Article
Endocrinology & Metabolism
Xiaojian Mao, Liangliang Tang, Hongyi Li, Wen Zhang, Li Liu, Heyong Wang, Abdalbari Headar
Summary: This study aims to explore the functions and gene sets of mutated genes related to hyperthyroidism in children. Through gene ontology and biological signaling pathway analysis, it was found that hormone activity and response to peptide hormone are the most significant gene ontology functions and thyroid hormone signaling pathway is the most significant biological signaling pathway. The identified mutated genes provide insights into the expected effects of multiple mutated genes on hyperthyroidism in children.
FRONTIERS IN ENDOCRINOLOGY
(2023)
Article
Agriculture, Dairy & Animal Science
Fatemeh Mohammadinejad, Mohammadreza Mohammadabadi, Zahra Roudbari, Tomasz Sadkowski
Summary: This study aimed to identify the hub genes and mechanisms involved in skeletal muscle maturation and hypertrophy in livestock species (Bos taurus, Ovis aries, and Sus scrofa). Gene expression profiles were analyzed, and differentially expressed genes and enriched pathways were identified in each species. Common genes and pathways were also identified, which play important roles in the development and maturation of skeletal muscles. This study provides valuable insights into the relationships between genes and biological pathways in the skeletal muscle maturation process.
Review
Oncology
Marjanu Hikmah Elias, Srijit Das, Nazefah Abdul Hamid
Summary: This systematic review and integrated bioinformatic analysis provide new insights into the key genes and pathways involved in the pathogenesis of cervical cancer, which can be beneficial for developing better screening and treatment strategies.
Article
Agriculture, Dairy & Animal Science
Morteza Bitaraf Sani, Zahra Roudbari, Omid Karimi, Mohammad Hossein Banabazi, Saeid Esmaeilkhanian, Nader Asadzadeh, Javad Zare Harofte, Ali Shafei Naderi, Pamela Anna Burger
Summary: This project aimed to find biological themes affecting growth in dromedaries. By analyzing candidate SNPs and related genes, the main biological functions related to growth were identified, providing potential candidate genes for camel breeding programs.
Article
Biochemistry & Molecular Biology
Ekaterina Rafikova, Nikolay Nemirovich-Danchenko, Anna Ogmen, Anna Parfenenkova, Anastasiia Velikanova, Stanislav Tikhonov, Leonid Peshkin, Konstantin Rafikov, Olga Spiridonova, Yulia Belova, Timofey Glinin, Anastasia Egorova, Mikhail Batin
Summary: The Open Genes database was created to simplify the search for potential aging therapy targets. It provides comprehensive data on 2402 genes associated with aging, including their lifespan-extending interventions, age-related changes, longevity associations, gene evolution, associations with diseases and hallmarks of aging, and gene product functions. The database is publicly accessible through an API and a user interface at https://open-genes.org/.
NUCLEIC ACIDS RESEARCH
(2023)
Article
Public, Environmental & Occupational Health
Xue-Meng Gao, Xiu-Hua Zhou, Meng-Wei Jia, Xiao-Zhuo Wang, Dan Liu
Summary: When the body damages its own tissues in response to an infection, sepsis develops. Understanding the molecular mechanisms of sepsis and identifying potential molecular targets for treatment is crucial. We used WGCNA analysis to construct two modules, the light-green GSE131761 module and the blue GSE137342 module, which showed the strongest links to sepsis. Gene ontology analysis revealed that genes in the light-green module were involved in inflammatory response, while genes in the blue module were associated with proteasomal protein catabolic process. Additionally, two hub genes, ANKRD22 and VNN1, were identified as crucial genes in sepsis etiology.
PREVENTIVE MEDICINE
(2023)
Article
Immunology
Raushan Kumar Chaudhary, L. Ananthesh, Prakash Patil, Uday Venkat Mateti, Sanjit Sah, Aroop Mohanty, Rama S. Rath, Bijaya Kumar Padhi, Sumira Malik, Kadhim Hussein Jassim, Moustafa A. Al-Shammari, Yasir Waheed, Prakasini Satapathy, Joshuan J. Barboza, Alfonso J. Rodriguez-Morales, Ranjit Sah
Summary: This study identified key genes and pathways associated with H5N1 infection in humans through protein-protein interaction and functional enrichment analysis. These hub genes and pathways play important roles in the diagnosis, prognosis, and treatment of H5N1 infection.
Article
Computer Science, Artificial Intelligence
Pushpak Pati, Guillaume Jaume, Antonio Foncubierta-Rodriguez, Florinda Feroce, Anna Maria Anniciello, Giosue Scognamiglio, Nadia Brancati, Maryse Fiche, Estelle Dubruc, Daniel Riccio, Maurizio Di Bonito, Giuseppe De Pietro, Gerardo Botti, Jean-Philippe Thiran, Maria Frucci, Orcun Goksel, Maria Gabrani
Summary: The accurate diagnosis, prognosis, and therapy response predictions for cancer patients rely heavily on the phenotype and distribution of histological entities in tissue specimens. Various methods have been developed to represent tissue structures using cell-graphs, leveraging graph theory and machine learning. This study proposes a novel hierarchical entity graph representation for tissue specimens and introduces a hierarchical graph neural network to map tissue structure to functionality. Through evaluation with the BRACS dataset, the proposed method demonstrates superior classification results compared to alternative methods and individual pathologists.
