Article
Computer Science, Interdisciplinary Applications
Kei Hirose, Kanta Miura, Atori Koie
Summary: This article proposes a cluster-based LDA method that improves prediction accuracy through hierarchical clustering and cross-validation, while addressing computational efficiency.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2023)
Article
Multidisciplinary Sciences
Bruna Lo Sasso, Luisa Agnello, Rosaria Vincenza Giglio, Caterina Maria Gambino, Anna Maria Ciaccio, Matteo Vidali, Marcello Ciaccio
Summary: Assessing anti-S-RBD IgG levels is essential for monitoring long-term antibody response. A third dose of the SARS-CoV-2 vaccine induces a strong immunological response, supporting the efficacy of the vaccine programs and the usefulness of the third dose.
SCIENTIFIC REPORTS
(2022)
Article
Computer Science, Artificial Intelligence
Fei Wang, Quan Wang, Feiping Nie, Zhongheng Li, Weizhong Yu, Rong Wang
Summary: In this paper, Un-LDA is proposed as an extension of LDA for unsupervised subspace learning and clustering. By optimizing the clusters using K-means and the subspace using supervised LDA methods alternately, Un-LDA overcomes the difficulty in solving non-convex objective optimization. Experiments show that Un-LDA algorithms are comparable or superior to existing methods.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Multidisciplinary Sciences
Andrea Maugeri, Martina Barchitta, Guido Basile, Antonella Agodi
Summary: By analyzing the epidemic patterns in different regions of Italy, clusters with distinct characteristics were identified, reflecting differences in the effectiveness of restrictions and testing strategies, as well as the timing and intensity of the epidemic. The findings provide policymakers with insights into the epidemic situation in different regions, guiding the adoption of appropriate countermeasures.
SCIENTIFIC REPORTS
(2021)
Article
Engineering, Civil
Vahab Amiri, Kei Nakagawa
Summary: This study utilized LDA for groundwater classification in a coastal aquifer and found that seasonal changes do not significantly affect the chemical composition of groundwater. The results showed that the SOM method can provide more accurate clustering of groundwater samples without any overlap between different clusters.
JOURNAL OF HYDROLOGY
(2021)
Article
Genetics & Heredity
Soumita Seth, Saurav Mallik, Tapas Bhadra, Zhongming Zhao
Summary: The major interest domains of single-cell RNA sequential analysis include identification of cell types, cell depiction, cell fate prediction, tumor classification, and investigation of cell heterogeneity. This study proposes a framework that integrates dimensionality reduction and hierarchical agglomerative clustering to discover cluster-specific frequent biomarkers from single-cell RNA sequencing data.
FRONTIERS IN GENETICS
(2022)
Article
Multidisciplinary Sciences
Nazish Shahid
Summary: This study compared neural network clustering (NNC) and hierarchical clustering (HC) to evaluate their computing dominance in classifying a populous data into clusters. An accurate clustering disposition is crucial for investigating the influence of predictors on a system over time. The results showed that simultaneous categorization of predictors and inputs through NNC and HC achieved a precision probability of 0.8, and the cluster genesis through combined HC and NNC was more reliable.
SCIENTIFIC REPORTS
(2023)
Article
Neurosciences
Hilmar R. J. van Weering, Tjalling W. Nijboer, Maaike L. Brummer, Erik W. G. M. Boddeke, Bart J. L. Eggen
Summary: Microglia are resident macrophages that play a key role in immune surveillance and maintaining CNS homeostasis. This study provides a computational pipeline for image segmentation, feature extraction, and morphological categorization of microglia using hierarchical clustering on principal components. The pipeline revealed regional variations in microglia morphologies but found no evidence of male-female differences in adult mice. The findings highlight the value of the pipeline for unbiased identification and classification of microglia in CNS (disease) models.
