre-trained vision transformer with an LLM named Llama-2-13b-chat by collecting an extensive collection of skin disease images (comprising 52,929 publicly available and proprietary images) along with clinical concepts and doctors’ notes, and designing a two-step training strategy. We have quantitatively evaluated SkinGPT-4 on 150 real-life cases with board-certified dermatologists. With SkinGPT-4, users could upload their own skin photos for diagnosis, and the system could autonomously evaluate the images, identify the characteristics and categories of the skin conditions, perform in-depth analysis, and provide interactive treatment recommendations.
Published: 2024
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4. An AI Agent for Fully Automated Multi‐Omic Analyses

Author: Juexiao Zhou, Bin Zhang, Guowei Li, Xiuying Chen, Haoyang Li, Xiaopeng Xu, Siyuan Chen, Wenjia He, Chencheng Xu, Liwei Liu, and Xin Gao
Subjects: agent, bioinformatics, large language model, omics analysis, Science
Abstract: Abstract With the fast‐growing and evolving omics data, the demand for streamlined and adaptable tools to handle bioinformatics analysis continues to grow. In response to this need, Automated Bioinformatics Analysis (AutoBA) is introduced, an autonomous AI agent designed explicitly for fully automated multi‐omic analyses based on large language models (LLMs). AutoBA simplifies the analytical process by requiring minimal user input while delivering detailed step‐by‐step plans for various bioinformatics tasks. AutoBA's unique capacity to self‐design analysis processes based on input data variations further underscores its versatility. Compared with online bioinformatic services, AutoBA offers multiple LLM backends, with options for both online and local usage, prioritizing data security and user privacy. In comparison to ChatGPT and open‐source LLMs, an automated code repair (ACR) mechanism in AutoBA is designed to improve its stability in automated end‐to‐end bioinformatics analysis tasks. Moreover, different from the predefined pipeline, AutoBA has adaptability in sync with emerging bioinformatics tools. Overall, AutoBA represents an advanced and convenient tool, offering robustness and adaptability for conventional multi‐omic analyses.
Published: 2024
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5. A unified method to revoke the private data of patients in intelligent healthcare with audit to forget

Author: Juexiao Zhou, Haoyang Li, Xingyu Liao, Bin Zhang, Wenjia He, Zhongxiao Li, Longxi Zhou, and Xin Gao
Subjects: Science
Abstract: Abstract Revoking personal private data is one of the basic human rights. However, such right is often overlooked or infringed upon due to the increasing collection and use of patient data for model training. In order to secure patients’ right to be forgotten, we proposed a solution by using auditing to guide the forgetting process, where auditing means determining whether a dataset has been used to train the model and forgetting requires the information of a query dataset to be forgotten from the target model. We unified these two tasks by introducing an approach called knowledge purification. To implement our solution, we developed an audit to forget software (AFS), which is able to evaluate and revoke patients’ private data from pre-trained deep learning models. Here, we show the usability of AFS and its application potential in real-world intelligent healthcare to enhance privacy protection and data revocation rights.
Published: 2023
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6. AB-Gen: Antibody Library Design with Generative Pre-trained Transformer and Deep Reinforcement Learning

Author: Xiaopeng Xu, Tiantian Xu, Juexiao Zhou, Xingyu Liao, Ruochi Zhang, Yu Wang, Lu Zhang, and Xin Gao
Subjects: Protein design, Transformer, Reinforcement learning, Generative modeling, Multi-objective optimization, Biology (General), QH301-705.5, Computer applications to medicine. Medical informatics, R858-859.7
Abstract: Antibody leads must fulfill multiple desirable properties to be clinical candidates. Primarily due to the low throughput in the experimental procedure, the need for such multi-property optimization causes the bottleneck in preclinical antibody discovery and development, because addressing one issue usually causes another. We developed a reinforcement learning (RL) method, named AB-Gen, for antibody library design using a generative pre-trained transformer (GPT) as the policy network of the RL agent. We showed that this model can learn the antibody space of heavy chain complementarity determining region 3 (CDRH3) and generate sequences with similar property distributions. Besides, when using human epidermal growth factor receptor-2 (HER2) as the target, the agent model of AB-Gen was able to generate novel CDRH3 sequences that fulfill multi-property constraints. Totally, 509 generated sequences were able to pass all property filters, and three highly conserved residues were identified. The importance of these residues was further demonstrated by molecular dynamics simulations, consolidating that the agent model was capable of grasping important information in this complex optimization task. Overall, the AB-Gen method is able to design novel antibody sequences with an improved success rate than the traditional propose-then-filter approach. It has the potential to be used in practical antibody design, thus empowering the antibody discovery and development process. The source code of AB-Gen is freely available at Zenodo (https://doi.org/10.5281/zenodo.7657016) and BioCode (https://ngdc.cncb.ac.cn/biocode/tools/BT007341).
Published: 2023
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7. Repetitive DNA sequence detection and its role in the human genome

Author: Xingyu Liao, Wufei Zhu, Juexiao Zhou, Haoyang Li, Xiaopeng Xu, Bin Zhang, and Xin Gao
Subjects: Biology (General), QH301-705.5
Abstract: Abstract Repetitive DNA sequences playing critical roles in driving evolution, inducing variation, and regulating gene expression. In this review, we summarized the definition, arrangement, and structural characteristics of repeats. Besides, we introduced diverse biological functions of repeats and reviewed existing methods for automatic repeat detection, classification, and masking. Finally, we analyzed the type, structure, and regulation of repeats in the human genome and their role in the induction of complex diseases. We believe that this review will facilitate a comprehensive understanding of repeats and provide guidance for repeat annotation and in-depth exploration of its association with human diseases.
Published: 2023
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8. Optimization of binding affinities in chemical space with generative pre-trained transformer and deep reinforcement learning [version 2; peer review: 2 approved, 3 approved with reservations]

Author: Xiaopeng Xu, Juexiao Zhou, Xin Gao, Yu Wang, Xingyu Liao, Zhongxiao Li, Ruochi Zhang, Chen Zhu, and Qing Zhan
Subjects: Drug design, transformers, reinforcement learning, molecular docking, hit discovery, eng, Medicine, Science
Abstract: Background The key challenge in drug discovery is to discover novel compounds with desirable properties. Among the properties, binding affinity to a target is one of the prerequisites and usually evaluated by molecular docking or quantitative structure activity relationship (QSAR) models. Methods In this study, we developed SGPT-RL, which uses a generative pre-trained transformer (GPT) as the policy network of the reinforcement learning (RL) agent to optimize the binding affinity to a target. SGPT-RL was evaluated on the Moses distribution learning benchmark and two goal-directed generation tasks, with Dopamine Receptor D2 (DRD2) and Angiotensin-Converting Enzyme 2 (ACE2) as the targets. Both QSAR model and molecular docking were implemented as the optimization goals in the tasks. The popular Reinvent method was used as the baseline for comparison. Results The results on the Moses benchmark showed that SGPT-RL learned good property distributions and generated molecules with high validity and novelty. On the two goal-directed generation tasks, both SGPT-RL and Reinvent were able to generate valid molecules with improved target scores. The SGPT-RL method achieved better results than Reinvent on the ACE2 task, where molecular docking was used as the optimization goal. Further analysis shows that SGPT-RL learned conserved scaffold patterns during exploration. Conclusions The superior performance of SGPT-RL in the ACE2 task indicates that it can be applied to the virtual screening process where molecular docking is widely used as the criteria. Besides, the scaffold patterns learned by SGPT-RL during the exploration process can assist chemists to better design and discover novel lead candidates.
Published: 2024
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9. Predicting the antigenic evolution of SARS-COV-2 with deep learning

