Article
Multidisciplinary Sciences
Muta Tah Hira, M. A. Razzaque, Claudio Angione, James Scrivens, Saladin Sawan, Mosharraf Sarkar
Summary: The research focused on integrated multi-omics analysis of cancer data using VAE and MMD-VAE. The results indicated that MMD-VAE outperformed VAE in most omics datasets, and the compressed features could be used for cancer prognosis.
SCIENTIFIC REPORTS
(2021)
Article
Engineering, Geological
G. Y. Luo, H. Cao, H. Pan
Summary: The proposed method converts a slope stability optimization problem into a boundary value problem, using an anisotropic Laplace equation and a virtual seepage field to efficiently search for critical slip surfaces without the need for iterations or assumptions about the shape of the slip surface.
Article
Statistics & Probability
Roberta De Vito, Ruggero Bellio, Lorenzo Trippa, Giovanni Parmigiani
Summary: This paper analyzes breast cancer gene expression across multiple studies to identify replicable gene patterns related to known biological pathways. The proposed approach outperforms standard factor analysis in identifying reproducible signals in various scenarios. The R package MSFA implementing the method is available on GitHub.
ANNALS OF APPLIED STATISTICS
(2021)
Article
Biochemical Research Methods
Pengfei Lyu, Yan Li, Xiaoquan Wen, Hongyuan Cao
Summary: This study proposes a statistical method called JUMP for high-dimensional replicability analysis. JUMP uses paired p-value sequences to determine null or non-null states and controls the false discovery rate. By analyzing spatially resolved transcriptomic datasets, JUMP discovers biological findings that existing methods cannot obtain.
Article
Multidisciplinary Sciences
Rayees Rahman, Nicole Zatorski, Jens Hansen, Yuguang Xiong, J. G. Coen van Hasselt, Eric A. Sobie, Marc R. Birtwistle, Evren U. Azeloglu, Ravi Iyengar, Avner Schlessinger
Summary: Utilizing gene structure features to define gene set expression signatures can help characterize cellular properties, functions, and drug effects, facilitating interoperability across different experimental platforms.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Article
Computer Science, Artificial Intelligence
Zhao Kang, Hongfei Liu, Jiangxin Li, Xiaofeng Zhu, Ling Tian
Summary: Principal Component Analysis (PCA) is widely used for dimensionality reduction and feature extraction. Robust PCA (RPCA) can handle noise or outliers to some extent. However, real-world data may have structures that cannot be fully captured. Therefore, this paper proposes a novel method called Self-paced PCA (SPCA) to further reduce the effect of noise and outliers by learning gradually from simple to complex.
PATTERN RECOGNITION
(2023)
Review
Genetics & Heredity
Susana G. Martins, Rita Zilhao, Solveig Thorsteinsdottir, Ana Rita Carlos
Summary: Oxidative stress and DNA damage can disrupt the normal physiological equilibrium of cells and tissues, particularly in the process of ECM remodeling. Mutations in ECM genes have a significant impact on tissue homeostasis, leading to increased oxidative stress and potential accumulation of DNA damage, ultimately resulting in negative impacts on tissue and organ function.
FRONTIERS IN GENETICS
(2021)
Article
Computer Science, Artificial Intelligence
S. Eskandari, M. Seifaddini
Summary: This paper proposes a new approach for streaming feature selection by defining the redundancy analysis step as a binary optimization problem and adopting the binary bat algorithm to find the minimal informative subsets. Experimental studies show that this method outperforms other online and offline streaming feature selection methods in terms of classification accuracy.
PATTERN RECOGNITION
(2023)
Article
Biochemistry & Molecular Biology
Takuji Usui, Malcolm R. Macleod, Sarah K. McCann, Alistair M. Senior, Shinichi Nakagawa
Summary: This study explores the replicability of research results and challenges the traditional belief that experimental standardization is the key factor. By introducing heterogeneity in experimental design, the study suggests that replicability can be improved. The use of meta-analysis to simultaneously assess efficacy and stability in phenotypic outcomes can help identify treatments that are effective and generalize to the population level. By embracing variability in phenotypic outcomes, the shift towards heterogenization can improve both replicability and generalizability in preclinical research.