Article
Statistics & Probability
Kaijie Xue, Jin Yang, Fang Yao
Summary: Most existing methods for functional data classification focus on one or a few processes. In this work, we address the classification of high-dimensional functional data with a large number of potentially correlated functional processes. The challenge lies in the complex inter-correlation structures among multiple functional processes. We propose a penalized classifier that achieves near-perfect classification for functional data while maintaining discrimination set inclusion consistency. Simulation studies and real data applications demonstrate its favorable performance.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2023)
Article
Statistics & Probability
Kadri Umbleja, Manabu Ichino, Hiroyuki Yaguchi
Summary: The proposed algorithm in this paper, microscopic hierarchical conceptual clustering, allows for more accurate clustering of symbolic data based on microscopic similarities, producing more adequate conceptual clusters. Using quantile values enables comparison between different types of symbolic data easily without added complexity.
ADVANCES IN DATA ANALYSIS AND CLASSIFICATION
(2021)
Article
Spectroscopy
Afra Hacer Arslan, Fatma Uysal Ciloglu, Ummugulsum Yilmaz, Emrah Simsek, Omer Aydin
Summary: In this study, label-free surface-enhanced Raman Spectroscopy (SERS) combined with multivariate analysis successfully detected Cryptosporidium parvum oocysts, Escherichia coli, and Staphylococcus aureus. Each microorganism exhibited distinct spectral features, and PCA and hierarchical clustering effectively differentiated them.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2022)
Article
Computer Science, Artificial Intelligence
Quan Wang, Fei Wang, Fuji Ren, Zhongheng Li, Feiping Nie
Summary: This paper presents a novel clustering optimization method called Un-LDA(CD), which uses a coordinate descent algorithm instead of the K-means algorithm to achieve better performance on complex clustering cases.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Chemistry, Analytical
Sajid Farooq, Denise Maria Zezell
Summary: This study analyzes urine samples using ATR-FTIR spectroscopy and machine learning methods to evaluate diabetes, demonstrating excellent performance. It is of great significance for improving diabetes surveillance technologies and disease classification methods.
Article
Biochemical Research Methods
Alexander Becht, Curd Schollmayer, Yulia Monakhova, Ulrike Holzgrabe
Summary: Most drugs are no longer produced in their own countries by pharmaceutical companies, leading to difficulties in tracing their origin and creating opportunities for counterfeit drugs. This study used various spectroscopic methods combined with multivariate data analysis to investigate the possibility of determining the origin of drugs. The results showed that nuclear magnetic resonance and near-infrared data performed well in accurately assigning drug preparations to their pharmaceutical company or manufacturer.
ANALYTICAL AND BIOANALYTICAL CHEMISTRY
(2021)
Article
Multidisciplinary Sciences
Kye-Young Koh, Saleem Ahmad, Jae-il Lee, Guk-Hyun Suh, Chang-Min Lee
Summary: This study used PCA and HCPC methods to classify poultry farms in South Korea into four clusters, with cluster 4 having significantly higher rates of infections and mortality. Cluster 2 had strong culling measures in place for highly susceptible birds, while cluster 3 had significantly higher point prevalence rates of HPAI H5N6 cases.
Review
Spectroscopy
Khalil Mallah, Kazem Zibara, Coline Kerbaj, Ali Eid, Nour Khoshman, Zahraa Ousseily, Abir Kobeissy, Tristan Cardon, Dasa Cizkova, Firas Kobeissy, Isabelle Fournier, Michel Salzet
Summary: Traumatic brain injury (TBI) is a major public health concern worldwide, and the use of proteomic and lipidomic platforms has broadened our understanding of TBI-related mechanisms and neuropathological complications. This study provides an updated overview of spatially resolved microproteomics and microlipidomics approaches guided by mass spectrometry imaging in TBI studies and discusses their applications in neurotrauma research. The accurate and precise techniques of microproteomic sampling, such as laser capture microdissection, parafilm assisted microdissection, and liquid microjunction extraction, are evaluated. The potential of lipid profiling, particularly in phospholipid metabolism and proinflammatory molecules, is highlighted.
MASS SPECTROMETRY REVIEWS
(2023)
Article
Biology
Antonella Raffo-Romero, Soulaimane Aboulouard, Emmanuel Bouchaert, Agata Rybicka, Dominique Tierny, Nawale Hajjaji, Isabelle Fournier, Michel Salzet, Marie Duhamel
Summary: Developing a robust canine mammary tumor model in the form of tumoroids has been achieved, which can replicate the diversity and heterogeneity of tumors. Canine tumoroids are a reliable drug screening model and can be cryopreserved to create a biobank.
