Article
Biochemical Research Methods
Victor Bernal, Rainer Bischoff, Peter Horvatovich, Victor Guryev, Marco Grzegorczyk
Summary: Gaussian graphical models (GGMs) are commonly used in systems biology to reconstruct regulatory networks by overcoming the 'high-dimensional problem' through shrinkage methods. However, the shrinkage introduces a non-linear bias in the partial correlations, impacting their interpretation and hindering network comparability. A proposed method, 'un-shrinking', aims to correct this bias and provide partial correlations closer to actual values for easier interpretation.
BMC BIOINFORMATICS
(2021)
Article
Automation & Control Systems
Gabor Lugosi, Jakub Truszkowski, Vasiliki Velona, Piotr Zwiernik
Summary: The study introduces a new input model for querying single entries of the covariance matrix to recover the support of the inverse covariance matrix with low query and computational complexities. The algorithms are suitable for recovering the structure of tree-like graphs and graphs of small treewidth more efficiently than computing the empirical covariance matrix for large classes of graphs.
JOURNAL OF MACHINE LEARNING RESEARCH
(2021)
Article
Statistics & Probability
Jack Storror Carter, David Rossell, Jim Q. Smith
Summary: Standard likelihood penalties for learning Gaussian graphical models do not have scale invariance unless the observed data is standardized to unit sample variances. We propose a new family of penalties based on partial correlations, which along with maximum likelihood, L0, and logarithmic penalties, exhibit scale invariance. We demonstrate the benefits of one such penalty, the partial correlation graphical LASSO, in terms of inference through simulated examples and real datasets.
SCANDINAVIAN JOURNAL OF STATISTICS
(2023)
Article
Statistics & Probability
Cong Ma, Junwei Lu, Han Liu
Summary: The goal of inter-subject analysis (ISA) is to explore dependency structure between different subjects with intra-subject dependency as nuisance. A modeling framework for ISA based on Gaussian graphical models is proposed, addressing the challenge of not imposing sparsity constraints on the whole precision matrix. The proposed method for estimation and inference is valid without the sparsity assumption, with applications beyond neuroscience.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2021)
Article
Biochemistry & Molecular Biology
Yuanxiao Chen, Xiao-Fei Zhang, Le Ou-Yang
Summary: Cancer is a complex disease primarily caused by genetic variants. Understanding the gene networks within tumors is crucial for comprehending the regulatory mechanisms of carcinogenesis. However, it is challenging to investigate the commonalities and specificities of gene networks in different cancer types due to the heterogeneity and similarities among them. This study proposes a new computational approach to jointly estimate the gene networks of multiple cancer types, which can identify both shared and unique regulatory components.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2023)
Article
Mathematical & Computational Biology
Katherine H. Shutta, Roberta De Vito, Denise M. Scholtens, Raji Balasubramanian
Summary: This tutorial provides an overview of Gaussian graphical models and demonstrates various tools for GGM analysis in R. It introduces the mathematical foundations of GGMs and emphasizes their applications in high-dimensional datasets. The methods are illustrated using a publicly available gene expression dataset from ovarian cancer patients.
STATISTICS IN MEDICINE
(2022)
Article
Statistics & Probability
Ting Ye, Luke Keele, Raiden Hasegawa, Dylan S. Small
Summary: The method of difference-in-differences (DID) is a widely used approach to study causal effects in observational studies. This study proposes a general identification strategy that leverages negative correlations between two groups of control units and the treated units to estimate the average treatment effect.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2023)
Article
Ergonomics
Andres Felipe Ramirez, Carlos Valencia
Summary: This study analyzed traffic accidents in Bogota between 2014 and 2016, identifying principal factors that increase accident risk, critical zones that require more attention, and developing a predictive tool for forecasting future accidents.
ACCIDENT ANALYSIS AND PREVENTION
(2021)
Article
Ergonomics
Andres Felipe Ramirez, Carlos Valencia
Summary: This study used data from the traffic police department in Bogota, Colombia to analyze traffic incidents with injuries or fatalities between 2014 and 2016. It identified the main factors that increase the risk of accidents and fatalities, critical zones in the city that require more attention, and developed a predictive tool to forecast future accidents.
