Article
Automation & Control Systems
Junyao You, Chengpu Yu, Jian Sun, Jie Chen
Summary: This paper focuses on the joint estimation of parameters and topologies of multivariate graphical ARMA processes. A generalized maximum entropy optimization model is derived by imposing a sparse regularization on the inverse spectrum to estimate the AR part parameters and graph topology simultaneously. The whole graphical ARMA model is identified by alternatingly estimating the graphical AR part and the MA part.
Article
Mathematics
Lingju Chen, Shaoxin Hong, Bo Tang
Summary: This study focuses on the identification and estimation of graphical models with nonignorable nonresponse. The proposed method introduces a new observable variable to identify the mean of response for the unidentifiable model and suggests a simulation imputation approach to estimate the marginal mean of response. The root N-consistent mean estimators show effectiveness in finite sample simulations and a real data example is used to illustrate the methodology.
JOURNAL OF MATHEMATICS
(2021)
Article
Automation & Control Systems
Daniele Alpago, Mattia Zorzi, Augusto Ferrante
Summary: This article proposes an identification method for latent-variable graphical models associated with autoregressive Gaussian stationary processes, utilizing the approximation of AR processes through stationary reciprocal processes and the numerical advantages of dealing with block-circulant matrices. The identification can be cast in a regularized convex program, and numerical examples show that the proposed method outperforms existing ones.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Veterinary Sciences
Xiushen Li, Li Guo, Weiwen Zhang, Junli He, Lisha Ai, Chengwei Yu, Hao Wang, Weizheng Liang
Summary: Using a bioinformatics approach, we identified amino acid metabolism as a possible major cause of infertility in patients with endometriosis, and provided potential targets for diagnosis and treatment.
FRONTIERS IN VETERINARY SCIENCE
(2022)
Article
Agriculture, Dairy & Animal Science
Tamu Yokomori, Aoi Ohnuma, Teruaki Tozaki, Takao Segawa, Takuya Itou
Summary: By using the Thoroughbred genome variant database to identify orthologues to personality-related human genes, 18 potential personality-related genes were found in horses, with 55 variants that may impact protein function. This study highlights the potential for identification of candidate genes, although the approach is less useful in horses due to a lack of supporting personality research. If future studies validate these potential associations, this bioinformatics strategy may become important in equine genetics.
Article
Mathematics
Claudia Angelini, Daniela De Canditiis, Anna Plaksienko
Summary: This paper addresses the problem of estimating graphical models of conditional dependencies between variables from multiple datasets under Gaussian settings. The proposed jewel 2.0 method improves upon the previous version by modeling commonality and class-specific differences in the graph structures and incorporating a stability selection procedure to reduce false positives. The performance of jewel 2.0 is demonstrated through simulated and real data examples, and the method is implemented in the R package jewel.
Article
Biochemical Research Methods
Muhammad Muneeb, Samuel Feng, Andreas Henschel
Summary: This paper investigates the feasibility of using transfer learning for genotype-phenotype prediction. By transferring knowledge from large populations to small populations, the accuracy of prediction can be significantly improved. The results show that transfer learning can create powerful models for genotype-phenotype predictions and apply them to populations with sparse data.
BMC BIOINFORMATICS
(2022)
Article
Neurosciences
Luis Carrillo-Reid, Shuting Han, Darik O'Neil, Ekaterina Taralova, Tony Jebara, Rafael Yuste
Summary: Neuronal ensembles play a crucial role in representing different states and guiding complex behaviors. The use of conditional random fields (CRFs) in identifying and manipulating pattern completion neurons that can activate entire ensembles shows potential in characterizing and selectively manipulating neural circuits.
JOURNAL OF NEUROSCIENCE
(2021)
Article
Automation & Control Systems
Junyao You, Chengpu Yu
Summary: This paper focuses on identifying graphical autoregressive models with dynamical latent variables. The dynamic structure of latent variables is described by a matrix polynomial transfer function. By considering the sparse interactions between observed variables and the low-rank property of the latent-variable model, a new optimization problem combining sparsity and low-rank is formulated to identify the graphical autoregressive part. The trace approximation and reweighted nuclear norm minimization are used for solving the problem. The dynamics of latent variables are recovered using trace norm convex programming and low-rank spectral decomposition.