Review
Oncology
Remi Longuespee, Dirk Theile, Inka Zoernig, Jessica C. Hassel, Joshua Raoul Lindner, Walter E. Haefeli, Margaux Fresnais
Summary: Monoclonal antibodies acting as immune checkpoint inhibitors are widely used in oncology, but precision medicine approaches for personalized treatment are underutilized. Mass spectrometric approaches, coupled with new sample preparation methods, have the potential to stratify patients and detect resistance early on, providing valuable information for precision medicine.
INTERNATIONAL JOURNAL OF CANCER
(2023)
Article
Multidisciplinary Sciences
Diego Fernando Garcia-del Rio, Tristan Cardon, Sven Eyckerman, Isabelle Fournier, Amelie Bonnefond, Kris Gevaert, Michel Salzet
Summary: Scientists used subcellular fractionation to increase information about alternative proteins (AltProts) and facilitate the detection of protein-protein interactions. A total of 112 unique AltProts were identified, with 16 identified crosslinks between AltProts and referenced proteins (RefProts). The study of the interactome and localization of AltProts reveals the importance of the ghost proteome.
Article
Cell Biology
Alice Capuz, Sylvain Osien, Melodie Anne Karnoub, Soulaimane Aboulouard, Estelle Laurent, Etienne Coyaud, Antonella Raffo-Romero, Marie Duhamel, Amelie Bonnefond, Mehdi Derhourhi, Marco Trerotola, Ikram El Yazidi-Belkoura, David Devos, Monika Zilkova, Firas Kobeissy, Fabien Vanden Abeele, Isabelle Fournier, Dasa Cizkova, Franck Rodet, Michel Salzet
Summary: Using various omics analyses, we discovered that newborn rat astrocytes produce neural immunoglobulin chains with similarities to aberrant immunoglobulins found in cancers. The complete enzymatic V(D)J recombination complex was also found in astrocytes. In addition, we identified different immunoglobulin chains in adult rat astrocytes and primary astrocytes from human fetus. These neural IgGs play a role in astrocyte fate and interact with specific targets.
CELL DEATH & DISEASE
(2023)
Article
Oncology
Eric Leblanc, Fabrice Narducci, Gwenael Ferron, Audrey Mailliez, Jean-Yves Charvolin, El Hajj Houssein, Frederic Guyon, Virginie Fourchotte, Eric Lambaudie, Agathe Crouzet, Yves Fouche, Sebastien Gouy, Pierre Collinet, Frederic Caquant, Christophe Pomel, Francois Golfier, Veronique Vaini-Cowen, Isabelle Fournier, Michel Salzet, Emmanuelle Tresch, Alicia Probst, Anne-Sophie Lemaire, Marie-Cecile Le Deley, Delphine Hudry
Summary: A two-step approach of radical fimbriectomy followed by delayed oophorectomy appears to be a safe and well-tolerated method for reducing the risk of ovarian cancer in high-risk women, avoiding surgery-induced early menopause.
Article
Chemistry, Analytical
Khawla Seddiki, Frederic Precioso, Melissa Sanabria, Michel Salzet, Isabelle Fournier, Arnaud Droit
Summary: Liquid chromatography-mass spectrometry (LC-MS) is a powerful method for cell profiling in cancer research. However, the presence of noise, peak shifts, and high dimensionality of the data make early cancer diagnosis challenging. This study proposes an end-to-end deep learning methodology that addresses these challenges and preserves the potential of LC-MS data.
