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The Journal of Machine Learning Research 2022年第1期23卷 255-296页

作者： Justin D. Silverman Kimberly Roche Zachary C. Holmes Lawrence A. David Sayan Mukherjee College of Information Science and Technology Department of Statistics and Institute for Computational and Data Science Penn State University University Park PA Program in Computational Biology and Bioinformatics Duke University Durham NC Department of Molecular Genetics and Microbiology Duke University Durham NC Department of Molecular Genetics and Microbiology and Center for Genomic and Computational Biology Duke University Durham NC Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University Durham NC

Bayesian multinomial logistic-normal (MLN) models are popular for the analysis of sequence count data (e.g., microbiome or gene expression data) due to their ability to model multivariate count data with complex covariance structure. However, existing implementations of MLN models are limited to small datasets due to the non-conjugacy of the multinomial and logistic-normal distributions. Motivated by the need to develop efficient inference for Bayesian MLN models, we develop two key ideas. First, we develop the class of Marginally Latent Matrix-T Process (Marginally LTP) models. We demonstrate that many popular MLN models, including those with latent linear, non-linear, and dynamic linear structure are special cases of this class. Second, we develop an efficient inference scheme for Marginally LTP models with specific accelerations for the MLN subclass. Through application to MLN models, we demonstrate that our inference scheme are both highly accurate and often 4-5 orders of magnitude faster than MCMC.

关键词： Bayesian statistics multivariate analysis count data microbiome gene expression

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Comparative analysis of deep learning models for detecting face mask

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Procedia computer science 2023年 216卷 48-56页

作者： M Vickya Ramadhan Kahlil Muchtar Yudha Nurdin Maulisa Oktiana Maya Fitria Novi Maulina Gregorius Natanael Elwirehardja Bens Pardamean Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh 23111 Indonesia. Telematics Research Center (TRC) Universitas Syiah Kuala Banda Aceh 23111 Indonesia. Microbiology Department School of Medicine Universitas Syiah Kuala Banda Aceh 23111 Indonesia. Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia. Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia.

The spread of Corona Virus Disease 19 (COVID-19) in Indonesia is still relatively high and has not shown a significant decrease. One of the main reasons is due to the lack of supervision on the implementation of health protocols such as wearing masks in daily activities. Recently, state-of-the-art algorithms were introduced to automate face mask detection. To be more specific, the researchers developed various kinds of architectures for the detection of masks based on computer vision methods. This paper aims to evaluate well-known architectures, namely the ResNet50, VGG11, InceptionV3, EfficientNetB4, and YOLO (You Only Look Once) to recommend the best approach in this specific field. By using the MaskedFace-Net dataset, the experimental results showed that the EfficientNetB4 architecture has better accuracy at 95.77% compared to the YOLOv4 architecture of 93.40%, InceptionV3 of 87.30%, YOLOv3 of 86.35%, ResNet50 of 84.41%, VGG11 of 84.38%, and YOLOv2 of 78.75%, respectively. It should be noted that particularly for YOLO, the model was trained using a collection of MaskedFace-Net images that had been pre-processed and labelled for the task. The model was initially able to train faster with pre-trained weights from the COCO dataset thanks to transfer learning, resulting in a robust set of features expected for face mask detection and classification.

关键词： Binary Classification Deep Learning Face mask detection

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Developmental regulation of neuronal gene expression by Elongator complex protein 1 dosage

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Journal of Genetics and Genomics 2022年第7期49卷 654-665页

作者： Elisabetta Morini Dadi Gao Emily M.Logan Monica Salani Aram J.Krauson Anil Chekuri Yei-Tsung Chen Ashok Ragavendran Probir Chakravarty Serkan Erdin Alexei Stortchevoi Jesper Q.Svejstrup Michael E.Talkowski Susan A.Slaugenhaupt Center for Genomic Medicine Massachusetts General Hospital Research InstituteBostonMAUSA Department of Neurology Massachusetts General Hospital Research Institute and Harvard Medical SchoolBostonMAUSA Program in Medical and Population Genetics and Stanley Center for Psychiatric Research Broad Institute of Harvard and MITCambridgeMAUSA Department of Life Sciences and Institute of Genome Sciences National Yang Ming Chiao Tung UniversityTaiwanChina Bioinformatics and Biostatistics The Francis Crick InstituteLondonUK Mechanisms of Transcription Laboratory The Francis Crick InstituteLondonUK Department of Cellular and Molecular Medicine Panum InstituteUniversity of CopenhagenCopenhagenDenmark

