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Cistrome data Browser and Toolkit:analyzing human and mouse genomic data using compendia of ChlP-seq and chromatin accessibility data

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Quantitative Biology 2020年第3期8卷 267-276页

作者： Rongbin Zheng Xin Dong Changxin Wan Xiaoying Shi Xiaoyan Zhang Clifford A.Meyer Clin ical Tran slati onal Research Center Shan ghai Pulmonary HospitalSchool of Life Science and Tech no logyTbngji UniversityShanghai 200433China Department of Bioinformatics School of Life Science and TechnologyTongji UniversityShanghai 200092China Department of Data Science Dana-Farber Cancer Institute and Harvard T.H.Chan School of Public HealthBostonMA 02215USA Center for Functional Cancer Epigenetics Dana-Farber Cancer InstituteBostonMA 02215USA

The Cistrome data Browser(DB)at the website(***/db)provides about 56,000 published human and mouse ChlP-seq,DNase-seq,and ATAC-seq chromatin profiles,which we have processed using uniform analysis and quality control *** Cistrome DB Toolkit at the website(***)was developed to allow users to investigate fundamental questions using this data *** this tutorial,we describe how to use the Cistrome DB to search for publicly available chromatin profiles,to assess sample quality,to access peak results,to visualize signal intensities,to explore DNA sequence motifs,and to identify putative target genes・We also describe the use of the Toolkit module to seek the factors most likely to regulate a gene of interest,the factors that bind to a given genomic interval(enhancer,SNP,etc.),and samples that have significant peak overlaps with user-defined peak *** tutorial guides biomedical researchers in the use of Cistrome DB resources to rapidly obtain valuable insights into gene regulatory questions.

关键词： ChlP-seq chromatin accessibility gene regulatory analysis transcription factor

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Federated Learning for Clinical Structured data: A Benchmark Comparison of Engineering and Statistical Approaches

arXiv

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arXiv 2023年

作者： Li, Siqi Miao, Di Wu, Qiming Hong, Chuan D’Agostino, Danny Li, Xin Ning, Yilin Shang, Yuqing Fu, Huazhu Ong, Marcus Eng Hock Haddadi, Hamed Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Institute of High Performance Computing Agency for Science Technology and Research Singapore Singapore Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Health Services Research Centre Singapore Health Services Singapore Singapore Department of Emergency Medicine Singapore General Hospital Singapore Singapore Department of Computing Imperial College London London United Kingdom Institute of Data Science National University of Singapore Singapore Singapore

Federated learning (FL) has shown promising potential in safeguarding data privacy in healthcare collaborations. While the term "FL" was originally coined by the engineering community, the statistical field has also explored similar privacy-preserving algorithms. Statistical FL algorithms, however, remain considerably less recognized than their engineering counterparts. Our goal was to bridge the gap by presenting the first comprehensive comparison of FL frameworks from both engineering and statistical domains. We evaluated five FL frameworks using both simulated and real-world data. The results indicate that statistical FL algorithms yield less biased point estimates for model coefficients and offer convenient confidence interval estimations. In contrast, engineering-based methods tend to generate more accurate predictions, sometimes surpassing central pooled and statistical FL models. This study underscores the relative strengths and weaknesses of both types of methods, emphasizing the need for increased awareness and their integration in future FL applications. © 2023, CC BY.

关键词： Decision making

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Every atom-atom map can be explained by electron pushing diagrams

arXiv

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arXiv 2023年

作者： Flamm, Christoph Müller, Stefan Stadler, Peter F. Department of Theoretical Chemistry University of Vienna Währingerstraße 17 WienA-1090 Austria Faculty of Mathematics University of Vienna Oskar-Morgenstern-Platz 1 WienA-1090 Austria Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig School of Embedded Composite Artificial Intelligence Leipzig University Härtelstraße 16-18 LeipzigD-04107 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Facultad de Ciencias Universidad National de Colombia Bogotá Colombia Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States