Author: Wenkai Han, Ningning Chen, Xinzhou Xu, Adil Sahil, Juexiao Zhou, Zhongxiao Li, Huawen Zhong, Elva Gao, Ruochi Zhang, Yu Wang, Shiwei Sun, Peter Pak-Hang Cheung, and Xin Gao
Subjects: Science
Abstract: Abstract The relentless evolution of SARS-CoV-2 poses a significant threat to public health, as it adapts to immune pressure from vaccines and natural infections. Gaining insights into potential antigenic changes is critical but challenging due to the vast sequence space. Here, we introduce the Machine Learning-guided Antigenic Evolution Prediction (MLAEP), which combines structure modeling, multi-task learning, and genetic algorithms to predict the viral fitness landscape and explore antigenic evolution via in silico directed evolution. By analyzing existing SARS-CoV-2 variants, MLAEP accurately infers variant order along antigenic evolutionary trajectories, correlating with corresponding sampling time. Our approach identified novel mutations in immunocompromised COVID-19 patients and emerging variants like XBB1.5. Additionally, MLAEP predictions were validated through in vitro neutralizing antibody binding assays, demonstrating that the predicted variants exhibited enhanced immune evasion. By profiling existing variants and predicting potential antigenic changes, MLAEP aids in vaccine development and enhances preparedness against future SARS-CoV-2 variants.
Published: 2023
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10. A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics

Author: Haoyang Li, Juexiao Zhou, Zhongxiao Li, Siyuan Chen, Xingyu Liao, Bin Zhang, Ruochi Zhang, Yu Wang, Shiwei Sun, and Xin Gao
Subjects: Science
Abstract: This study comprehensively benchmarks 18 state-of-the-art methods for cellular deconvolution of spatial transcriptomics and provide decision-tree-style guidelines and recommendations for method selection.
Published: 2023
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11. Annotating TSSs in Multiple Cell Types Based on DNA Sequence and RNA-seq Data via DeeReCT-TSS

Author: Juexiao Zhou, Bin Zhang, Haoyang Li, Longxi Zhou, Zhongxiao Li, Yongkang Long, Wenkai Han, Mengran Wang, Huanhuan Cui, Jingjing Li, Wei Chen, and Xin Gao
Subjects: Transcription start site, Machine learning, Deep learning, Meta-learning, RNA sequencing, Biology (General), QH301-705.5, Computer applications to medicine. Medical informatics, R858-859.7
Abstract: The accurate annotation of transcription start sites (TSSs) and their usage are critical for the mechanistic understanding of gene regulation in different biological contexts. To fulfill this, specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner, and various computational tools have also been developed for in silico prediction of TSSs solely based on genomic sequences. Most of these computational tools cast the problem as a binary classification task on a balanced dataset, thus resulting in drastic false positive predictions when applied on the genome scale. Here, we present DeeReCT-TSS, a deep learning-based method that is capable of identifying TSSs across the whole genome based on both DNA sequence and conventional RNA sequencing data. We show that by effectively incorporating these two sources of information, DeeReCT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell types. Furthermore, we develop a meta-learning-based extension for simultaneous TSS annotations on 10 cell types, which enables the identification of cell type-specific TSSs. Finally, we demonstrate the high precision of DeeReCT-TSS on two independent datasets by correlating our predicted TSSs with experimentally defined TSS chromatin states. The source code for DeeReCT-TSS is available at https://github.com/JoshuaChou2018/DeeReCT-TSS_release and https://ngdc.cncb.ac.cn/biocode/tools/BT007316.
Published: 2022
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12. DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning

Author: Zhongxiao Li, Yisheng Li, Bin Zhang, Yu Li, Yongkang Long, Juexiao Zhou, Xudong Zou, Min Zhang, Yuhui Hu, Wei Chen, and Xin Gao
Subjects: Polyadenylation, Gene regulation, Sequence analysis, Deep learning, Bioinformatics, Biology (General), QH301-705.5, Computer applications to medicine. Medical informatics, R858-859.7
Abstract: Alternative polyadenylation (APA) is a crucial step in post-transcriptional regulation. Previous bioinformatic studies have mainly focused on the recognition of polyadenylation sites (PASs) in a given genomic sequence, which is a binary classification problem. Recently, computational methods for predicting the usage level of alternative PASs in the same gene have been proposed. However, all of them cast the problem as a non-quantitative pairwise comparison task and do not take the competition among multiple PASs into account. To address this, here we propose a deep learning architecture, Deep Regulatory Code and Tools for Alternative Polyadenylation (DeeReCT-APA), to quantitatively predict the usage of all alternative PASs of a given gene. To accommodate different genes with potentially different numbers of PASs, DeeReCT-APA treats the problem as a regression task with a variable-length target. Based on a convolutional neural network-long short-term memory (CNN-LSTM) architecture, DeeReCT-APA extracts sequence features with CNN layers, uses bidirectional LSTM to explicitly model the interactions among competing PASs, and outputs percentage scores representing the usage levels of all PASs of a gene. In addition to the fact that only our method can quantitatively predict the usage of all the PASs within a gene, we show that our method consistently outperforms other existing methods on three different tasks for which they are trained: pairwise comparison task, highest usage prediction task, and ranking task. Finally, we demonstrate that our method can be used to predict the effect of genetic variations on APA patterns and sheds light on future mechanistic understanding in APA regulation. Our code and data are available at https://github.com/lzx325/DeeReCT-APA-repo.
Published: 2022
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13. Pan‐tissue analysis of allelic alternative polyadenylation suggests widespread functional regulation