Familial dysautonomia(FD), a hereditary sensory and autonomic neuropathy, is caused by a mutation in the Elongator complex protein 1(ELP1) gene that leads to a tissue-specific reduction of ELP1 protein. Our work to generate a phenotypic mouse model for FD headed to the discovery that homozygous deletion of the mouse Elp1 gene leads to embryonic lethality prior to mid-gestation. Given that FD is caused by a reduction, not loss, of ELP1, we generated two new mouse models by introducing different copy numbers of the human FD ELP1 transgene into the Elp1 knockout mouse(Elp1) and observed that human ELP1 expression rescues embryonic development in a dose-dependent manner. We then conducted a comprehensive transcriptome analysis in mouse embryos to identify genes and pathways whose expression correlates with the amount of ELP1. We found that ELP1 is essential for the expression of genes responsible for nervous system development. Further, gene length analysis of the differentially expressed genes showed that the loss of Elp1 mainly impacts the expression of long genes and that by gradually restoring Elongator, their expression is progressively rescued. Finally, through evaluation of co-expression modules, we identified gene sets with unique expression patterns that depended on ELP1 expression.

关键词： ELP1 Elongator Transcriptional elongation Familial dysautonomia Neurodevelopmental disease

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Quantum variational autoencoder utilizing regularized mixed-state latent representations

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Physical Review A 2025年第4期111卷 042416-042416页

作者： Gaoyuan Wang Jonathan Warrell Prashant S. Emani Mark Gerstein Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA Department of Molecular Biophysics and Biochemistry Yale University New Haven Connecticut 06520 USA NEC Laboratories America Princeton New Jersey 08540 USA Department of Computer Science Yale University New Haven Connecticut 06520 USA Department of Statistics & Data Science Yale University New Haven Connecticut 06520 USA Department of Biomedical Informatics & Data Science Yale University New Haven Connecticut 06520 USA

A major challenge in near-term quantum computing is its application to large real-world datasets due to scarce quantum hardware resources. One approach to enabling tractable quantum models for such datasets involves finding low-dimensional representations that preserve essential information for downstream analysis. In classical machine learning, variational autoencoders (VAEs) facilitate efficient data compression, representation learning for subsequent tasks, and novel data generation. However, no quantum model has been proposed that exactly captures all of these features for direct application to quantum data on quantum computers. Some existing quantum models for data compression lack regularization of latent representations, thus preventing direct use for generation and control of generalization. Others are hybrid models with only some internal quantum components, impeding direct training on quantum data. To address this, we present a fully quantum framework, ζ-QVAE, which encompasses all the capabilities of classical VAEs and can be directly applied to map both classical and quantum data to a lower-dimensional space, while effectively reconstructing much of the original state from it. Our model utilizes regularized mixed states to attain optimal latent representations. It accommodates various divergences for reconstruction and regularization. Furthermore, by accommodating mixed states at every stage, it can utilize the full data density matrix and allow for a training objective defined on probabilistic mixtures of input data. Doing so, in turn, makes efficient optimization possible and has potential implications for private and federated learning. In addition to exploring the theoretical properties of ζ-QVAE, we demonstrate its performance on representative genomics and synthetic data. Our results indicate that ζ-QVAE consistently learns representations that better utilize the capacity of the latent space and exhibits similar or better performance compared with

关键词： Quantum computers

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Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space

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Procedia Computer Science 2023年 227卷 1046-1053页

作者： Fergianto E. Gunawan Arief S. Budiman Bens Pardamean Endang Juana Sugiarto Romeli Tjeng W. Cenggoro Kartika Purwandari Alam A. Hidayat Anak. A.N.P. Redi Muhammad Asrol Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta 11440 Indonesia PT Impack Pratama Industri Jakarta 14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99.

关键词： closed space deep learning humity prediction indoor temperature prediction

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Single-cell multi-ome regression models identify functional and disease-associated enhancers and enable chromatin potential analysis (vol 56, pg 627, 2024)

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NATURE GENETICS 2024年第6期56卷 1319-1319页

作者： Mitra, Sneha Malik, Rohan Wong, Wilfred Rahman, Afsana Hartemink, Alexander J. Pritykin, Yuri Dey, Kushal K. Leslie, Christina S. Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York City NY USA Rye Country Day School Rye NY USA Tri-Institutional Training Program in Computational Biology and Medicine New York City NY USA Hunter College City University of New York New York City NY USA Department of Computer Science Duke University Durham NC USA Program in Computational Biology and Bioinformatics Duke University Durham NC USA Center for Genomic and Computational Biology Duke University Durham NC USA Department of Computer Science Princeton University Princeton NJ USA Lewis-Sigler Institute for Integrative Genomics Princeton University Princeton NJ USA

