Article
Physics, Multidisciplinary
Pietro Cinaglia, Mario Cannataro
Summary: This paper presents a solution based on a graph neural network (GNN) for the identification of candidate gene-disease associations (GDAs). The model is trained with known relationships between genes and diseases and utilizes graph convolutions with multiple layers and non-linearity functions. Experimental results on the DisGeNET dataset show a 95% AUC for training, validation, and testing, with a 93% positive response rate for the Top-15 candidate GDAs identified by the solution.
Article
Automation & Control Systems
Long Sun, Zhenbing Liu, Xiyan Sun, Licheng Liu, Rushi Lan, Xiaonan Luo
Summary: In this paper, a fast and lightweight framework named weighted multi-scale residual network (WMRN) is proposed for a better tradeoff between image super-resolution performance and computational efficiency. The network utilizes depthwise separable convolutions and weighted multi-scale residual blocks to improve efficiency and multi-scale representation capability, with Convolutional layers in the reconstruction subnetwork to filter feature maps for high-quality image reconstruction. Extensive experiments show the effectiveness of WMRN compared to several state-of-the-art algorithms.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2021)
Article
Computer Science, Artificial Intelligence
Zhouxin Yu, Jintang Li, Liang Chen, Zibin Zheng
Summary: Bundle recommendation is widely used in real-world applications, but it faces challenges such as multiple associations, different interaction patterns between users and items/bundles, and data sparsity. To address these challenges, this paper proposes a Unified Hypergraph framework for Bundle Recommendation (UHBR), which can comprehensively represent the relationships among users, bundles, and items in a more flexible way.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Zewei He, Binjie Ding, Guizhong Fu, Yanpeng Cao, Jiangxin Yang, Yanlong Cao
Summary: This paper aims to enhance the performance of single-image super-resolution (SISR) by introducing a selective multi-scale network (SMsN) model which combines selective multi-scale module (SMsM) and attentive global feature fusion (AGFF) scheme. The SMsN model outperforms some state-of-the-art SISR methods in terms of accuracy and efficiency, as demonstrated by extensive experimental results.
SIGNAL IMAGE AND VIDEO PROCESSING
(2022)
Article
Multidisciplinary Sciences
Pengshun Li, Jiarui Chang, Yi Zhang, Yi Zhang
Summary: The study proposed a multi-zone order demand prediction model to effectively predict taxi order demand in different zones at city-scale. This model includes two steps: zone division and multi-zone order prediction, and utilizes multiple methods for prediction.
Article
Biochemical Research Methods
Jun Wang, Ziying Yang, Carlotta Domeniconi, Xiangliang Zhang, Guoxian Yu
Summary: CDPathway is a novel approach that can identify cooperative driver pathways by quantifying driver weights and constructing heterogeneous networks, which effectively identify candidate driver genes and reconstruct pathway interaction networks.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Lei Deng, Dayun Liu, Yizhan Li, Runqi Wang, Junyi Liu, Jiaxuan Zhang, Hui Liu
Summary: The study introduced an efficient framework called MSPCD to infer unknown circRNA-disease associations by integrating biological information and neural network feature extraction, and employing deep neural networks for prediction. Experimental results demonstrated that MSPCD outperformed previous methods, showing promising potential in inferring unknown circRNA-disease associations.
BMC BIOINFORMATICS
(2022)
Article
Multidisciplinary Sciences
Di Lu, Shuli Cheng, Liejun Wang, Shiji Song
Summary: In this paper, a new deep learning-based method for change detection is proposed, which utilizes multi-scale feature fusion and distribution strategies to improve the accuracy of change region detection. Experimental results demonstrate that this method outperforms other comparative methods.
SCIENTIFIC REPORTS
(2022)
Article
Biochemistry & Molecular Biology
Yibin Cheng, Fengling Lai, Xin Wang, Dantong Shang, Juan Zou, Majing Luo, Xizhong Xia, Hanhua Cheng, Rongjia Zhou
Summary: Spermatogenesis is essential for producing sperm cells, and the newly evolved gene srag in the teleost Monopterus albus plays an important role in promoting autophagy in testis. srag integrates into the autophagy network through interactions with Sox9 and Becn1. This gene enhances autophagy by interacting with Becn1 in both in vitro and in vivo analyses.