Author: Yisheng Li, Bernhard Schaefke, Xudong Zou, Min Zhang, Florian Heyd, Wei Sun, Bin Zhang, Guipeng Li, Weizheng Liang, Yuhao He, Juexiao Zhou, Yunfei Li, Liang Fang, Yuhui Hu, and Wei Chen
Subjects: alternative polyadenylation, error hypothesis, gene regulation, neutral theory of molecular evolution, regulatory evolution, Biology (General), QH301-705.5, Medicine (General), R5-920
Abstract: Abstract Alternative polyadenylation (APA) is a major layer of gene regulation. However, it has recently been argued that most APA represents molecular noise. To clarify their functional relevance and evolution, we quantified allele‐specific APA patterns in multiple tissues from an F1 hybrid mouse. We found a clearly negative correlation between gene expression and APA diversity for the 2,866 genes (24.9%) with a dominant polyadenylation site (PAS) usage above or equal to 90%, suggesting that their other PASs represent molecular errors. Among the remaining genes with multiple PASs, 3,971 genes (34.5%) express two or more isoforms with potentially functional importance. Interestingly, the genes with potentially functional minor PASs specific to neuronal tissues often express two APA isoforms with distinct subcellular localizations. Furthermore, our analysis of cis‐APA divergence shows its pattern across tissues is distinct from that of gene expression. Finally, we demonstrate that the relative usage of alternative PASs is not only affected by their cis‐regulatory elements, but also by potential coupling between transcriptional and APA regulation as well as competition kinetics between alternative sites.
Published: 2020
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14. An Interpretable Computer-Aided Diagnosis Method for Periodontitis From Panoramic Radiographs

Author: Haoyang Li, Juexiao Zhou, Yi Zhou, Qiang Chen, Yangyang She, Feng Gao, Ying Xu, Jieyu Chen, and Xin Gao
Subjects: teeth segmentation and numbering, periodontitis diagnosis, panoramic radiograph, computer-aided diagnostics, interpretable model, Physiology, QP1-981
Abstract: Periodontitis is a prevalent and irreversible chronic inflammatory disease both in developed and developing countries, and affects about 20–50% of the global population. The tool for automatically diagnosing periodontitis is highly demanded to screen at-risk people for periodontitis and its early detection could prevent the onset of tooth loss, especially in local communities and health care settings with limited dental professionals. In the medical field, doctors need to understand and trust the decisions made by computational models and developing interpretable models is crucial for disease diagnosis. Based on these considerations, we propose an interpretable method called Deetal-Perio to predict the severity degree of periodontitis in dental panoramic radiographs. In our method, alveolar bone loss (ABL), the clinical hallmark for periodontitis diagnosis, could be interpreted as the key feature. To calculate ABL, we also propose a method for teeth numbering and segmentation. First, Deetal-Perio segments and indexes the individual tooth via Mask R-CNN combined with a novel calibration method. Next, Deetal-Perio segments the contour of the alveolar bone and calculates a ratio for individual tooth to represent ABL. Finally, Deetal-Perio predicts the severity degree of periodontitis given the ratios of all the teeth. The Macro F1-score and accuracy of the periodontitis prediction task in our method reach 0.894 and 0.896, respectively, on Suzhou data set, and 0.820 and 0.824, respectively on Zhongshan data set. The entire architecture could not only outperform state-of-the-art methods and show robustness on two data sets in both periodontitis prediction, and teeth numbering and segmentation tasks, but also be interpretable for doctors to understand the reason why Deetal-Perio works so well.
Published: 2021
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15. CaMeRe: A Novel Tool for Inference of Cancer Metabolic Reprogramming

Author: Haoyang Li, Juexiao Zhou, Huiyan Sun, Zhaowen Qiu, Xin Gao, and Ying Xu
Subjects: metabolic reprogramming, web server, glycosylation, cancer, path-searching, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract: Metabolic reprogramming is prevalent in cancer, largely due to its altered chemical environments such as the distinct intracellular concentrations of O2, H2O2 and H+, compared to those in normal tissue cells. The reprogrammed metabolisms are believed to play essential roles in cancer formation and progression. However, it is highly challenging to elucidate how individual normal metabolisms are altered in a cancer-promoting environment; hence for many metabolisms, our knowledge about how they are changed is limited. We present a novel method, CaMeRe (CAncer MEtabolic REprogramming), for identifying metabolic pathways in cancer tissues. Based on the specified starting and ending compounds, along with gene expression data of given cancer tissue samples, CaMeRe identifies metabolic pathways connecting the two compounds via collection of compatible enzymes, which are most consistent with the provided gene-expression data. In addition, cancer-specific knowledge, such as the expression level of bottleneck enzymes in the pathways, is incorporated into the search process, to enable accurate inference of cancer-specific metabolic pathways. We have applied this tool to predict the altered sugar-energy metabolism in cancer, referred to as the Warburg effect, and found the prediction result is highly accurate by checking the appearance and ranking of those key pathways in the results of CaMeRe. Computational evaluation indicates that the tool is fast and capable of handling large metabolic network inference in cancer tissues. Hence, we believe that CaMeRe offers a powerful tool to cancer researchers for their discovery of reprogrammed metabolisms in cancer. The URL of CaMeRe is http://csbl.bmb.uga.edu/CaMeRe/.
Published: 2020
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16. Optimization of binding affinities in chemical space with generative pre-trained transformer and deep reinforcement learning [version 2; peer review: 3 approved, 2 approved with reservations]

Author: Xiaopeng Xu, Juexiao Zhou, Chen Zhu, Qing Zhan, Zhongxiao Li, Ruochi Zhang, Yu Wang, Xingyu Liao, and Xin Gao
Subjects: Research Article, Articles, Drug design, transformers, reinforcement learning, molecular docking, hit discovery
Abstract: Background The key challenge in drug discovery is to discover novel compounds with desirable properties. Among the properties, binding affinity to a target is one of the prerequisites and usually evaluated by molecular docking or quantitative structure activity relationship (QSAR) models. Methods In this study, we developed SGPT-RL, which uses a generative pre-trained transformer (GPT) as the policy network of the reinforcement learning (RL) agent to optimize the binding affinity to a target. SGPT-RL was evaluated on the Moses distribution learning benchmark and two goal-directed generation tasks, with Dopamine Receptor D2 (DRD2) and Angiotensin-Converting Enzyme 2 (ACE2) as the targets. Both QSAR model and molecular docking were implemented as the optimization goals in the tasks. The popular Reinvent method was used as the baseline for comparison. Results The results on the Moses benchmark showed that SGPT-RL learned good property distributions and generated molecules with high validity and novelty. On the two goal-directed generation tasks, both SGPT-RL and Reinvent were able to generate valid molecules with improved target scores. The SGPT-RL method achieved better results than Reinvent on the ACE2 task, where molecular docking was used as the optimization goal. Further analysis shows that SGPT-RL learned conserved scaffold patterns during exploration. Conclusions The superior performance of SGPT-RL in the ACE2 task indicates that it can be applied to the virtual screening process where molecular docking is widely used as the criteria. Besides, the scaffold patterns learned by SGPT-RL during the exploration process can assist chemists to better design and discover novel lead candidates.
Published: 2024
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17. ScholarChemQA: Unveiling the Power of Language Models in Chemical Research Question Answering.

Author: Xiuying Chen, Tairan Wang, Taicheng Guo, Kehan Guo, Juexiao Zhou, Haoyang Li, Mingchen Zhuge, Jürgen Schmidhuber, Xin Gao 0001, and Xiangliang Zhang 0001
Published: 2024
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18. SkinCAP: A Multi-modal Dermatology Dataset Annotated with Rich Medical Captions.