Article
Biochemical Research Methods
Yiru Sheng, R. Ayesha Ali, Andreas Heyland
Summary: CoRMAP is a meta-analysis tool that retrieves comparative gene expression data from any RNA-Seq dataset using de novo assembly, standardized gene expression tools, and implementing OrthoMCL. It ensures accurate comparison of gene expression levels between experiments and species by using orthogroup assignments.
BMC BIOINFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Mohammad Mahdi Kamani, Farzin Haddadpour, Rana Forsati, Mehrdad Mahdavi
Summary: In this study, we propose a fairness-aware PCA algorithm that preserves fairness while compromising the reconstruction loss to a certain extent. The algorithm guarantees the optimal trade-off between overall reconstruction loss and fairness constraints by utilizing Pareto optimality. Empirical studies demonstrate the superior performance of our algorithm compared to state-of-the-art methods.
Article
Biochemistry & Molecular Biology
Yang Li, Xiuli Lu, Zhihao Yu, Haozhen Wang, Bing Gao
Summary: In this study, it was found that ATP1A1 may be a key susceptibility gene involved in calcium stone formation. The down-regulation of ATP1A1 was associated with crystal deposition and activation of related signaling pathways. Overexpression of ATP1A1 or treatment with a specific inhibitor alleviated oxidative stress, inflammation, apoptosis, crystal-cell adhesion, and stone formation. These findings suggest that ATP1A1 could be a potential therapeutic target for treating calcium stones.
Article
Biochemical Research Methods
Fan Yang, Kevin J. Gleason, Jiebiao Wang, Jubao Duan, Xin He, Brandon L. Pierce, Lin S. Chen
Summary: The study introduces a new analysis method CCmed to detect replicable cis-mediated trans-associations in relevant conditions/studies by integrating statistics from multiple tissues/studies. Analyses of data from 13 brain tissues in the GTEx project revealed multiple cross-tissue trans-associations mediated by cis-gene expression, with evidence of replication in two studies. Additionally, trans-genes associated with schizophrenia loci in at least two brain tissues were identified.
Article
Biochemistry & Molecular Biology
Guya Diletta Marconi, Luigia Fonticoli, Ylenia Della Rocca, Stefano Oliva, Thangavelu Soundara Rajan, Oriana Trubiani, Giovanna Murmura, Francesca Diomede, Jacopo Pizzicannella
Summary: The study focused on the interaction between human periodontal ligament stem cells and two different implant titanium surfaces to evaluate cytotoxicity, cellular adhesion capacity, and improvement in ECM release. Various techniques including microscopic evaluation, viability assays, immunofluorescence, Western blot, and RT-PCR were used to analyze the parameters. Understanding cell/implant interaction is essential for developing more effective surfaces that enhance ECM release and promote osseointegration.
Review
Computer Science, Artificial Intelligence
Zohre Sadeghian, Ebrahim Akbari, Hossein Nematzadeh, Homayun Motameni
Summary: Feature selection is a real-world problem that aims to find a minimal subset of relevant features from an original feature set. Meta-heuristic algorithms have been used as an approach for feature selection, aiming to reduce computational costs and improve classification performance. This summary of meta-heuristic feature selection methods proposed from 2018 to 2022 provides insights into their evaluation criteria, fitness functions, classifiers used, and time complexity. The study highlights the need to use specific meta-heuristic algorithms depending on the dataset and identifies research gaps in this field.