ACCIDENT ANALYSIS AND PREVENTION
(2021)
Article
Ergonomics
Andres Felipe Ramirez, Carlos Valencia
Summary: The planning and location of resources for urban traffic management present complex decision problems due to uncertain variables, lack of data, and numerous factors to consider. This study uses data from the traffic police department in Bogota to identify key factors for traffic accidents, critical zones, and develop a predictive tool for future accidents.
ACCIDENT ANALYSIS AND PREVENTION
(2021)
Article
Multidisciplinary Sciences
Yu Chen, Jin Cheng, Arvind Gupta, Huaxiong Huang, Shixin Xu
Summary: This paper introduces a new method for parameter inference in systems of coupled ordinary differential equations with partial observations. The method combines fast Gaussian process-based gradient matching and deterministic optimization algorithms to achieve accurate, robust, and efficient inference, even with limited observable variables and large noise.
ROYAL SOCIETY OPEN SCIENCE
(2021)
Article
Biology
S. Klaassen, J. Kueck, M. Spindler, V Chernozhukov
Summary: This paper investigates the uniform inference on high-dimensional graphical models under approximate sparsity, and demonstrates how to estimate and recover the graphical models using modern machine learning methods. The paper establishes uniform estimation rates and sparsity guarantees for the square-root lasso estimator in a random design, and demonstrates its good performance through comprehensive simulations.
Review
Engineering, Biomedical
Jinyuan Zhang, Jian Xu, Jongcheon Lim, James K. Nolan, Hyowon Lee, Chi Hwan Lee
Summary: The emerging trends in wearable glucose monitoring and implantable insulin delivery technologies have opened new possibilities for diabetes management, enabling closed-loop care. These technologies, with advanced materials and construction, allow real-time monitoring of glucose excursions and self-regulating drug delivery.
ADVANCED HEALTHCARE MATERIALS
(2021)
Article
Endocrinology & Metabolism
Xintong Li, Dongmei Xu, Li Zhang, Ruimin Cao, Yide Hao, Lingling Cui, Tingting Chen, Yingying Guo, Jiaxin Li
Summary: The study found that body composition-related indicators were independently associated with the onset of GDM in pregnant women with both pre-pregnancy BMI < 24 kg/m² and BMI >= 24 kg/m² groups. Factors such as body fat mass, visceral fat level, and waist-hip ratio showed higher correlation with pre-pregnancy BMI. Total body water, protein levels, mineral levels, bone mineral content, soft lean mass, fat-free mass, skeletal muscle mass, and basal metabolic rate were found to be protective factors for GDM after adjusting for some confounders. The waist-hip ratio was found to be up to 4.562 times the risk of GDM development in all pregnant women, and gestational weight gain was the best predictor for GDM.
FRONTIERS IN ENDOCRINOLOGY
(2022)
Article
Mathematics, Interdisciplinary Applications
Donald R. Williams
Summary: Gaussian graphical models allow for estimating conditional dependence structures encoded by partial correlations, typically using lasso regularization in psychology. Bayesian methods are not commonly used in practice and have not been applied in psychological network literature. Posterior probabilities can be used to assess conditional dependent and independent relations.
MULTIVARIATE BEHAVIORAL RESEARCH
(2021)
Article
Endocrinology & Metabolism
Noha A. Yousri, Karsten Suhre, Esraa Yassin, Alya Al-Shakaki, Amal Robay, Maha Elshafei, Omar Chidiac, Steven C. Hunt, Ronald G. Crystal, Khalid A. Fakhro
Summary: Macro- and microvascular complications of type 2 diabetes, obesity, and dyslipidemia have shared metabolic pathways. This study identified 373 metabolites associated with these conditions, including novel ones such as phospholipids, sphingolipids, and dipeptides. Clustering analysis revealed metabo-clinical signatures that correlated with different clinical patterns, highlighting the importance of specific metabolic pathways. The study also discovered potential associations between certain metabolites and retinopathy, high cholesterol levels, and kidney function.