Author: Juexiao Zhou, Liyuan Sun, Yan Xu 0014, Wenbin Liu, Shawn Afvari, Zhongyi Han, Jiaoyan Song, Yongzhi Ji, Xiaonan He, and Xin Gao 0001
Published: 2024
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19. Deep learning-driven pulmonary arteries and veins segmentation reveals demography-associated pulmonary vasculature anatomy.

Author: Yuetan Chu, Gongning Luo, Longxi Zhou, Shaodong Cao, Guolin Ma, Xianglin Meng, Juexiao Zhou, Changchun Yang, Dexuan Xie, Ricardo Henao, Xigang Xiao, Lianming Wu, Zhaowen Qiu, and Xin Gao 0001
Published: 2024
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20. HELM-GPT: de novo macrocyclic peptide design using generative pre-trained transformer.

Author: Xiaopeng Xu, Chencheng Xu, Wenjia He, Lesong Wei, Haoyang Li, Juexiao Zhou, Ruochi Zhang, Yu Wang, Yuanpeng Xiong, and Xin Gao 0001
Published: 2024
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21. KaryoXpert: An accurate chromosome segmentation and classification framework for karyotyping analysis without training with manually labeled metaphase-image mask annotations.

Author: Siyuan Chen, Kaichuang Zhang, Jingdong Hu, Na Li, Ao Xu, Haoyang Li, Juexiao Zhou, Chao Huang, Yongguo Yu, and Xin Gao 0001
Published: 2024
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22. Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI.

Author: Juexiao Zhou, Longxi Zhou, Di Wang 0015, Xiaopeng Xu, Haoyang Li, Yuetan Chu, Wenkai Han, and Xin Gao 0001
Published: 2024
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23. Optimization of binding affinities in chemical space with generative pre-trained transformer and deep reinforcement learning [version 1; peer review: 5 approved with reservations]

Author: Xiaopeng Xu, Juexiao Zhou, Chen Zhu, Qing Zhan, Zhongxiao Li, Ruochi Zhang, Yu Wang, Xingyu Liao, and Xin Gao
Subjects: Research Article, Articles, Drug design, transformers, reinforcement learning, molecular docking, hit discovery
Abstract: Background: The key challenge in drug discovery is to discover novel compounds with desirable properties. Among the properties, binding affinity to a target is one of the prerequisites and usually evaluated by molecular docking or quantitative structure activity relationship (QSAR) models. Methods: In this study, we developed Simplified molecular input line entry system Generative Pre-trained Transformer with Reinforcement Learning (SGPT-RL), which uses a transformer decoder as the policy network of the reinforcement learning agent to optimize the binding affinity to a target. SGPT-RL was evaluated on the Moses distribution learning benchmark and two goal-directed generation tasks, with Dopamine Receptor D2 (DRD2) and Angiotensin-Converting Enzyme 2 (ACE2) as the targets. Both QSAR model and molecular docking were implemented as the optimization goals in the tasks. The popular Reinvent method was used as the baseline for comparison. Results: The results on Moses benchmark showed that SGPT-RL learned good property distributions and generated molecules with high validity and novelty. On the two goal-directed generation tasks, both SGPT-RL and Reinvent were able to generate valid molecules with improved target scores. The SGPT-RL method achieved better results than Reinvent on the ACE2 task, where molecular docking was used as the optimization goal. Further analysis shows that SGPT-RL learned conserved scaffold patterns during exploration. Conclusions: The superior performance of SGPT-RL in the ACE2 task indicates that it can be applied to the virtual screening process where molecular docking is widely used as the criteria. Besides, the scaffold patterns learned by SGPT-RL during the exploration process can assist chemists to better design and discover novel lead candidates.
Published: 2023
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24. SD2: spatially resolved transcriptomics deconvolution through integration of dropout and spatial information.

Author: Haoyang Li, Hanmin Li, Juexiao Zhou, and Xin Gao 0001
Published: 2022
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25. An interpretable deep learning workflow for discovering subvisual abnormalities in CT scans of COVID-19 inpatients and survivors.

Author: Longxi Zhou, Xianglin Meng, Yuxin Huang, Kai Kang, Juexiao Zhou, Yuetan Chu, Haoyang Li, Dexuan Xie, Jiannan Zhang, Weizhen Yang, Na Bai, Yi Zhao, Mingyan Zhao, Guohua Wang, Lawrence Carin, Xigang Xiao, Kaijiang Yu, Zhaowen Qiu, and Xin Gao 0001
Published: 2022
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26. Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI.

Author: Juexiao Zhou, Longxi Zhou, Di Wang 0015, Xiaopeng Xu, Haoyang Li, Yuetan Chu, Wenkai Han, and Xin Gao 0001
Published: 2023
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27. Path to Medical AGI: Unify Domain-specific Medical LLMs with the Lowest Cost.

Author: Juexiao Zhou, Xiuying Chen, and Xin Gao 0001
Published: 2023
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28. Audit to Forget: A Unified Method to Revoke Patients' Private Data in Intelligent Healthcare.

Author: Juexiao Zhou, Haoyang Li, Xingyu Liao, Bin Zhang 0042, Wenjia He, Zhongxiao Li, Longxi Zhou, and Xin Gao 0001
Published: 2023
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29. Automated Bioinformatics Analysis via AutoBA.

Author: Juexiao Zhou, Bin Zhang 0042, Xiuying Chen, Haoyang Li, Xiaopeng Xu, Siyuan Chen, and Xin Gao 0001
Published: 2023
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30. SkinGPT: A Dermatology Diagnostic System with Vision Large Language Model.

Author: Juexiao Zhou and Xin Gao 0001
Published: 2023
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31. Automatic and Interpretable Model for Periodontitis Diagnosis in Panoramic Radiographs.