ANALYTICAL CHEMISTRY
(2023)
Article
Biochemistry & Molecular Biology
Dimitrios Diamantis, Antonios D. Tsiailanis, Christina Papaemmanouil, Maria-Christina Nika, Zoi Kanaki, Simona Golic Grdadolnik, Andrej Babic, Eleftherios Paraskevas Tzakos, Isabelle Fournier, Michel Salzet, Prem Prakash Kushwaha, Nikolaos S. Thomaidis, Theodoros Rampias, Eswar Shankar, Serdar Karakurt, Sanjay Gupta, Andreas G. Tzakos
Summary: A novel prodrug approach was developed to inhibit cancer progression by targeting alkaline phosphatase (ALP) in the tumor microenvironment (TME). The prodrug, phospho-apigenin, increased the stability of apigenin and showed enhanced antiproliferative effect in malignant cells with elevated ALP levels compared to apigenin alone. In vivo experiments also demonstrated its significant tumor growth suppression.
JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS
(2023)
Correction
Cell Biology
Alice Capuz, Sylvain Osien, Tristan Cardon, Melodie Anne Karnoub, Soulaimane Aboulouard, Antonella Raffo-Romero, Marie Duhamel, Dasa Cizkova, Marco Trerotola, David Devos, Firas Kobeissy, F. Vanden Abeele, Amelie Bonnefond, Isabelle Fournier, Franck Rodet, Michel Salzet
CELL DEATH & DISEASE
(2023)
Article
Biochemical Research Methods
Diego Fernando Garcia-del Rio, Isabelle Fournier, Tristan Cardon, Michel Salzet
Summary: This study presents a protocol for identifying human subcellular AltProts and deciphering their interactions using cross-linking mass spectrometry. The protocol includes steps for cell culture, in cellulo cross-linking, subcellular extraction, and sequential digestion, as well as liquid chromatography-tandem mass spectrometry and cross-link data analyses. The implementation of this workflow allows for the non-targeted identification of signaling pathways involving AltProts.
Review
Oncology
Andreas Bikfalvi, Cristine Alves da Costa, Tony Avril, Jean-Vianney Barnier, Luc Bauchet, Lucie Brisson, Pierre Francois Cartron, Helene Castel, Eric Chevet, Herve Chneiweiss, Anne Clavreul, Bruno Constantin, Valerie Coronas, Thomas Daubon, Monique Dontenwill, Francois Ducray, Natacha Entz-Werle, Dominique Figarella-Branger, Isabelle Fournier, Jean-Sebastien Frenel, Mathieu Gabut, Thierry Galli, Julie Gavard, Gilles Huberfeld, Jean-Philippe Hugnot, Ahmed Idbaih, Marie-Pierre Junier, Thomas Mathivet, Philippe Menei, David Meyronet, Celine Mirjolet, Fabrice Morin, Jean Mosser, Elisabeth Cohen-Jonathan Moyal, Veronique Rousseau, Michel Salzet, Marc Sanson, Giorgio Seano, Emeline Tabouret, Aurelie Tchoghandjian, Laurent Turchi, Francois M. Vallette, Somya Vats, Maite Verreault, Thierry Virolle
Summary: Glioblastoma (GBM) is a highly lethal brain tumor, and recent studies have emphasized the importance of the tumor microenvironment (TME) as a therapeutic target. However, a comprehensive understanding of the different cellular and molecular components involved in the GBM TME and their interactions is still needed for the development of more effective treatments. This review presents a comprehensive report on the GBM TME, combining the contributions of researchers and physicians in France, and provides a holistic view of the subject by describing the specific features of the GBM TME at the cellular, molecular, and therapeutic levels.
Article
Endocrinology & Metabolism
Lise Folon, Morgane Baron, Benedicte Toussaint, Emmanuel Vaillant, Mathilde Boissel, Victoria Scherrer, Helene Loiselle, Audrey Leloire, Alaa Badreddine, Beverley Balkau, Guillaume Charpentier, Sylvia Franc, Michel Marre, Soulaimane Aboulouard, Michel Salzet, Mickael Canouil, Mehdi Derhourhi, Philippe Froguel, Amelie Bonnefond
Summary: Rare biallelic pathogenic mutations in PCSK1 cause early-onset obesity and other endocrinopathies. Functional genomic study shows that rare heterozygous variants of PCSK1 can also impact obesity risk.
LANCET DIABETES & ENDOCRINOLOGY
(2023)