Article
Biochemistry & Molecular Biology
Tamara C. Bidone, David J. Odde
Summary: Computational models have provided insights into how cells establish connections with the external environment, but how individual adhesion proteins regulate the dynamics of whole adhesion complexes remains unclear. This is due to the differences in time and length scales and limitations in accurately extracting key information from molecular simulation approaches. This review discusses the models of integrin-based adhesion complexes and highlights important findings related to conformational transitions and the molecular clutch mechanism.
CURRENT OPINION IN STRUCTURAL BIOLOGY
(2023)
Review
Ophthalmology
Nicole C. L. Noel, W. Ted Allison, Ian M. MacDonald, Jennifer C. Hocking
Summary: Photoreceptor dysfunctions and degenerative diseases are significant causes of vision loss in patients. Zebrafish provide a unique model for studying cone diseases and have contributed to our understanding of photoreceptor biology and disease.
PROGRESS IN RETINAL AND EYE RESEARCH
(2022)
Article
Cell Biology
Duncan Wei, Xiaopu Chen, Jing Xu, Wenzhen He
Summary: The immune system is known to play a crucial role in the development of ischaemic stroke, but the exact immune-related mechanism remains unclear. Gene expression data of ischaemic stroke patients and healthy controls were analyzed to identify differentially expressed genes (DEGs). Immune-related genes (IRGs) data was also utilized for further analysis. Based on IRGs and weighted co-expression network analysis (WGCNA), two molecular subtypes of ischaemic stroke were identified. Nine hub genes, including IL7R, ITK, SOD1, CD3D, LEF1, FBL, MAF, DNMT1, and SLAMF1, were found to potentially distinguish between the two subtypes and be associated with immune regulation.
IET SYSTEMS BIOLOGY
(2023)
Article
Genetics & Heredity
Musu Yuan, Liang Chen, Minghua Deng
Summary: Single-cell multiomics sequencing techniques, such as CITE-seq, provide a powerful tool for simultaneous quantification of gene expression and surface proteins. However, clustering analysis of CITE-seq data is challenging due to noise, high dimensionality, and sparsity. To address these challenges, researchers proposed scCTClust, a method that utilizes omics-specific neural networks to extract cluster information, finds maximally correlated representations of two omics, and applies decentralized clustering to the combined representations. Extensive experiments demonstrated the effectiveness of scCTClust and its potential for generalization to different data sets.
FRONTIERS IN GENETICS
(2022)
Article
Ophthalmology
Yu Su, Yuexiong Yi, Lu Li, Changzheng Chen
Summary: This study aimed to investigate the pathogenesis of age-related macular degeneration (AMD) by constructing a regulatory circRNA-miRNA-mRNA network. Through network analysis, a potential regulation axis of hsa_circRNA7329/hsa-miR-9/SCD was identified, suggesting a role in promoting inflammation and pathologic angiogenesis in AMD development. Further investigation is required to elucidate the underlying mechanisms.
EXPERIMENTAL EYE RESEARCH
(2021)
Article
Biochemistry & Molecular Biology
Xi'nan Zhou, Yangyang Zheng, Zhibo Cai, Xingyuan Wang, Yang Liu, Anzhou Yu, Xiuling Chen, Jiayin Liu, Yao Zhang, Aoxue Wang
Summary: This study identified 26 TPR gene families in tomatoes and found that their expression was affected by various stressors, with some genes being related to disease resistance. These results provide a theoretical basis for further research into TPR-mediated disease defense mechanisms and breeding for disease resistance in tomatoes.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Article
Mathematical & Computational Biology
Rasmus Magnusson, Jesper N. Tegner, Mika Gustafsson
Summary: Prediction algorithms for protein or gene structures, including transcription factor binding, have been informative in understanding gene regulation. The study found that the expression of 1600 TFs could explain over 95% of the variation in 25,000 genes, with a smaller core set of around 125 TFs explaining close to 80% of the variance. Reducing the number of TFs below 500 led to a rapid decline in prediction performance. By evaluating the prediction model using transcriptional data from 22 human diseases, the study demonstrated the ability of TFs to predict dysregulation of target genes, with key causative TFs identified for subsequent validation using disease-associated genetic variants.