MOLECULAR BIOLOGY AND EVOLUTION
(2021)
Article
Biochemical Research Methods
Yang Li, Guanyu Qiao, Keqi Wang, Guohua Wang
Summary: This paper proposes a new model DTI-MGNN based on multi-channel graph convolutional network and graph attention for predicting drug-target interaction. The model combines topological structure and semantic features to improve the representation learning ability of drug-target interactions, and achieves state-of-the-art results on public datasets.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biology
Xiongzhi Wang, Yunfeng Nie, Wenqi Ren, Min Wei, Jingang Zhang
Summary: This study aims to develop a high-accuracy 3D simulation model for generating image datasets and acquiring real-time depth information in invasive surgery. An end-to-end multi-scale supervisory depth estimation network (MMDENet) is proposed for the depth estimation of binocular images. The proposed MMDENet utilizes a multi-scale feature extraction module and a multi-dimensional information-guidance refinement module, resulting in a 3.14% reduction in endpoint error compared to state-of-the-art methods.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Engineering, Electrical & Electronic
Thuong Nguyen Canh, Byeungwoo Jeon
Summary: Recent research has shown that deep learning-based multi-scale compressive imaging outperforms conventional methods in terms of reconstruction quality and speed, and the efficiency and performance of multi-scale sampling can be further improved by jointly learning to decompose, sample, and reconstruct images.
IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING
(2021)
Article
Environmental Sciences
Linbo Tang, Wei Tang, Xin Qu, Yuqi Han, Wenzheng Wang, Baojun Zhao
Summary: A scale-aware feature pyramid network (SARFNet) is proposed for multi-scale object detection within Synthetic Aperture Radar (SAR) images. The network incorporates a feature alignment module, a scale-equalizing pyramid convolution, and a self-learning anchor assignment strategy to flexibly match targets with different appearance changes.
Article
Computer Science, Artificial Intelligence
Xiaohan Chen, Beike Zhang, Dong Gao
Summary: This study introduces an automatic feature learning neural network based on raw vibration signals, which utilizes convolutional neural networks and long short-term memory to extract signal characteristics, achieving an accuracy of 98.46%.
JOURNAL OF INTELLIGENT MANUFACTURING
(2021)
Article
Computer Science, Artificial Intelligence
Ruizhi Han, Zhulin Liu, C. L. Philip Chen
Summary: This paper presents a new variant model of the Broad Learning System (BLS) for accurate diagnosis of Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) using MRI images. The proposed model integrates multi-scale convolution features and abstract features to achieve precise diagnosis. Experimental results demonstrate that the proposed model outperforms other methods in AD and MCI diagnostic tasks.
APPLIED SOFT COMPUTING
(2022)
Article
Biochemistry & Molecular Biology
Lilas Courtot, Elodie Bournique, Chrystelle Maric, Laure Guitton-Sert, Miguel Madrid-Mencia, Vera Pancaldi, Jean-Charles Cadoret, Jean-Sebastien Hoffmann, Valerie Bergoglio
Summary: This study reveals that low replication stress can lead to advanced DNA replication timing, which is cell-type specific and involves large heterochromatin domains. These advanced events can be inherited by the next generation of cells, leading to changes in chromatin accessibility, replication origin landscape, and gene expression.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Article
Immunology
Lucille Stuani, Marie Sabatier, Estelle Saland, Guillaume Cognet, Nathalie Poupin, Claudie Bosc, Florence A. Castelli, Lara Gales, Evgenia Turtoi, Camille Montersino, Thomas Farge, Emeline Boet, Nicolas Broin, Clement Larrue, Natalia Baran, Madi Y. Cisse, Marc Conti, Sylvain Loric, Tony Kaoma, Alexis Hucteau, Aliki Zavoriti, Ambrine Sahal, Pierre-Luc Mouchel, Mathilde Gotanegre, Cedric Cassan, Laurent Fernando, Feng Wang, Mohsen Hosseini, Emeline Chu-Van, Laurent Le Cam, Martin Carroll, Mary A. Selak, Norbert Vey, Remy Castellano, Francois Fenaille, Andrei Turtoi, Guillaume Cazals, Pierre Bories, Yves Gibon, Brandon Nicolay, Sebastien Ronseaux, Joseph R. Marszalek, Koichi Takahashi, Courtney D. DiNardo, Marina Konopleva, Vera Pancaldi, Yves Collette, Floriant Bellvert, Fabien Jourdan, Laetitia K. Linares, Christian Recher, Jean-Charles Portais, Jean-Emmanuel Sarry
Summary: Mutations in IDH lead to enhanced mitochondrial oxidative metabolism in AML, involving increased electron transport chain activity and fatty acid oxidation. IDH1 mutant inhibitors reduce 2-HG levels and CEBPα methylation, but do not reverse fatty acid oxidation and OxPHOS. Targeting mitochondrial activities may improve anti-AML efficacy of IDH mutant inhibitors.