We present a gene-level regulatory model, single-cell ATAC + RNA linking (SCARlink), which predicts single-cell gene expression and links enhancers to target genes using multi-ome (scRNA-seq and scATAC–seq co-assay) sequencing data. The approach uses regularized Poisson regression on tile-level accessibility data to jointly model all regulatory effects at a gene locus, avoiding the limitations of pairwise gene–peak correlations and dependence on peak calling. SCARlink outperformed existing gene scoring methods for imputing gene expression from chromatin accessibility across high-coverage multi-ome datasets while giving comparable to improved performance on low-coverage datasets. Shapley value analysis on trained models identified cell-type-specific gene enhancers that are validated by promoter capture Hi-C and are 11× to 15× and 5× to 12× enriched in fine-mapped eQTLs and fine-mapped genome-wide association study (GWAS) variants, respectively. We further show that SCARlink-predicted and observed gene expression vectors provide a robust way to compute a chromatin potential vector field to enable developmental trajectory analysis.

关键词： Epigenetics Population genetics Software

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Integrated analysis of circRNA regulation with ADARB2 enrichment in inhibitory neurons

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Computers in Biology and Medicine 2024年 182卷 109212-109212页

作者： Yu, Suwan Ha, Hongseok Kim, Kwangsoo Interdisciplinary Program in Bioinformatics College of Natural Sciences Seoul National University Seoul Korea Republic of Institute of Endemic Disease Seoul National University Medical Research Center Seoul National University Seoul Korea Republic of Department of Medicine College of Medicine Seoul National University Seoul Korea Republic of Department of Transdisciplinary Medicine Institute of Convergence Medicine with Innovative Technology Seoul National University Hospital Seoul Korea Republic of

This study investigates the regulation of circular RNAs (circRNAs) with Adenosine Deaminase RNA Specific B2 (ADARB2) enrichment specifically in inhibitory neurons. Using an integrated analysis combining high-throughput sequencing and bioinformatics approaches, we identified a group of circRNAs that are potentially enhanced by ADARB2. Our findings highlight the pivotal role of ADARB2 in circRNA synthesis within inhibitory neurons, likely through its specific binding to precursor RNAs, which facilitates circRNA biogenesis. © 2024

关键词： Neurons

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Metformin escape in prostate cancer by activating the PTGR1 transcriptional program through a novel super-enhancer

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Signal Transduction and Targeted Therapy 2023年第9期8卷 4365-4379页

作者： Jianheng Ye Shanghua Cai Yuanfa Feng Jinchuang Li Zhiduan Cai Yulin Deng Ren Liu Xuejin Zhu Jianming Lu Yangjia Zhuo Yingke Liang Jianjiang Xie Yanqiong Zhang Huichan He Zhaodong Han Zhenyu Jia Weide Zhong Department of Urology Guangzhou First People’s HospitalSouth China University of Technology510180 GuangzhouGuangdongChina Urology Key Laboratory of Guangdong Province The First Affiliated Hospital of Guangzhou Medical UniversityGuangzhou Medical University510230 GuangzhouGuangdongChina Guangzhou National Laboratory No.9 XingDaoHuanBei RoadGuangzhou International Bio Island510005 GuangzhouGuangdongChina Institute of Chinese Materia Medica China Academy of Chinese Medical Sciences100700 BeijingChina Department of Botany and Plant Sciences University of CaliforniaRiversideCA 92507USA Graduate Program in Genetics Genomics&BioinformaticsUniversity of CaliforniaRiversideCA 92507USA State Key Laboratory of Quality Research in Chinese Medicine Macao University of Science and TechnologyTaipa999078 MacaoChina

The therapeutic efficacy of metformin in prostate cancer(PCa)appears uncertain based on various clinical *** treatment failure may be attributed to the high frequency of transcriptional dysregulation,which leads to drug ***,the underlying mechanism is still *** this study,we found evidences that metformin resistance in PCa cells may be linked to cell cycle ***-enhancers(SEs),crucial regulatory elements,have been shown to be associated with drug resistance in various *** analysis of SEs in metformin-resistant(MetR)PCa cells revealed a correlation with Prostaglandin Reductase 1(PTGR1)expression,which was identified as significantly increased in a cluster of cells with metformin resistance through single-cell transcriptome *** functional experiments showed that PTGR1 overexpression accelerated cell cycle progression by promoting progression from the G0/G1 to the S and G2/M phases,resulting in reduced sensitivity to ***,we identified key transcription factors that significantly increase PTGR1 expression,such as SRF and RUNX3,providing potential new targets to address metformin resistance in *** conclusion,our study sheds new light on the cellular mechanism underlying metformin resistance and the regulation of the SE-TFs-PTGR1 axis,offering potential avenues to enhance metformin’s therapeutic efficacy in PCa.