Article
Biochemistry & Molecular Biology
Panayiotis Louca, Ana Nogal, Aurelie Moskal, Neil J. Goulding, Martin J. Shipley, Taryn Alkis, Joni Lindbohm, Jie Hu, Domagoj Kifer, Ni Wang, Bo Chawes, Kathryn M. Rexrode, Yoav Ben-Shlomo, Mika Kivimaki, Rachel A. Murphy, Bing Yu, Marc J. Gunter, Karsten Suhre, Deborah A. Lawlor, Massimo Mangino, Cristina Menni
Summary: This study identified multiple metabolites associated with hypertension, with lipid and organic acids being predominant, and discovered 5 novel metabolites. Pathway analysis suggested important roles of amino acids, serine/glycine, and bile acids in hypertension regulation.
Article
Biotechnology & Applied Microbiology
David S. Fischer, Anna C. Schaar, Fabian J. Theis
Summary: A graph neural network is used to model how cells communicate in tissues. Existing models of intercellular communication only consider receptor-ligand signaling and ignore spatial proximity. This study presents a node-centric expression modeling method that estimates the impact of niche composition on gene expression from spatial molecular profiling data. The method successfully recovers signatures of molecular processes involved in cell communication.
NATURE BIOTECHNOLOGY
(2023)
Article
Biochemistry & Molecular Biology
Asma A. Elashi, Salman M. Toor, Ilhame Diboun, Yasser Al-Sarraj, Shahrad Taheri, Karsten Suhre, Abdul Badi Abou-Samra, Omar M. E. Albagha
Summary: Maturity-onset diabetes of the young (MODY) is a rare monogenic form of diabetes mellitus. This study estimated the prevalence and genetic spectrum of MODY in the Middle Eastern population of Qatar through whole-genome sequencing, identifying known and potentially novel disease-causing mutations. The study found that MODY accounts for approximately 2.2-3.4% of diabetes patients in Qatar and highlighted the need for further research on the newly identified mutations.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Cell Biology
Mohammad Lotfollahi, Sergei Rybakov, Karin Hrovatin, Soroor Hediyeh-zadeh, Carlos Talavera-Lopez, Alexander V. Misharin, Fabian J. Theis
Summary: Lotfollahi et al. propose ExpiMap, a biologically informed deep-learning model for interpretable reference mapping of RNA sequencing data. ExpiMap maps cells into biologically understandable components representing known 'gene programs', allowing for detailed analysis and interpretation of single-cell data.
NATURE CELL BIOLOGY
(2023)
Article
Biochemical Research Methods
Hannah Spitzer, Scott Berry, Mark Donoghoe, Lucas Pelkmans, Fabian J. Theis
Summary: CAMPA is a deep learning framework that learns representations of molecular pixel profiles from multiplexed images. It clusters these representations to quantify subcellular landmarks and captures interpretable cellular phenotypes. Using this framework, the study reveals the changes in subcellular organization upon perturbation of RNA synthesis, RNA processing, or cell size, and uncovers the links between the molecular composition of membraneless organelles and cell-to-cell variability in bulk RNA synthesis rates.
Review
Oncology
Stefanie Baerthel, Chiara Falcomata, Roland Rad, Fabian J. Theis, Dieter Saur
Summary: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer with a heterogeneous genetic landscape and an immunosuppressive tumor microenvironment. Recent advances in single-cell sequencing and spatial transcriptomics have provided insights into the diversity and plasticity of PDAC, both in its malignant cells and the surrounding tissue. This review highlights the importance of single-cell analysis in understanding PDAC and discusses the potential of multimodal approaches to study its biology and response to therapy.
Article
Medicine, General & Internal
Noha A. Yousri, Omar M. E. Albagha, Steven C. Hunt
Summary: This study investigates the biological mechanisms of T2D in a population with a high prevalence of T2D in the Middle East using epigenetics, whole genome sequencing, and metabolomics. The study identifies novel methylated genes in T2D and their associated metabolic pathways, providing insights for future clinical use and therapeutic targets.