NPJ SYSTEMS BIOLOGY AND APPLICATIONS
(2022)
Review
Biochemical Research Methods
Sarah Mubeen, Alpha Tom Kodamullil, Martin Hofmann-Apitius, Daniel Domingo-Fernandez
Summary: Pathway enrichment analysis is widely used for interpreting biomedical data, but its results are influenced by various factors. Benchmark studies have evaluated these factors and identified key influences on the analysis results.
BRIEFINGS IN BIOINFORMATICS
(2022)
Editorial Material
Cell Biology
Jesper N. Tegner, David Gomez-Cabrero
Summary: Molecular profiling of clinical tissue samples is vital in precision medicine, but understanding the contribution of mixed cell types and detecting changes in cell populations due to infections or drugs is difficult. Recent advances in machine learning offer the promise of learning explanatory models directly from data.
TRENDS IN CELL BIOLOGY
(2022)
Article
Multidisciplinary Sciences
Jin Ye, Isabel A. Calvo, Itziar Cenzano, Amaia Vilas, Xabier Martinez-de-Morentin, Miren Lasaga, Diego Alignani, Bruno Paiva, Ana C. Vinado, Patxi San Martin-Uriz, Juan P. Romero, Delia Quilez Agreda, Marta Minana Barrios, Ignacio Sancho-Gonzalez, Gabriele Todisco, Luca Malcovati, Nuria Planell, Borja Saez, Jesper N. Tegner, Felipe Prosper, David Gomez-Cabrero
Summary: This study developed a tailored bioinformatic pipeline to integrate public and proprietary single-cell RNA sequencing datasets, providing a comprehensive description of the regulatory microenvironment of HSCs in mice and suggesting conserved features in humans.
Article
Biochemical Research Methods
Rebeca Queiroz Figueiredo, Sara Diaz del Ser, Tamara Raschka, Martin Hofmann-Apitius, Alpha Tom Kodamullil, Sarah Mubeen, Daniel Domingo-Fernandez
Summary: Distinct gene expression patterns within various contexts were explored in this study to reveal specific functional roles and biological processes. By utilizing co-expression networks, unique and common patterns were identified, and pathway-level analysis further investigated the relationship between gene expression and specific contexts. The data and tools from this study are important for a deeper understanding of the relationship between gene expression and biological processes.
BMC BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Yojana Gadiya, Andrea Zaliani, Philip Gribbon, Martin Hofmann-Apitius
Summary: The study develops a patent enrichment tool called PEMT, which extracts relevant patent information linked to chemical structures and gene names. It supports drug discovery and research by establishing a patent landscape around genes of interest. The tool is open-source and its source code and software package are available through the provided link.
Article
Multidisciplinary Sciences
Sumeer Ahmad Khan, Robert Lehmann, Xabier Martinez-de-Morentin, Alberto Maillo, Vincenzo Lagani, Narsis A. Kiani, David Gomez-Cabrero, Jesper Tegner
Summary: Recent progress in Single-Cell Genomics has led to the development of different library protocols and techniques for molecular profiling. We have formulated a novel methodology called scAEGAN that integrates and predicts data from various libraries, samples, and data modalities. Through evaluations on simulated and real datasets, scAEGAN has demonstrated superior performance in library integration, data sparsity robustness, and paired data integration compared to Seurat3 and Seurat4. Furthermore, scAEGAN outperforms Babel in predictive analysis between different data modalities. Overall, scAEGAN surpasses current state-of-the-art methods and addresses the challenges of integration and prediction in single-cell genomics.
Article
Biochemistry & Molecular Biology
Piotr Gawron, Ewa Smula, Reinhard Schneider, Marek Ostaszewski
Summary: MINERVA Net is an open-access repository of disease maps that allows users to publicly share minimal information about their maps. This article introduces the concept of MINERVA Net and demonstrates its use by comparing proteins and their interactions in three different disease maps.