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE
(2023)
Review
Public, Environmental & Occupational Health
R. De Vito, Yuan Chin Amy Lee, M. Parpinel, D. Serraino, Andrew Fergus Olshan, Jose Pedro Zevallos, F. Levi, Zhuo Feng Zhang, H. Morgenstern, W. Garavello, K. Kelsey, M. McClean, S. Schantz, Guo Pei Yu, P. Boffetta, Shu Chun Chuang, M. Hashibe, C. La Vecchia, G. Parmigiani, V. Edefonti
Article
Multidisciplinary Sciences
Otavio Cabral-Marques, Alexandre Marques, Lasse Melvaer Giil, Roberta De Vito, Judith Rademacher, Jeannine Guenther, Tanja Lange, Jens Y. Humrich, Sebastian Klapa, Susanne Schinke, Lena F. Schimke, Gabriele Marschner, Silke Pitann, Sabine Adler, Ralf Dechend, Dominik N. Mueller, Ioana Braicu, Jalid Sehouli, Kai Schulze-Forster, Tobias Trippel, Carmen Scheibenbogen, Annetine Staff, Peter R. Mertens, Madlen Loebel, Justin Mastroianni, Corinna Plattfaut, Frank Gieseler, Duska Dragun, Barbara Elizabeth Engelhardt, Maria J. Fernandez-Cabezudo, Hans D. Ochs, Basel K. Al-Ramadi, Peter Lamprecht, Antje Mueller, Harald Heidecke, Gabriela Riemekasten
NATURE COMMUNICATIONS
(2018)
Review
Nutrition & Dietetics
Valeria Edefonti, Roberta De Vito, Michela Dalmartello, Linia Patel, Andrea Salvatori, Monica Ferraroni
ADVANCES IN NUTRITION
(2020)
Review
Nutrition & Dietetics
Valeria Edefonti, Roberta De Vito, Andrea Salvatori, Francesca Bravi, Linia Patel, Michela Dalmartello, Monica Ferraroni
ADVANCES IN NUTRITION
(2020)
Article
Statistics & Probability
Roberta De Vito, Ruggero Bellio, Lorenzo Trippa, Giovanni Parmigiani
Summary: This paper analyzes breast cancer gene expression across multiple studies to identify replicable gene patterns related to known biological pathways. The proposed approach outperforms standard factor analysis in identifying reproducible signals in various scenarios. The R package MSFA implementing the method is available on GitHub.
ANNALS OF APPLIED STATISTICS
(2021)
Article
Multidisciplinary Sciences
Ranieri Coelho Salgado, Dennyson Leandro M. Fonseca, Alexandre H. C. Marques, Sarah Maria da Silva Napoleao, Tabata Takahashi Franca, Karen Tiemi Akashi, Caroline Aliane de Souza Prado, Gabriela Crispim Baiocchi, Desiree Rodrigues Placa, Gabriel Jansen-Marques, Igor Salerno Filgueiras, Roberta De Vito, Paula Paccielli Freire, Gustavo Cabral de Miranda, Niels Olsen Saraiva Camara, Vera Lucia Garcia Calich, Hans D. Ochs, Lena F. Schimke, Igor Jurisica, Antonio Condino-Neto, Otavio Cabral-Marques
Summary: This study used an integrative approach to uncover the landscape of human immune responses to Candida species, identifying key signaling molecules involved in TLR and IFN signaling cascades. The findings suggest potential molecular pathways for therapeutic intervention in anti-Candida immune responses, with genes showing cell type-specific expression patterns in various immune cells.
SCIENTIFIC REPORTS
(2021)
Meeting Abstract
Cardiac & Cardiovascular Systems
Kei Hang Katie Chan, Qing Liu, Alex P. Reiner, Roberta De Vito, Charles Kooperberg, Jennifer Brody, Leslie Lange, Joann E. Manson, Adolfo Correa, L. Adrienne Cupples, Matthew Flickinger, Jie Li, Xiaochen Lin, Tracy Madsen, Kari E. North, Laura M. Raffield, Alisa Manning, James B. Meigs, Simin Liu
Meeting Abstract
Rheumatology
F. Ingegnoli, R. De Vito, R. Caporali, M. Parpinel, G. Grosso, M. Ferraroni, V. Edefonti
ANNALS OF THE RHEUMATIC DISEASES
(2022)
Article
Medicine, General & Internal
Nickolas Lewis, Laura C. Chambers, Huong T. Chu, Taylor Fortnam, Roberta De Vito, Lisa M. Gargano, Philip A. Chan, James McDonald, Joseph W. Hogan
Summary: This study aimed to investigate the effect of vaccination after recovery from COVID-19 on preventing SARS-CoV-2 reinfection. The findings suggest that individuals who remained unvaccinated faced a relatively high risk of reinfection, while vaccination after recovery from COVID-19 was associated with reducing the risk of reinfection by approximately half.