关键词： RUNX3 cancer offering

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Decomposition of dynamic transcriptomic responses during effector-triggered immunity reveals conserved responses in two distinct plant cell populations

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Plant Communications 2024年第8期5卷 1-17页

作者： Xiaotong Liu Daisuke Igarashi Rachel A.Hillmer Thomas Stoddard You Lu Kenichi Tsuda Chad L.Myers Fumiaki Katagiri Department of Plant and Microbial Biology University of Minnesota-Twin CitiesSt PaulMN 55108USA Department of Computer Science and Engineering University of Minnesota-Twin CitiesMinneapolisMN 55455USA Bioinformatics and Computational Biology Graduate Program University of Minnesota-Twin CitiesMinneapolisMN 55455USA Institute for Innovation Ajinomoto Co.Inc.KawasakiJapan State Key Laboratory of Agricultural Microbiology Hubei Hongshan LaboratoryHubei Key Lab of Plant PathologyCollege of Plant Science and TechnologyHuazhong Agricultural UniversityWuhan 430070China Present address:225 S 6th St MinneapolisMN 55402USA Present address:Biotalys Inc.2520 Meridian Parkway Suite 480DurhamNC 27713USA Present address:Life Sciences Institute and Department of Cell and Developmental Biology University of Michigan Medical SchoolAnn ArborMI 48109USA

Rapid plant immune responses in the appropriate cells are needed for effective defense against *** transcriptome analysis is often used to describe overall immune responses,collection of transcriptome data with sufficient resolution in both space and time is *** reanalyzed public Arabidopsis time-course transcriptome data obtained after low-dose inoculation with a Pseudomonas syringae strain expressing the effector AvrRpt2,which induces effector-triggered immunity in ***-peak time-course patterns are prevalent among thousands of upregulated *** implemented a multicompartment modeling approach to decompose the double-peak pattern into two single-peak patterns for each *** decomposed peaks reveal an“echoing”pattern:the peak times of the first and second peaks correlate well across most upregulated *** demonstrated that the two peaks likely represent responses of two distinct cell populations that respond either cell autonomously or indirectly to ***,the peak decomposition has extracted spatial information from the time-course *** echoing pattern also indicates a conserved transcriptome response with different initiation times between the two cell populations despite different elicitor types.A gene set highly overlapping with the conserved gene set is also upregulated with similar kinetics during pattern-triggered *** of a WRKY network via different entry-point WRKYs can explain the similar but not identical transcriptome responses elicited by different elicitor *** discuss potential benefits of the properties of the WRKY activation network as an immune signaling network in light of pressure from rapidly evolving pathogens.

关键词： Arabidopsis Pseudomonas syringae plant immune response multi-compartment model WRKY transcription factors resilient immune signaling network

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High-Resolution Imaging of Human Cancer Proteins Using Microprocessor Materials

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ChemBioChem 2022年第17期23卷 e202200310-e202200310页

作者： Solares, Maria J. Jonaid, G.M. Luqiu, William Y. Berry, Samantha Khadela, Janki Liang, Yanping Evans, Madison C. Pridham, Kevin J. Dearnaley, William J. Sheng, Zhi Kelly, Deborah F. Molecular Cellular and Integrative Biosciences Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University Park 16802 PA United States Bioinformatics and Genomics Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University Park 16802 PA United States Department of Biomedical Engineering Pennsylvania State University University Park 16802 PA United States Center for Structural Oncology Pennsylvania State University University Park 16802 PA United States Department of Electrical and Computer Engineering Duke University Durham 27708 NC United States Fralin Biomedical Research Institute at VTC Virginia Tech Roanoke 24016 VA United States

Mutations in tumor suppressor genes, such as Tumor Protein 53 (TP53), are heavily implicated in aggressive cancers giving rise to gain- and loss-of-function phenotypes. While individual domains of the p53 protein have been studied extensively, structural information for full-length p53 remains incomplete. Functionalized microprocessor chips (microchips) with properties amenable to electron microscopy permitted us to visualize complete p53 assemblies for the first time. The new structures revealed p53 in an inactive dimeric state independent of DNA binding. Residues located at the protein-protein interface corresponded with modification sites in cancer-related hot spots. Changes in these regions may amplify the toxic effects of clinical mutations. Taken together, these results contribute advances in technology and imaging approaches to decode native protein models in different states of activation. © 2022 Wiley-VCH GmbH.

关键词： cancer electron microscopy microchips p53, molecular modeling

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