Author: Haoyang Li, Juexiao Zhou, Yi Zhou, Jie Chen 0003, Feng Gao 0001, Ying Xu, and Xin Gao 0001
Published: 2020
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32. Global fertility in 204 countries and territories, 1950-2021, with forecasts to 2100: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Author: V Bhattacharjee, N, E Schumacher, A, Aali, A, Habtegiorgis Abate, Y, Abbasgholizadeh, R, Abbasian, M, Abbasi-Kangevari, M, Abbastabar, H, Abd ElHafeez, S, Abd-Elsalam, S, Abdollahi, M, Abdollahifar, M, Abdoun, M, Abdullahi, A, Abebe, M, Shawel Abebe, S, Abiodun, O, Abolhassani, H, Abolmaali, M, Abouzid, M, Beressa Aboye, G, Guimarães Abreu, L, Aberhe Abrha, W, M Abrigo, M, Abtahi, D, Abualruz, H, Abubakar, B, Abu-Gharbieh, E, Me Abu-Rmeileh, N, Girum Girum Adal, T, Molla Adane, M, Atanda Adeagbo Adeagbo, O, Adesoji Adedoyin, R, Adekanmbi, V, Aden, B, Victor Adepoju, A, O Adetokunboh, O, Bunmi Adetunji, J, Adedayo Adeyinka, D, Israel Adeyomoye, O, Estiningtyas Sakilah Adnani, Q, Adra, S, Felix Afolabi, R, Afyouni, S, Sohail Afzal, M, Afzal, S, Aghamiri, S, Agodi, A, Agyemang-Duah, W, Opoku Ahinkorah, B, J Ahlstrom, A, Ahmad, A, Ahmad, D, Ahmad, F, M Ahmad, M, Ahmad, S, Ahmad, T, Ahmed, A, Ahmed, H, A Ahmed, L, Saleh Ahmed, M, Anees Ahmed, S, Ajami, M, Aji, B, Taddesse Akalu, G, Akbarialiabad, H, Olusola Akinyemi, R, Ahmed Akkaif, M, Akkala, S, Al Hamad, H, Mahfuz Al Hasan, S, Al Qadire, M, Mohammed Ali Al-Ahdal, T, O Alalalmeh, S, A Alalwan, T, Al-Aly, Z, Alam, K, Mustafa Al-Amer, R, Mashhour Alanezi, F, M Alanzi, T, Albakri, A, Albashtawy, M, T AlBataineh, M, Alemi, H, Alemi, S, Mulugeta Alemu, Y, Al-Eyadhy, A, Ali Saeed Al-Gheethi, A, F Alhabib, K, Alhajri, N, Alhalaiqa Naji Alhalaiqa, F, Kaba Alhassan, R, Ali, A, Abdulqadir Ali, B, Ali, L, Usman Ali, M, Ali, R, Shujait Shujait Ali, S, Mohammad Alif, S, Aligol, M, Alijanzadeh, M, M Aljasir, M, Mohamed Aljunid, S, Al-Marwani, S, Uy Almazan, J, M Al-Mekhlafi, H, Almidani, O, A Alomari, M, Al-Omari, B, S Alqahtani, J, Yaseen Alqutaibi, A, M Al-Raddadi, R, Khalifah Al-Sabah, S, Altaf, A, A Al-Tawfiq, J, A Altirkawi, K, Oyine Aluh, D, Jawad Alvi, F, Alvis-Guzman, N, Alwafi, H, Mohammed Al-Worafi, Y, Aly, H, Aly, S, H Alzoubi, K, Kwabena Ameyaw, E, Tawfik Amin, T, Amindarolzarbi, A, Amini-Rarani, M, Amiri, S, 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Bhageerathy Reshmi, Nazila Rezaei, Aida Rezaei Nejad, Mohsen Rezaeian, Abanoub Riad, Mavra A Riaz, Jennifer Rickard, Reza Rikhtegar, Hannah Elizabeth Robinson-Oden, Célia Fortuna Rodrigues, Jefferson Antonio Buendia Rodriguez, Ravi Rohilla, Debby Syahru Romadlon, Luca Ronfani, Himanshu Sekhar Rout, Bedanta Roy, Nitai Roy, Priyanka Roy, Enrico Rubagotti, Guilherme de Andrade Ruela, Susan Fred Rumisha, Tilleye Runghien, Manjula S, Chandan S N, Aly M A Saad, Zahra Saadatian, Maha Mohamed Saber-Ayad, Morteza SaberiKamarposhti, Siamak Sabour, Fatos Sada, Basema Saddik, Bashdar Abuzed Sadee, Ehsan Sadeghi, Erfan Sadeghi, Mohammad Reza Saeb, Umar Saeed, Sher Zaman Safi, Dominic Sagoe, Manika Saha, Amirhossein Sahebkar, Soumya Swaroop Sahoo, Monalisha Sahu, Zahra Saif, Joseph W Sakshaug, Payman Salamati, Afeez Abolarinwa Salami, Mohamed A Saleh, Marwa Rashad Salem, Mohammed Z Y Salem, Sohrab Salimi, Sara Samadzadeh, Yoseph Leonardo Samodra, Vijaya Paul Samuel, Abdallah M Samy, Juan Sanabria, Nima Sanadgol, Francesca Sanna, Milena M Santric-Milicevic, Haaris Saqib, Sivan Yegnanarayana Iyer Saraswathy, Aswini Saravanan, Babak Saravi, Yaser Sarikhani, Tanmay Sarkar, Rodrigo Sarmiento-Suárez, Gargi Sachin Sarode, Sachin C Sarode, Arash Sarveazad, Brijesh Sathian, Thirunavukkarasu Sathish, Anudeep Sathyanarayan, Abu Sayeed, Md Abu Sayeed, Nikolaos Scarmeas, Winfried Schlee, Art Schuermans, David C Schwebel, Falk Schwendicke, Siddharthan Selvaraj, Pallav Sengupta, Subramanian Senthilkumaran, Sadaf G Sepanlou, Dragos Serban, Edson Serván-Mori, Yashendra Sethi, SeyedAhmad SeyedAlinaghi, Seyed Arsalan Seyedi, Allen Seylani, Mahan Shafie, Jaffer Shah, Pritik A Shah, Ataollah Shahbandi, Samiah Shahid, Moyad Jamal Shahwan, Ahmed Shaikh, Masood Ali Shaikh, Muhammad Aaqib Shamim, Mehran Shams-Beyranvand, Mohammad Anas Shamsi, Mohd Shanawaz, Abhishek Shankar, Mohammed Shannawaz, Medha Sharath, Sadaf Sharfaei, Amin Sharifan, Javad Sharifi-Rad, Manoj Sharma, Rajesh Sharma, Ujjawal Sharma, Vishal Sharma, Rajesh P Shastry, Amin Shavandi, David H Shaw, Amir Mehdi Shayan, Maryam Shayan, Amr Mohamed Elsayed Shehabeldine, Aziz Sheikh, Rahim Ali Sheikhi, Manjunath Mala Shenoy, Pavanchand H Shetty, Peilin Shi, Desalegn Shiferaw, Mika Shigematsu, Rahman Shiri, Reza Shirkoohi, Aminu Shittu, Velizar Shivarov, Farhad Shokraneh, Sina Shool, Seyed Afshin Shorofi, Kanwar Hamza Shuja, Kerem Shuval, Emmanuel Edwar Siddig, João Pedro Silva, Luís Manuel Lopes Rodrigues Silva, Soraia Silva, Biagio Simonetti, Anjali Singal, Abhinav Singh, Balbir Bagicha Singh, Jasvinder A Singh, Md Shahjahan Siraj, Georgia Smith, Bogdan Socea, Anton Sokhan, Ranjan Solanki, Shipra Solanki, Hamidreza Soleimani, Sameh S M Soliman, Yonatan Solomon, Yimeng Song, Reed J D Sorensen, Michael Spartalis, Chandrashekhar T Sreeramareddy, Vijay Kumar Srivastava, Muhammad Haroon Stanikzai, Vladimir I