MEDICAL IMAGE ANALYSIS
(2022)
Article
Cell Biology
Ammad Shaukat, Muhammad Haider Farooq Khan, Hina Ahmad, Zain Umer, Muhammad Tariq
Summary: A previously unknown interaction between the histone kinase Ballchen and CBP was discovered in Drosophila, revealing a new pathway for maintaining gene activation through the regulation of H3K27ac levels. Their co-localization at actively transcribed genes suggests a synergistic relationship in regulating gene expression during development.
FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY
(2021)
Article
Clinical Neurology
Young Jun Ko, Soo Yeon Kim, Seungbok Lee, Jihoon G. Yoon, Man Jin Kim, Hyeji Jun, Hunmin Kim, Jong-Hee Chae, Ki Joong Kim, Kwangsoo Kim, Byung Chan Lim
Summary: This study aimed to identify the clinical and genetic characteristics of patients with epilepsy in an NDD cohort and demonstrate the importance of genetic testing. The results showed no significant differences in clinical features and treatment responses between NDD patients with epilepsy caused by epilepsy-related genes and those caused by NDD-related genes. Further studies are needed to understand the integrated pathomechanisms.
FRONTIERS IN NEUROLOGY
(2023)
Article
Medicine, General & Internal
Yisheng Peng, Zhengli Liu, Guanqi Fu, Boxiang Zhao, Maofeng Gong, Zhaoxuan Lu, Yangyi Zhou, Liang Chen, Haobo Su, Wensheng Lou, Guoping Chen, Xu He, Jianping Gu, Jie Kong
Summary: This study aimed to identify the signaling pathways and immune microenvironments related to elderly stroke patients. It was found that increased age was significantly positively correlated with myeloid-derived suppressor cells and natural killer T cells, and negatively correlated with immature dendritic cells.
Article
Immunology
Ali Mahmoudia, Stephen L. Atkin, Tannaz Jamialahmadi, Amirhossein Sahebkar
Summary: This study aimed to investigate the effect of statins on genes/proteins involved in foam cell formation. Analysis of gene expression profiles and protein-protein interactions revealed various critical overexpressed genes, including G6PD, NPC1, ABCA1, ABCG1, PGD, PLIN2, PPAP2B, and TXNRD1. Functional enrichment analysis showed that these genes were involved in cholesterol metabolism and biosynthesis. Molecular docking analysis indicated that lipophilic statins, especially pitavastatin and lovastatin, had a significant effect on foam cell formation, with ABCA1 being the most important protein target.
INTERNATIONAL IMMUNOPHARMACOLOGY
(2023)
Article
Endocrinology & Metabolism
Zhuo Gao, S. Aishwarya, Xiao-mei Li, Xin-lun Li, Li-na Sui
Summary: This study identified potential key genes and pathways involved in the progression of DKD through integrated bioinformatics analysis. Several differentially expressed genes with roles in DKD progression were identified, along with two drugs that have a validated role in reversing the observed differential gene expression patterns.
FRONTIERS IN ENDOCRINOLOGY
(2021)
Article
Plant Sciences
Reena Rani, Ghulam Raza, Hamza Ashfaq, Muhammad Rizwan, Muhammad Khuram Razzaq, Muhammad Qandeel Waheed, Hussein Shimelis, Allah Ditta Babar, Muhammad Arif
Summary: Soybean is a significant crop globally, and this study used genome-wide association studies to identify genes and functional markers associated with soybean agronomic traits, providing valuable data for future breeding programs.
FRONTIERS IN PLANT SCIENCE
(2023)
Article
Biochemistry & Molecular Biology
Maria Tziastoudi, Christos Cholevas, Theoharis C. Theoharides, Ioannis Stefanidis
Summary: This study utilized gene ontology analysis and protein network construction to identify the potential roles of immune-related molecules and Cadherin/Wnt signaling pathways in diabetic nephropathy, suggesting them as potential therapeutic targets for the treatment of DN.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Cardiac & Cardiovascular Systems
Jae-Seung Yun, Jaesik Kim, Sang-Hyuk Jung, Seon-Ah Cha, Seung-Hyun Ko, Yu-Bae Ahn, Hong-Hee Won, Kyung-Ah Sohn, Dokyoon Kim
Summary: This study developed and evaluated a deep learning algorithm for screening type 2 diabetes using retinal images. The algorithm showed improved risk stratification when combined with traditional risk factors, making it a useful tool for identifying individuals at high risk of type 2 diabetes.