JOURNAL OF EXPERIMENTAL MEDICINE
(2021)
Article
Biochemical Research Methods
Alexis Coullomb, Vera Pancaldi
Summary: tysserand is a Python library designed to reconstruct spatial networks from spatially resolved omics experiments. It serves as a common tool for the bioinformatics community, allowing for the integration of multiple methods, parameter selection, network cleaning, and data transfer to other libraries.
Article
Biochemical Research Methods
A. Xenos, N. Malod-Dognin, S. Milinkovic, N. Przulj
Summary: This study introduces algorithms based on network embeddings to untangle the complexity of omics data and mine them for new biomedical information. By decomposing matrices with Nonnegative Matrix Tri-Factorization, the study demonstrates that genes with similar biological functions are embedded close in space and can extract new biomedical knowledge through linear operations on their vector representations. The method successfully predicts new genes participating in protein complexes and identifies cancer-related genes with potential clinical relevance based on cosine similarities between vector representations.
Article
Oncology
Jaume Fores-Martos, Cesar Boullosa, David Rodrigo-Dominguez, Jon Sanchez-Valle, Beatriz Suay-Garcia, Joan Climent, Antonio Falco, Alfonso Valencia, Joan Anton Puig-Butille, Susana Puig, Rafael Tabares-Seisdedos
Summary: Epidemiological studies have found an inverse comorbidity between neurodegenerative disorders and overall cancer risk, with specific site-specific cancers showing varying risk associations. The study aimed to investigate the molecular, genetic, and pharmacological links between Alzheimer's and Parkinson's diseases and various cancer types, identifying significant transcriptomic associations and genetic correlations, especially between Parkinson's disease, prostate cancer, and melanoma. The research expands on previous findings by examining new ways of genetic interactions and the role of drugs in the comorbid associations between neurodegeneration and cancer.
Article
Biotechnology & Applied Microbiology
Marie Rouanet, Naima Hanoun, Hubert Lulka, Cindy Ferreira, Pierre Garcin, Martin Sramek, Godefroy Jacquemin, Agnes Coste, Delphine Pagan, Carine Valle, Emeline Sarot, Vera Pancaldi, Frederic Lopez, Louis Buscail, Pierre Cordelier
Summary: Toll-like receptors (TLRs) play a dual role in pancreatic cancer, inhibiting tumor cell proliferation and inducing cell death, but also potentially promoting tumor growth. Further investigations reveal that TLR7 agonists modulate the characteristics of tumor-associated macrophages, and depletion of these macrophages hampers TLR7 agonist-induced tumor growth.
Article
Biochemistry & Molecular Biology
Karolina Jodkowska, Vera Pancaldi, Maria Rigau, Ricardo Almeida, Jose M. Fernandez-Justel, Osvaldo Grana-Castro, Sara Rodriguez-Acebes, Miriam Rubio-Camarillo, Enrique Carrillo-de Santa Pau, David Pisano, Fatima Al-Shahrour, Alfonso Valencia, Maria Gomez, Juan Mendez
Summary: In this study, the authors analyzed the activity of origins in mouse embryonic stem cells and examined their response to replicative stress. They found that stressed origins were also active in a small fraction of cells during normal S phase, and stress increased their activation frequency. The study also identified that origin efficiency is proportional to the proximity to transcriptional start sites and the number of contacts established between origin-containing chromatin fragments.
NUCLEIC ACIDS RESEARCH
(2022)
Article
Oncology
Patricia Johansson, Teresa Laguna, Julio Ossowski, Vera Pancaldi, Martina Brauser, Ulrich Duehrsen, Lara Keuneke, Ana Queiros, Julia Richter, Jose Martin-Subero, Reiner Siebert, Brigitte Schlegelberger, Ralf Kueppers, Jan Duerig, Eva M. Murga Penas, Enrique Carillo-de Santa Pau, Anke K. Bergmann
Summary: This study identifies altered DNA methylation in T-LGLL cells compared to benign T cells, with BCL11B being highly differentially methylated. Although validation in a larger patient cohort is needed, BCL11B could be considered as a potential biomarker for this leukemia. Additionally, altered gene expression and hypermethylation of enhancer regions could serve as potential mechanisms for treatment of this disease.