Starodubov, Antonina V Starodubova, Simona Cătălina Stefan, Paschalis Steiropoulos, Mark A Stokes, Vetriselvan Subramaniyan, Muhammad Suleman, Rizwan Suliankatchi Abdulkader, Abida Sultana, Jing Sun, Chandan Kumar Swain, Bryan L Sykes, Lukasz Szarpak, Mindy D Szeto, Miklós Szócska, Payam Tabaee Damavandi, Rafael Tabarés-Seisdedos, Ozra Tabatabaei Malazy, Seyed-Amir Tabatabaeizadeh, Shima Tabatabai, Karen M Tabb, Mohammad Tabish, Moslem Taheri Soodejani, Jabeen Taiba, Ardeshir Tajbakhsh, Iman M Talaat, Ashis Talukder, Mircea Tampa, Jacques Lukenze Tamuzi, Ker-Kan Tan, Haosu Tang, Derbie Alemu DA Tareke, Mengistie Kassahun Tariku, Vivian Y Tat, Seyed Mohammad Tavangar, Mojtaba Teimoori, Mohamad-Hani Temsah, Reem Mohamad Hani Temsah, Masayuki Teramoto, Dufera Rikitu Terefa, Riki Tesler, Enoch Teye-Kwadjo, Ramna Thakur, Pugazhenthan Thangaraju, Kavumpurathu Raman Thankappan, Rekha Thapar, Samar Tharwat, Rasiah Thayakaran, Nihal Thomas, Ales Tichopad, Jansje Henny Vera Ticoalu, Tenaw Yimer Tiruye, Mariya Vladimirovna Titova, Marcello Tonelli, Marcos Roberto Tovani-Palone, Eugenio Traini, Jasmine T Tran, Nghia Minh Tran, Indang Trihandini, Samuel Joseph Tromans, Thien Tan Tri Tai Truyen, Aristidis Tsatsakis, Evangelia Eirini Tsermpini, Munkhtuya Tumurkhuu, Stefanos Tyrovolas, Sayed Mohammad Nazim Uddin, Aniefiok John Udoakang, Arit Udoh, Atta Ullah, Saeed Ullah, Sana Ullah, Srikanth Umakanthan, Chukwuma David Umeokonkwo, Brigid Unim, Bhaskaran Unnikrishnan, Era Upadhyay, Jibrin Sammani Usman, Marco Vacante, Seyed Mohammad Vahabi, Asokan Govindaraj Vaithinathan, Rohollah Valizadeh, Jef Van den Eynde, Elena Varavikova, Orsolya Varga, Priya Vart, Shoban Babu Varthya, Tommi Juhani Vasankari, Balachandar Vellingiri, Deneshkumar Venugopal, Nicholas Alexander Verghese, Madhur Verma, Massimiliano Veroux, Georgios-Ioannis Verras, Dominique Vervoort, Jorge Hugo Villafañe, Manish Vinayak, Francesco S Violante, Mukesh Vishwakarma, Sergey Konstantinovitch Vladimirov, Vasily Vlassov, Bay Vo, Simona Ruxandra Volovat, Theo Vos, Isidora S Vujcic, Hatem A Wafa, Yasir Waheed, Elias Bekele Wakwoya, Cong Wang, Denny Wang, Fang Wang, Shu Wang, Yanzhong Wang, Yuan-Pang Wang, Paul Ward, Emebet Gashaw Wassie, Stefanie Watson, Marcia R Weaver, Kosala Gayan Weerakoon, Daniel J Weiss, Katherine M Wells, Yi Feng Wen, Ronny Westerman, Taweewat Wiangkham, Dakshitha Praneeth Wickramasinghe, Nuwan Darshana Wickramasinghe, Peter Willeit, Yohannes Addisu Wondimagegene, Felicia Wu, Juan Xia, Hong Xiao, Gelin Xu, Suowen Xu, Xiaoyue Xu, Ali Yadollahpour, Shirin Yaghoobpoor, Tina Yaghoobpour, Sajad Yaghoubi, Zwanden Sule Yahaya, Danting Yang, Lin Yang, Yuichiro Yano, Habib Yaribeygi, Pengpeng Ye, Renjulal Yesodharan, Subah Abderehim Yesuf, Saber Yezli, Amanuel Yigezu, Paul Yip, Dong Keon Yon, Naohiro Yonemoto, Yuyi You, Mustafa Z Younis, Zabihollah Yousefi, Chuanhua Yu, Yong Yu, Chun-Wei Yuan, Nima Zafari, Fathiah Zakham, Nazar Zaki, Giulia Zamagni, Milad Zandi, Ghazal G Z Zandieh, Moein Zangiabadian, Mikhail Sergeevich Zastrozhin, Haijun Zhang, Meixin Zhang, Yunquan Zhang, Chenwen Zhong, Juexiao Zhou, Bin Zhu, Lei Zhu, Magdalena Zielińska, Zhiyong Zou, Samer H Zyoud, Christopher J L Murray, Amanda E Smith, and Stein Emil Vollset
Abstract: Background: Accurate assessments of current and future fertility-including overall trends and changing population age structures across countries and regions-are essential to help plan for the profound social, economic, environmental, and geopolitical challenges that these changes will bring. Estimates and projections of fertility are necessary to inform policies involving resource and health-care needs, labour supply, education, gender equality, and family planning and support. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 produced up-to-date and comprehensive demographic assessments of key fertility indicators at global, regional, and national levels from 1950 to 2021 and forecast fertility metrics to 2100 based on a reference scenario and key policy-dependent alternative scenarios. Methods: To estimate fertility indicators from 1950 to 2021, mixed-effects regression models and spatiotemporal Gaussian process regression were used to synthesise data from 8709 country-years of vital and sample registrations, 1455 surveys and censuses, and 150 other sources, and to generate age-specific fertility rates (ASFRs) for 5-year age groups from age 10 years to 54 years. ASFRs were summed across age groups to produce estimates of total fertility rate (TFR). Livebirths were calculated by multiplying ASFR and age-specific female population, then summing across ages 10-54 years. To forecast future fertility up to 2100, our Institute for Health Metrics and Evaluation (IHME) forecasting model was based on projections of completed cohort fertility at age 50 years (CCF50; the average number of children born over time to females from a specified birth cohort), which yields more stable and accurate measures of fertility than directly modelling TFR. CCF50 was modelled using an ensemble approach in which three sub-models (with two, three, and four covariates variously consisting of female educational attainment, contraceptive met need, population density in
Published: 2024