Chemical reactions can be understood as transformations of multigraphs (molecules) that preserve vertex labels (atoms) and degrees (sums of bonding and non-bonding electrons), thereby implying the atom-atom map of a reaction. The corresponding reaction mechanism is often described by an electron pushing diagram that explains the transformation by consecutive local relocations of invidudal edges (electron pairs). Here, we show that every degree-preserving map between multigraphs, and thus every atom-atom map, can be generated by cyclic electron pushing. Moreover, it is always possible to decompose such an explanation into electron pushing diagrams involving only four electron pairs. This in turn implies that every reaction can be decomposed into a sequence of elementary reactions that involve at most two educt molecules and two product molecules. Hence, the requirement of a mechanistic explantion in terms of electron pushing and small imaginary transition states does not impose a combinatorial constraint on the feasibility of hypothetical chemical *** Codes 05C92, 92E10 Copyright © 2023, The Authors. All rights reserved.

关键词： Atoms

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Unveiling roles of non-coding RNAs in cancer through advanced technologies

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Quantitative Biology 2025年第4期13卷 e70005-e70005页

作者： Wang, Runhao Han, Leng Brown Center for Immunotherapy Indiana University School of Medicine Indianapolis IN United States Department of Biostatistics and Health Data Science Indiana University School of Medicine Indianapolis IN United States Center for Computational Biology and Bioinformatics Indiana University School of Medicine Indianapolis IN United States

Non-coding RNAs (ncRNAs) have emerged as key regulators in tumorigenesis. In this perspective, we briefly review the significance of ncRNA in cancer biology and highlight recent technological advancements in characterization of ncRNA in cancer research. Specifically, we discuss how these advanced approaches, such as Patho-DBiT, CRISPR screens, and snoKARR-seq, hold the potential to revolutionize ncRNA research by offering comprehensive insights into their spatial expression patterns and functional roles. © 2025 The Author(s). Quantitative Biology published by John Wiley & Sons Australia, Ltd on behalf of Higher Education Press.

关键词： cancer CRISPR screens ncRNAs patho-DBiT snoKARR-seq spatial transcriptomics

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Optimization of a Solar PV Power Plant with Poultry Demand Side Management (PoDSM) for Poultry Farm

Optimization of a Solar PV Power Plant with Poultry Demand S...

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Computational Performance Evaluation (ComPE), International Conference on

作者： Mohammad Zeyad S.M. Masum Ahmed Eftakhar Hossain Md. Sadik Tasrif Anubhove Sayeed Hasan Dewan Mahnaaz Mahmud Sanjid Islam Erasmus+ Joint MSC in Smart Cities and Communities School of Engineering and Physical Sciences (EPS) Heriot-Watt University Edinburgh United Kingdom Energy and Technology Research Division Advanced Bioinformatics Computational Biology and Data Science Laboratory Bangladesh Chattogram Bangladesh

ISBN: (纸本)9781665436571

The fundamental aspiration of this work is to set up a poultry farm system structure that will meet all kinds of electricity needs of the poultry farm from the solar PV power plant. To accomplish the task, a new term called Poultry Demand Side Management (PoDSM) has been introduced which discusses Poultry Agriculture demand and Renewable Energy (Solar) demand in Bangladesh. Moreover, for a better understanding and outcomes, the research work has been done under a poultry farm in Charsita village, Lakshmipur, Bangladesh. In addition, energy planning and management systems, which satisfy electricity demand and the estimation of the demand for the entire poultry farm, were addressed in the letter part of this article. However, through the analysis of energy modeling, energy planning, and management systems which justify the term Poultry Demand Side Management (PoDSM). Moreover, an energy planning and management system has been discussed which meets the demand for electricity with required loads demand calculation for the entire poultry farm. Nevertheless, after finalizing the design of the Solar PV Power Plant, 2.8 kW of power will be generated to meet the electricity demand of poultry farm, which will reduce the energy demand of the main grid for poultry farms. However, this work aims to improve the generation of Renewable Energy using Solar PV.