Article
Biochemistry & Molecular Biology
Ashley van der Spek, Isobel D. Stewart, Brigitte Kuehnel, Maik Pietzner, Tahani Alshehri, Friederike Gauss, Pirro G. Hysi, Siamak MahmoudianDehkordi, Almut Heinken, Annemarie I. Luik, Karl-Heinz Ladwig, Gabi Kastenmueller, Cristina Menni, Johannes Hertel, M. Arfan Ikram, Renee de Mutsert, Karsten Suhre, Christian Gieger, Konstantin Strauch, Henry Voelzke, Thomas Meitinger, Massimo Mangino, Antonia Flaquer, Melanie Waldenberger, Annette Peters, Ines Thiele, Rima Kaddurah-Daouk, Boadie W. Dunlop, Frits R. Rosendaal, Nicholas J. Wareham, Tim D. Spector, Sonja Kunze, Hans Joergen Grabe, Dennis O. Mook-Kanamori, Claudia Langenberg, Cornelia M. van Duijn, Najaf Amin
Summary: The study identified 8 metabolites associated with depression through metabolome-wide association analysis. These metabolites are either directly derived from food or products of host and gut microbial metabolism of food-derived products. The findings highlight the potential of diet interventions in preventing depression.
MOLECULAR PSYCHIATRY
(2023)
Article
Nutrition & Dietetics
Nagham Nafiz Hendi, Yasser Al-Sarraj, Umm-Kulthum Ismail Umlai, Karsten Suhre, Georges Nemer, Omar Albagha
Summary: This study conducted the first genome-wide association study on Middle Easterners to identify the genetic determinants of Vitamin D levels. It identified an association between a known locus for the GC gene and Vitamin D levels, as well as two novel variants on chromosome 11. The study highlights the genetic heterogeneity across different populations and the significance of genetic factors in Vitamin D deficiency.
FRONTIERS IN NUTRITION
(2023)
Article
Genetics & Heredity
Lieke Michielsen, Mohammad Lotfollahi, Daniel Strobl, Lisa Sikkema, Marcel J. T. Reinders, Fabian J. Theis, Ahmed Mahfouz
Summary: Single-cell genomics is generating a large amount of data, which can be integrated to create comprehensive reference atlases of tissue. However, there is a lack of systematic approach to harmonize cell type annotation terminology and depth across different datasets.
NAR GENOMICS AND BIOINFORMATICS
(2023)
Article
Cell Biology
Aziz Belkadi, Gaurav Thareja, Fatemeh Abbaszadeh, Ramin Badii, Eric Fauman, Karsten Qatar Genome Program Res Consortium, Karsten Suhre
Summary: Natural human knockouts of genes associated with desirable outcomes can lead to the discovery of new drug targets and treatments. This study combined whole-genome sequencing with proteomics and metabolomics to evaluate the power of this approach for finding genes of clinical and pharmaceutical interest. A rare PCSK9 variant with low circulating levels was identified in a homozygous carrier from the Qatar Biobank, highlighting the potential of consanguineous populations for drug discovery.
Meeting Abstract
Biochemistry & Molecular Biology
Abril Izquierdo, Nick Shrine, Jing Chen, Anna Guyatt, Richard Packer, Chiara Batini, Karsten Suhre, Alfred Pozarickij, Robin G. Walters, Stephanie London, Andrew Morris, Louise Wain, Ian P. Hall, Martin D. Tobin
EUROPEAN JOURNAL OF HUMAN GENETICS
(2023)
Meeting Abstract
Biochemistry & Molecular Biology
Jing Chen, Nick Shrine, Abril Izquierdo, Anna Guyatt, Richard Packer, Chiara Batini, Xiaowei Hu, Ain W. Manichaikul, Brian Hobbs, Michael Cho, Tamar Sofer, Karsten Suhre, Alfred Pozarickij, Robin G. Walters, Stephanie London, Andrew Morris, Louise Wain, Ian P. Hall, Martin D. Tobin
EUROPEAN JOURNAL OF HUMAN GENETICS
(2023)
Meeting Abstract
Respiratory System
L. Yang, I. Angelidis, L. Heumos, M. Ansari, S. Zhou, C. Mayr, L. Simon, M. Strunz, F. Theis, H. Adler, H. Schiller
EUROPEAN RESPIRATORY JOURNAL
(2022)