33. A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis.

Author: Longxi Zhou, Zhongxiao Li, Juexiao Zhou, Haoyang Li, Yupeng Chen, Yuxin Huang, Dexuan Xie, Lintao Zhao, Ming Fan 0003, Shahrukh Hashmi, Faisal Abdelkareem, Riham Eiada, Xigang Xiao, Lihua Li 0002, Zhaowen Qiu, and Xin Gao 0001
Published: 2020
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34. Accurate transcriptome-wide identification and quantification of alternative polyadenylation from RNA-seq data with APAIQ

Author: Yongkang Long, Bin Zhang, Shuye Tian, Jia Jia Chan, Juexiao Zhou, Zhongxiao Li, Yisheng Li, Zheng An, Xingyu Liao, Yu Wang, Shiwei Sun, Ying Xu, Yvonne Tay, Wei Chen, and Xin Gao
Subjects: Genetics, Genetics (clinical)
Abstract: Alternative polyadenylation (APA) enables a gene to generate multiple transcripts with different 3′ ends, which is dynamic across different cell types or conditions. Many computational methods have been developed to characterize sample-specific APA using the corresponding RNA-seq data, but suffered from high error rate on both polyadenylation site (PAS) identification and quantification of PAS usage (PAU), and bias toward 3′ untranslated regions. Here we developed a tool for APA identification and quantification (APAIQ) from RNA-seq data, which can accurately identify PAS and quantify PAU in a transcriptome-wide manner. Using 3′ end-seq data as the benchmark, we showed that APAIQ outperforms current methods on PAS identification and PAU quantification, including DaPars2, Aptardi, mountainClimber, SANPolyA, and QAPA. Finally, applying APAIQ on 421 RNA-seq samples from liver cancer patients, we identified >540 tumor-associated APA events and experimentally validated two intronic polyadenylation candidates, demonstrating its capacity to unveil cancer-related APA with a large-scale RNA-seq data set.
Published: 2023
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35. PPML-Omics: A privacy-preserving federated machine learning method protects patients' privacy in omic data.

Author: Juexiao Zhou, Siyuan Chen, Yulian Wu, Haoyang Li, Bin Zhang, Longxi Zhou, Yan Hu, Zihang Xiang, Zhongxiao Li, Ningning Chen, Wenkai Han, Chencheng Xu, Di Wang, and Xin Gao
Subjects: *DATA privacy, *DEEP learning, *MACHINE learning
Abstract: Modern machine learning models toward various tasks with omic data analysis give rise to threats of privacy leakage of patients involved in those datasets. Here, we proposed a secure and privacy-preserving machine learning method (PPML-Omics) by designing a decentralized differential private federated learning algorithm. We applied PPML-Omics to analyze data from three sequencing technologies and addressed the privacy concern in three major tasks of omic data under three representative deep learning models. We examined privacy breaches in depth through privacy attack experiments and demonstrated that PPML-Omics could protect patients' privacy. In each of these applications, PPML-Omics was able to outperform methods of comparison under the same level of privacy guarantee, demonstrating the versatility of the method in simultaneously balancing the privacy-preserving capability and utility in omic data analysis. Furthermore, we gave the theoretical proof of the privacy-preserving capability of PPML-Omics, suggesting the first mathematically guaranteed method with robust and generalizable empirical performance in protecting patients' privacy in omic data. [ABSTRACT FROM AUTHOR]
Published: 2024
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36. Optimization of binding affinities in chemical space with generative pretrained transformer and deep reinforcement learning

Author: Xiaopeng Xu, Juexiao Zhou, Chen Zhu, Qing Zhan, Zhongxiao Li, Ruochi Zhang, Yu Wang, Xingyu Liao, and Xin Gao
Abstract: Background: The key challenge in drug discovery is to discover novel compounds with desirable properties. Among the properties, binding affinity to a target is one of the prerequisites and usually evaluated by molecular docking or quantitative structure activity relationship (QSAR) models. Methods: In this study, we developed Simplified molecular input line entry system Generative Pretrained Transformer with Reinforcement Learning (SGPT-RL), which uses a transformer decoder as the policy network of the reinforcement learning agent to optimize the binding affinity to a target. SGPT-RL was evaluated on the Moses distribution learning benchmark and two goal-directed generation tasks, with Dopamine Receptor D2 (DRD2) and Angiotensin-Converting Enzyme 2 (ACE2) as the targets. Both QSAR model and molecular docking were implemented as the optimization goals in the tasks. The popular Reinvent method was used as the baseline for comparison. Results: The results on Moses benchmark showed that SGPT-RL learned good property distributions and generated molecules with high validity and novelty. On the two goal-directed generation tasks, both SGPT-RL and Reinvent were able to generate valid molecules with improved target scores. The SGPT-RL method achieved better results than Reinvent on the ACE2 task, where molecular docking was used as the optimization goal. Further analysis shows that SGPT-RL learned conserved scaffold patterns during exploration. Conclusions: The superior performance of SGPT-RL in the ACE2 task indicates that it can be applied to the virtual screening process where molecular docking is widely used as the criteria. Besides, the scaffold patterns learned by SGPT-RL during the exploration process can assist chemists to better design and discover novel lead candidates.
Published: 2023
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37. Predicting the antigenic evolution of SARS-COV-2 with deep learning

Author: Wenkai Han, Ningning Chen, Xinzhou Xu, Adil Sahil, Juexiao Zhou, Zhongxiao Li, Huawen Zhong, Ruochi Zhang, Yu Wang, Shiwei Sun, Peter Pak-Hang Cheung, and Xin Gao
Abstract: The severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) antigenic profile evolves in response to the vaccine and natural infection-derived immune pressure, resulting in immune escape and threatening public health. Exploring the possible antigenic evolutionary potentials improves public health preparedness, but it is limited by the lack of experimental assays as the sequence space is exponentially large. Here we introduce the Machine Learning-guided Antigenic Evolution Prediction (MLAEP), which combines structure modeling, multi-task learning, and genetic algorithm to model the viral fitness landscape and explore the antigenic evolution viain silicodirected evolution. As demonstrated by existing SARS-COV-2 variants, MLAEP can infer the order of variants along antigenic evolutionary trajectories, which is also strongly correlated with their sampling time. The novel mutations predicted by MLAEP are also found in immunocompromised covid patients and newly emerging variants, like XBB1.5. The predictions of MLAEP were validated by conducting in vitro neutralizing antibody binding assay, which demonstrated that the model-generated variants displayed significantly increased immune evasion ability compared with the controls. In sum, our approach enables profiling existing variants and forecasting prospective antigenic variants, thus may help guide the development of vaccines and increase preparedness against future variants. Our model is available athttps://mlaep.cbrc.kaust.edu.sa.
Published: 2022
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38. PPML-Omics: a Privacy-Preserving federated Machine Learning method protects patients’ privacy in omic data