关键词： Performance evaluation Renewable energy sources Analytical models Demand side management Process control Agriculture Planning

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MHCquant2 refines immunopeptidomics tumor antigen discovery

Research Square

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Research Square 2024年

作者： Scheid, Jonas Lemke, Steffen Hoenisch-Gravel, Naomi Dengler, Anna Sachsenberg, Timo Declerq, Arthur Gabriels, Ralf Bauer, Jens Wacker, Marcel Bichmann, Leon Martens, Lennart Dubbelaar, Marissa L. Nahnsen, Sven Walz, Juliane S. Department of Peptide-based Immunotherapy Institute of Immunology University and University Hospital Tübingen Tübingen Germany "Image-Guided and Functionally Instructed Tumor Therapies" University of Tübingen Tübingen Germany University of Tübingen Tübingen Germany Institute for Bioinformatics and Medical Informatics University of Tübingen Germany Department of Computer Science University of Tübingen Germany VIB-UGent Center for Medical Biotechnology VIB Belgium Department of Biomolecular Medicine Ghent University Belgium School of Medicine Yale University New HavenCT United States Department for Computer Science Biomedical Data Science University of Tübingen Tübingen Germany Clinical Collaboration Unit Department of Internal Medicine University Hospital Tübingen Tübingen Germany Partner Site Tübingen Germany

The identification of human leukocyte antigen (HLA)-presented peptides as targets of anti-cancer T cell response is pivotal for the development of novel immunotherapies. Mass spectrometry (MS)-based immunopeptidomics enables the detection of these peptides, yet confident identifications and thus implementation in immunotherapy design are hampered by the high diversity and low abundance of naturally presented HLA peptides. Here, we introduce MHCquant2, a Nextflow-based open-source pipeline that leverages OpenMS tools and peptide property predictors (DeepLC, MS2PIP) for highly sensitive and scalable HLA peptide identification and quantification across various MS platforms. MHCquant2 increased peptide identifications up to 27% with a significant expansion of low-abundant peptides, outperforming state-of-the-art pipelines. Using MHCquant2 we build a comprehensive benign tissue repository comprising re-analyzed data from available benign immunopeptidomes and a novel benignMHCquant2 dataset, adding more than 160,000 novel naturally presented HLA peptides. First applications of this benign repository and the MHCquant2 pipeline enabled (i) the refinement of tumor-associated antigens, (ii) the detection of novel, high-frequent tumor-exclusive peptide antigens for multiple tumor entities, and (iii) the identification and quantification of mutation-derived low-abundant neoepitopes. MHCquant2 refines tumor antigen discovery in immunopeptidomics, paving the way for the implementation of off-the-shelf and personalized immunotherapy design. © 2024, CC BY.

关键词： Tumors

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Position: Bayesian deep learning is needed in the age of large-scale AI 24

Position: Bayesian deep learning is needed in the age of lar...

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Proceedings of the 41st International Conference on Machine Learning

作者： Theodore Papamarkou Maria Skoularidou Konstantina Palla Laurence Aitchison Julyan Arbel David Dunson Maurizio Filippone Vincent Fortuin Philipp Hennig José Miguel Hernández-Lobato Aliaksandr Hubin Alexander Immer Theofanis Karaletsos Mohammad Emtiyaz Khan Agustinus Kristiadi Yingzhen Li Stephan Mandt Christopher Nemeth Michael A. Osborne Tim G. J. Rudner David Rügamer Yee Whye Teh Max Welling Andrew Gordon Wilson Ruqi Zhang Department of Mathematics The University of Manchester Manchester UK Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge Spotify London UK Computational Neuroscience Unit University of Bristol Bristol UK Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany and Department of Computer Science Technical University of Munich Munich Germany and Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge UK Department of Mathematics University of Oslo Oslo Norway and Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative California Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London UK Department of Computer Science UC Irvine Irvine Department of Mathematics and Statistics Lancaster University Lancaster UK Department of Engineering Science University of Oxford Oxford UK Center for Data Science New York University New York Munich Center for Machine Learning Munich Germany and Department of Statistics LMU Munich Munich Germany DeepMind London UK and Department of Statistics University of Oxford Oxford UK Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences and Center for Data Science Computer Science Department New York University New York Department of Computer Science Purdue University West Lafayette

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential.