Article
Biochemical Research Methods
E. Vistica Sampino, J. Morgan, A. Chorzalska, L. Nguyen, C. Yu, A. Rodriguez, M. Pardo, D. Bonal, O. Liang, M. Kim, R. De Vito, R. R. Lulla, P. M. Dubielecka
Summary: This study investigated the impact of fixation on immunophenotyping in flow cytometry, showing significant marker shifts in certain immune cell subpopulations. It emphasizes the importance of pre-experimental assessment of fixation-induced artifacts in flow cytometry output.
JOURNAL OF IMMUNOLOGICAL METHODS
(2022)
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
Immunology
Franziska Sotzny, Igor Salerno Filgueiras, Claudia Kedor, Helma Freitag, Kirsten Wittke, Sandra Bauer, Nuno Sepulveda, Dennyson Leandro Mathias da Fonseca, Gabriela Crispim Baiocchi, Alexandre H. C. Marques, Myungjin Kim, Tanja Lange, Desiree Rodrigues Placa, Finn Luebber, Frieder M. Paulus, Roberta De Vito, Igor Jurisica, Kai Schulze-Forster, Friedemann Paul, Judith Bellmann-Strobl, Rebekka Rust, Uta Hoppmann, Yehuda Shoenfeld, Gabriela Riemekasten, Harald Heidecke, Otavio Cabral-Marques, Carmen Scheibenbogen
Summary: Most patients with Post COVID Syndrome (PCS) present with a variety of symptoms, and a subset of them fulfill diagnostic criteria of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). This study found alterations in the levels of natural regulatory autoantibodies (AABs) targeting G-protein coupled receptors (GPCRs) in PCS patients, with correlations between AABs and symptom severity, particularly the ADRB2 antibodies associated with fatigue and vasomotor symptoms.
FRONTIERS IN IMMUNOLOGY
(2022)
Article
Virology
Gabriela C. C. Baiocchi, Aristo Vojdani, Avi Z. Z. Rosenberg, Elroy Vojdani, Gilad Halpert, Yuri Ostrinski, Israel Zyskind, Igor S. S. Filgueiras, Lena F. F. Schimke, Alexandre H. C. Marques, Lasse M. M. Giil, Yael B. B. Lavi, Jonathan I. I. Silverberg, Jason Zimmerman, Dana A. A. Hill, Amanda Thornton, Myungjin Kim, Roberta De Vito, Dennyson L. M. Fonseca, Desiree R. Placa, Paula P. P. Freire, Niels O. S. Camara, Vera L. G. Calich, Carmen Scheibenbogen, Harald Heidecke, Miriam T. T. Lattin, Hans D. D. Ochs, Gabriela Riemekasten, Howard Amital, Yehuda Shoenfeld, Otavio Cabral-Marques
Summary: COVID-19 patients may develop autoimmune diseases due to increased levels of autoantibodies, predominantly in moderate or severe cases. A comprehensive assessment of autoantibodies in 231 individuals, including COVID-19 patients and healthy controls, showed dysregulated IgG and IgA autoantibody signatures associated with COVID-19 severity. Elderly patients with severe disease had higher IgG autoantibody concentrations. This study suggests autoantibodies as potential therapeutic targets and biomarkers of COVID-19 severity.
JOURNAL OF MEDICAL VIROLOGY
(2023)
Article
Nutrition & Dietetics
Roberta De Vito, Federica Fiori, Monica Ferraroni, Silvia Cavalli, Roberto Caporali, Francesca Ingegnoli, Maria Parpinel, Valeria Edefonti
Summary: A recent Italian study found that higher consumption of olive oil and nuts may have a positive effect on disease activity in rheumatoid arthritis (RA), especially for patients with more severe or long-standing forms of the disease. Increasing intake of olive oil, olives, and nuts may be beneficial for improving disease activity in RA.