Author: Juexiao Zhou, Siyuan Chen, Yulian Wu, Haoyang Li, Bin Zhang, Longxi Zhou, Yan Hu, Zihang Xiang, Zhongxiao Li, Ningning Chen, Wenkai Han, Di Wang, and Xin Gao
Abstract: Modern machine learning models towards various tasks with omic data analysis give rise to threats of privacy leakage of patients involved in those datasets. Despite the advances in different privacy technologies, existing methods tend to introduce too much computational cost (e.g. cryptographic methods) or noise (e.g. differential privacy), which hampers either model usefulness or accuracy in protecting privacy in biological data. Here, we proposed a secure and privacy-preserving machine learning method (PPML-Omics) by designing a decentralized version of the differential private federated learning algorithm. We applied PPML-Omics to analyze data from three sequencing technologies, and addressed the privacy concern in three major tasks of omic data, namely cancer classification with bulk RNA-seq, clustering with single-cell RNA-seq, and the integration of spatial gene expression and tumour morphology with spatial transcriptomics, under three representative deep learning models. We also examined privacy breaches in depth through privacy attack experiments and demonstrated that PPML-Omics could protect patients’ privacy. In each of these applications, PPML-Omics was able to outperform methods of comparison under the same level of privacy guarantee, demonstrating the versatility of the method in simultaneously balancing the privacy-preserving capability and utility in practical omic data analysis. Furthermore, we gave the theoretical proof of the privacy-preserving capability of PPML-Omics, suggesting the first mathematically guaranteed method with robust and generalizable empirical performance in protecting patients’ privacy in omic data.
Published: 2022
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39. Applications of deep learning in understanding gene regulation

Author: Zhongxiao Li, Elva Gao, Juexiao Zhou, Wenkai Han, Xiaopeng Xu, and Xin Gao
Subjects: Genetics, Radiology, Nuclear Medicine and imaging, Biochemistry, Genetics and Molecular Biology (miscellaneous), Biochemistry, Computer Science Applications, Biotechnology
Published: 2023
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40. DeeReCT-TSS: A novel meta-learning-based method annotates TSS in multiple cell types based on DNA sequences and RNA-seq data

Author: Juexiao Zhou, Bin Zhang, Haoyang Li, Longxi Zhou, Zhongxiao Li, Yongkang Long, Wenkai Han, Mengran Wang, Huanhuan Cui, Wei Chen, and Xin Gao
Abstract: The accurate annotation of transcription start sites (TSSs) and their usage is critical for the mechanistic understanding of gene regulation under different biological contexts. To fulfill this, on one hand, specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner. On the other hand, various computational tools have also been developed for in silico prediction of TSSs solely based on genomic sequences. Most of these computational tools cast the problem as a binary classification task on a balanced dataset and thus result in drastic false positive predictions when applied on the genome-scale. To address these issues, we present DeeReCT-TSS, a deep-learning-based method that is capable of TSSs identification across the whole genome based on both DNA sequences and conventional RNA-seq data. We show that by effectively incorporating these two sources of information, DeeReCT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell types. Furthermore, we develop a meta-learning-based extension for simultaneous transcription start site (TSS) annotation on 10 cell types, which enables the identification of cell-type-specific TSS. Finally, we demonstrate the high precision of DeeReCT-TSS on two independent datasets from the ENCODE project by correlating our predicted TSSs with experimentally defined TSS chromatin states. Our application, pre-trained models and data are available at https://github.com/JoshuaChou2018/DeeReCT-TSS_release.
Published: 2021
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41. DeeReCT-TSS: A novel meta-learning-based method annotates TSS in multiple cell types based on DNA sequences and RNA-seq data

Author: Wenkai Han, Longxi Zhou, Zhongxiao Li, Yongkang Long, Juexiao Zhou, Wei Chen, Haoyang Li, Mengran Wang, bin zhang, Huanhuan Cui, and Xin Gao
Subjects: Meta learning (computer science), Computer science, RNA-Seq, Computational biology, Constructive, DNA sequencing
Abstract: The accurate annotation of transcription start sites (TSSs) and their usage is critical for the mechanistic understanding of gene regulation under different biological contexts. To fulfil this, on one hand, specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner. On the other hand, various computational tools have also been developed for in silico prediction of TSSs solely based on genomic sequences. Most of these computational tools cast the problem as a binary classification task on a balanced dataset and thus result in drastic false positive predictions when applied on the genome-scale. To address these issues, we present DeeReCT-TSS, a deep-learning-based method that is capable of TSSs identification across the whole genome based on both DNA sequences and conventional RNA-seq data. We show that by effectively incorporating these two sources of information, DeeReCT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell types. Furthermore, we develop a meta-learning-based extension for simultaneous transcription start site (TSS) annotation on 10 cell types, which enables the identification of cell-type-specific TSS. Finally, we demonstrate the high precision of DeeReCT-TSS on two independent datasets from the ENCODE project by correlating our predicted TSSs with experimentally defined TSS chromatin states. Our application, pre-trained models and data are available at https://github.com/JoshuaChou2018/DeeReCT-TSS_release.
Published: 2021
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42. A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis

Author: Xigang Xiao, Ming Fan, Zhongxiao Li, Lihua Li, Faisal Abdelkareem, Yuxin Huang, Longxi Zhou, Xin Gao, Lintao Zhao, Yupeng Chen, Riham Eiada, Juexiao Zhou, Dexuan Xie, Shahrukh K. Hashmi, Haoyang Li, and Zhaowen Qiu
Subjects: Coronavirus disease 2019 (COVID-19), Computer science, Pneumonia, Viral, Computed tomography, Solid modeling, Machine learning, computer.software_genre, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Betacoronavirus, 0302 clinical medicine, Deep Learning, medicine, Humans, Segmentation, Electrical and Electronic Engineering, Lung, Pandemics, Modality (human–computer interaction), medicine.diagnostic_test, Radiological and Ultrasound Technology, business.industry, SARS-CoV-2, Deep learning, COVID-19, Gold standard (test), Image segmentation, Computer Science Applications, Artificial intelligence, Tomography, business, Coronavirus Infections, Tomography, X-Ray Computed, computer, Software, Algorithms
Abstract: COVID-19 has caused a global pandemic and become the most urgent threat to the entire world. Tremendous efforts and resources have been invested in developing diagnosis, prognosis and treatment strategies to combat the disease. Although nucleic acid detection has been mainly used as the gold standard to confirm this RNA virus-based disease, it has been shown that such a strategy has a high false negative rate, especially for patients in the early stage, and thus CT imaging has been applied as a major diagnostic modality in confirming positive COVID-19. Despite the various, urgent advances in developing artificial intelligence (AI)-based computer-aided systems for CT-based COVID-19 diagnosis, most of the existing methods can only perform classification, whereas the state-of-the-art segmentation method requires a high level of human intervention. In this paper, we propose a fully-automatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources. Our method is founded upon two innovations: 1) the first CT scan simulator for COVID-19, by fitting the dynamic change of real patients’ data measured at different time points, which greatly alleviates the data scarcity issue; and 2) a novel deep learning algorithm to solve the large-scene-small-object problem, which decomposes the 3D segmentation problem into three 2D ones, and thus reduces the model complexity by an order of magnitude and, at the same time, significantly improves the segmentation accuracy. Comprehensive experimental results over multi-country, multi-hospital, and multi-machine datasets demonstrate the superior performance of our method over the existing ones and suggest its important application value in combating the disease.
Published: 2020

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