CLINICAL EPIGENETICS
(2022)
Correction
Oncology
Patricia Johansson, Teresa Laguna, Julio Ossowski, Vera Pancaldi, Martina Brauser, Ulrich Duehrsen, Lara Keuneke, Ana Queiros, Julia Richter, Jose I. Martin-Subero, Reiner Siebert, Brigitte Schlegelberger, Ralf Kueppers, Jan Duerig, Eva M. Murga Penas, Enrique Carrillo-de Santa Pau, Anke K. Bergmann
CLINICAL EPIGENETICS
(2023)
Article
Cell Biology
Vera Pancaldi
Summary: With the increasing availability of datasets and experimental assays on chromatin organization, there is a need for tools to visualize and analyze these structures. Network theory approaches have become popular for describing 3D epigenome organization, enabling visualization of 1D epigenomics datasets and analysis of 3D organization and dynamics. This review summarizes the applications of network theory in studying chromatin contact maps and highlights its potential in uncovering epigenomic patterns and their relationship to cellular phenotypes.
CURRENT OPINION IN GENETICS & DEVELOPMENT
(2023)
Article
Multidisciplinary Sciences
Nina Verstraete, Malvina Marku, Marcin Domagala, Helene Arduin, Julie Bordenave, Jean-Jacques Fournie, Loic Ysebaert, Mary Poupot, Vera Pancaldi
Summary: Monocyte-derived macrophages play a crucial role in maintaining tissue homeostasis and defending against pathogens. In the context of tumors, tumor-associated macrophages, including nurse-like cells (NLCs) in chronic lymphocytic leukemia, promote tumorigenesis and protect cancer cells from apoptosis. The development of an agent-based model allowed for the identification of different macrophage phenotypes and their impact on cancer cell survival, highlighting the potentially important role of phagocytosis in NLC polarization and cancer progression.
Meeting Abstract
Oncology
Maria-Fernanda Senosain, Yong Zou, Khushbu Patel, Vera Pancaldi, Carlos F. Lopez, Pierre P. Massion
Meeting Abstract
Oncology
Audrey Lumeau, Nicolas Bery, Cyril Ribeyre, Samad Elkaoutari, Guillaume Labrousse, Miguel Madrid-Mencia, Vera Pancaldi, Marie-Jeanne Pillaire, Valerie Bergoglio, Nelson Dusseti, Jean-Sebastien Hoffmann, Louis Buscail, Malik Lutzmann, Pierre Cordelier
Correction
Biotechnology & Applied Microbiology
Kim-Anh Le Cao, Al J. Abadi, Emily F. Davis-Marcisak, Lauren Hsu, Arshi Arora, Alexis Coullomb, Atul Deshpande, Yuzhou Feng, Pratheepa Jeganathan, Melanie Loth, Chen Meng, Wancen Mu, Vera Pancaldi, Kris Sankaran, Dario Righelli, Amrit Singh, Joshua S. Sodicoff, Genevieve L. Stein-O'Brien, Ayshwarya Subramanian, Joshua D. Welch, Yue You, Ricard Argelaguet, Vincent J. Carey, Ruben Dries, Casey S. Greene, Susan Holmes, Michael I. Love, Matthew E. Ritchie, Guo-Cheng Yuan, Aedin C. Culhane, Elana Fertig
Article
Biotechnology & Applied Microbiology
Kim-Anh Le Cao, Al J. Abadi, Emily F. Davis-Marcisak, Lauren Hsu, Arshi Arora, Alexis Coullomb, Atul Deshpande, Yuzhou Feng, Pratheepa Jeganathan, Melanie Loth, Chen Meng, Wancen Mu, Vera Pancaldi, Kris Sankaran, Amrit Singh, Joshua S. Sodicoff, Genevieve L. Stein-O'Brien, Ayshwarya Subramanian, Joshua D. Welch, Yue You, Ricard Argelaguet, Vincent J. Carey, Ruben Dries, Casey S. Greene, Susan Holmes, Michael Love, Matthew E. Ritchie, Guo-Cheng Yuan, Aedin C. Culhane, Elana Fertig