Article
Mathematical & Computational Biology
Hector Zenil, James A. R. Marshall, Jesper Tegner
Summary: This study uses numerical approximation methods to objectively classify and evaluate animal behavior based on algorithmic and structural complexity. The research finds that animal behavior is influenced by environmental conditions and exhibits algorithmic biases in decision-making and reasoning processes. Additionally, experiments on human perception of randomness suggest the existence of algorithmic biases in human cognition.
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
(2023)
Article
Multidisciplinary Sciences
Etienne Maheux, Igor Koval, Juliette Ortholand, Colin Birkenbihl, Damiano Archetti, Vincent Bouteloup, Stephane Epelbaum, Carole Dufouil, Martin Hofmann-Apitius, Stanley Durrleman
Summary: This study developed a statistical model, AD Course Map, for predicting the progression of Alzheimer's disease (AD) based on current medical and radiological data. The model was tested on a large dataset of over 96,000 cases and showed high accuracy in predicting clinical endpoints. By enriching the population with predicted progressors, the required sample size for trials could be reduced by 38% to 50%.
NATURE COMMUNICATIONS
(2023)
Article
Biochemistry & Molecular Biology
Alexandre Xavier, Vicki E. Maltby, Ewoud Ewing, Maria Pia Campagna, Sean M. Burnard, Jesper N. Tegner, Mark Slee, Helmut Butzkueven, Ingrid Kockum, Lara Kular, Vilija G. Jokubaitis, Trevor Kilpatrick, Lars Alfredsson, Maja Jagodic, Anne-Louise Ponsonby, Bruce V. Taylor, Rodney J. Scott, Rodney A. Lea, Jeannette Lechner-Scott
Summary: This study reveals that DNA methylation differences in multiple sclerosis (MS) occur independently of known genetic risk loci. It shows that these differences more effectively differentiate the disease compared to known genetic risk loci. The study also indicates that the methylation differences in MS predominantly occur in B cells and monocytes, involving cell-specific biological pathways.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Cell & Tissue Engineering
Maryam Alowaysi, Mohammad Al-Shehri, Moayad Baadhaim, Hajar AlZahrani, Doaa Aboalola, Mustafa Daghestani, Heba Hashem, Rawan Aljahdali, Rayan Salem, Adel Alharbi, Mohammed Muharraq, Khaled Alghamdi, Fawaz Alsobiy, Asima Zia, Robert Lehmann, Jesper Tegner, Khaled Alsayegh
Summary: Myoglobin (MB) is expressed in the heart and skeletal muscle, and it promotes oxygen diffusion for energy production. Recent research has shown that MB is also expressed in various malignant tumors and cancer cells. Further studies using gene disruption technology will enhance our understanding of MB's role in human biology and diseases.
STEM CELL RESEARCH
(2023)
Article
Multidisciplinary Sciences
Yihao Wang, Philipp Wegner, Daniel Domingo-Fernandez, Alpha Tom Kodamullil
Summary: This study investigates the effectiveness of infusing the embedding of two aligned ontologies as prior knowledge into NLP models, and demonstrates through experiments that the knowledge-infused model slightly outperforms the model without incorporating contextualized knowledge in gene-disease association prediction tasks. However, further research is needed to explore the generalizability of the model, and adding more bridges is expected to bring further improvement based on the observed trend.
Article
Computer Science, Artificial Intelligence
Jesper N. Tegner
Summary: Machine translation now enables automatic detection of different cell types from single-cell transcriptomic data, allowing for the potential to dissect complex clinical samples like heterogeneous tumors at a large scale.
NATURE MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Sumeer Ahmad Khan, Alberto Maillo, Vincenzo Lagani, Robert Lehmann, Narsis A. Kiani, David Gomez-Cabrero, Jesper Tegner
Summary: The rise of single-cell genomics has provided an opportunity for machine learning algorithms. The scBERT method, inspired by the success of BERT in natural language processing, is a data-driven tool for annotating cell types in single-cell genomics data. However, the imbalance in cell-type distribution significantly affects the performance of scBERT, and careful consideration of data distribution and the introduction of subsampling techniques are necessary.
NATURE MACHINE INTELLIGENCE
(2023)