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Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine

arXiv

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arXiv 2024年

作者： Yang, Rui Ning, Yilin Keppo, Emilia Liu, Mingxuan Hong, Chuan Bitterman, Danielle S. Ong, Jasmine Chiat Ling Ting, Daniel Shu Wei Liu, Nan Center for Quantitative Medicine Duke-NUS Medical School Singapore Singapore Faculty of Arts and Science University of Toronto TorontoON Canada Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Artificial Intelligence in Medicine Program Mass General Brigham Harvard Medical School BostonMA United States Division of Pharmacy Singapore General Hospital Singapore Singapore Singapore Eye Research Institute Singapore National Eye Center Singapore Singapore Byers Eye Institute Stanford University StanfordCA United States Program in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Institute of Data Science National University of Singapore Singapore Singapore

Generative artificial intelligence (AI) has brought revolutionary innovations in various fields, including medicine. However, it also exhibits limitations. In response, retrieval-augmented generation (RAG) provides a potential solution, enabling models to generate more accurate contents by leveraging the retrieval of external knowledge. With the rapid advancement of generative AI, RAG can pave the way for connecting this transformative technology with medical applications and is expected to bring innovations in equity, reliability, and personalization to health care. © 2024, CC BY.

关键词： Medical applications

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Application for sugarcane plantation: Manpower and transportation planning

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AIP Conference Proceedings 2023年第1期2594卷

作者： H. G. Mawandha D. Sudigyo T. Suparyanto B. Pardamean Department of Agrotechnology Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Sugarcane plantations in Indonesia are still managed in a labor-intensive manner, especially plantation-owned by farmers. Labor intensive means that every stage of work in sugarcane plantation management still uses manpower. One of the jobs in sugarcane plantations that requires a lot of labor is sugar cane harvesting. Trend changing in the workforce gender would affect the quantity and quality of jobs, which is determined by the workforce's capacity and the workload of the work performed. The result of this research was a sugarcane agricultural calculator that made it simple for farmers to measure manpower and transportation requirements in estimating harvest costs. Manpower and transportation of harvested sugarcane commodities were estimated through the workload approach, namely, crop production and manpower's ability to carry a load. These two components helped sugarcane farmers to determine manpower and transportation during the harvest. The final yield produced by the projected harvest labor cost would be known immediately.

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Predicting drug transcriptional response similarity using Signed Graph Convolutional Network

Predicting drug transcriptional response similarity using Si...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Ziyan Wang Chengzhi Hong Xuan Liu Zhankun Xiong Feng Liu Wen Zhang School of Computer Science Wuhan University Wuhan China College of Informatics Huazhong Agricultural University Wuhan China Hubei Engineering Technology Research Center of Agricultural Big Data Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Agricultural Bioinformatics Key Laboratory of Hubei Province Key Laboratory of Smart Farming for Agricultural Animals Ministry of Agriculture Huazhong Agricultural University Wuhan China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Exploring the transcriptional response after employing chemical compounds assists in treating gene-related diseases and understanding biological activity of compounds. Calculating the similarity of drug transcriptional response can help to discover novel compounds that have the similar biological activity to known drugs for treating the same disease. Considering the transcriptional profiles of compounds are limited and harder to get than the structure of compounds, it is worth modeling the structure-transcriptional response similarity relationship. In this paper, we propose a signed graph convolutional network (SGCN)-based method, namely SGCN-DTRS, to predict drug transcriptional response similarity, which is quantitatively measured by Connectivity Map (CMap) scores, from their structures. SGCNDTRS constructs a CMap signed network from compounds and their CMap scores in the training data, which takes compounds as nodes, molecular structural representations of compounds as the attributes of nodes, and similarity relations between compounds as edges. Then SGCN-DTRS learns the CMap compound embeddings to predict CMap scores of pairwise compounds. Extensive experiments verify the superiority of the proposed method against the compared state-of-the-art methods and reveal that the relational information of the CMap data, which is learned from CMap signed network, is important for the CMap score prediction. SGCN-DTRS can not only work for the compounds in the training set but also is applicable to unseen compounds.

关键词： Drugs Training databases Training data Predictive models Biology Graph neural networks

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