NUTRITION METABOLISM AND CARDIOVASCULAR DISEASES
(2022)
Article
Biology
Vivek Sriram, Manu Shivakumar, Sang-Hyuk Jung, Yonghyun Nam, Lisa Bang, Anurag Verma, Seunggeun Lee, Eun Kyung Choe, Dokyoon Kim
Summary: This article introduces a tool for generating disease-disease network visualizations based on summary statistics from phenome-wide association studies. The tool was used to construct a network to study the relationships between human diseases. By analyzing this network, potential genetic explanations for the relationships between diseases can be identified, and a better understanding of the underlying architecture of human diseases can be achieved. This tool can help researchers identify potential genetic targets for drug design and contribute to the exploration of personalized medicine.
Letter
Oncology
Sang-Hyuk Jung, Dokyoon Kim, Ji Won Park
BRITISH JOURNAL OF CANCER
(2022)
Article
Computer Science, Hardware & Architecture
Jeong-Hyeon Moon, Jun-Hyung Yu, Kyung-Ah Sohn
Summary: This paper proposes a novel ensemble learning approach for anomaly detection by extracting specific features to improve overall accuracy and detect diverse types of unknown attacks. The method also considers unexpressed variations in the training data and can be deployed in various CPS environments.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Endocrinology & Metabolism
Eun Kyung Choe, Manu Shivakumar, Seung Mi Lee, Anurag Verma, Dokyoon Kim
Summary: This study used genotyping and health data to investigate the association between polygenic risk score (PRS) for obesity and obesity-related diseases. The results showed that low PRS may be a risk factor for metabolically unhealthy lean body.
INTERNATIONAL JOURNAL OF OBESITY
(2022)
Article
Computer Science, Artificial Intelligence
Namhyuk Ahn, Byungkon Kang, Kyung-Ah Sohn
Summary: This study focuses on the application of deep learning models in single-image super-resolution and proposes an architecture design that enhances network performance through cascading mechanism and feature fusion. By implementing group convolution and recursive schemes, the model achieves high efficiency and improves output quality. The research shows that the method performs well in various tasks.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Information Systems
Jun-Hyung Yu, Jeong-Hyeon Moon, Kyung-Ah Sohn
Summary: This paper proposes a new framework for video anomaly detection by designing a convolutional long short-term memory-based model that emphasizes semantic objects using self-attention mechanisms and concatenation operations to improve performance. The experiments demonstrated that our framework outperformed previous models on the Chinese University of Hong Kong dataset.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Jang Wook Kim, So Yeon Kim, Kyung-Ah Sohn
Summary: Dataset bias is a significant obstacle in image classification, particularly in few-shot learning with limited samples per class. To address this, we propose a bias prediction network that recovers biases from image data features, improving few-shot image classification performance. Our method trains the framework to extract features that are difficult for the bias prediction network to recover. We evaluate our approach on multiple benchmark datasets and integrate it with existing few-shot learning models, showing improved performance in different scenarios. The proposed bias prediction model is compatible with other few-shot learning models and applicable to real-world applications with biased samples.
Article
Computer Science, Artificial Intelligence
Md Azher Uddin, Joolekha Bibi Joolee, Kyung-Ah Sohn
Summary: This paper proposes a novel deep multi-modal framework that effectively utilizes facial and verbal cues for automated depression assessment. By analyzing audio and video data and applying specific algorithms and strategies, this method can diagnose depression more accurately than other existing methods.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
Article
Biotechnology & Applied Microbiology
Sehee Wang, So Yeon Kim, Kyung-Ah Sohn
Summary: Feature selection methods are crucial for accurate disease classification and identifying informative biomarkers. ClearF++ addresses the limitations of previous methods by using reconstruction error from low-dimensional embeddings as a proxy for entropy term and incorporating feature-wise clustering. It outperforms other commonly used methods in terms of prediction accuracy and stability, making it valuable for biomedical data analysis.
BIOENGINEERING-BASEL
(2023)
Article
Computer Science, Information Systems
Seungmin Jang, Jeong-Hyeon Moon, So Yeon Kim, Kyung-Ah Sohn
Summary: With the increasing number of untrimmed videos on the internet, there is a growing demand for advanced action segmentation methods that can accurately localize sequences within lengthy videos. Traditional approaches have tried to address the issue of over-segmentation by smoothing consecutive frame predictions, but this may overlook important spatio-temporal characteristics. To address these challenges more effectively, we propose a novel approach that constructs a geometric curve based on frame-wise embeddings and extracts curvature features. Experimental results show that incorporating curvature information into existing action segmentation models can significantly enhance performance.
Article
Biochemical Research Methods
Yonghyun Nam, Sang-Hyuk Jung, Jae-Seung Yun, Vivek Sriram, Pankhuri Singhal, Marta Byrska-Bishop, Anurag Verma, Hyunjung Shin, Woong-Yang Park, Hong-Hee Won, Dokyoon Kim
Summary: Understanding comorbidity is crucial for disease prevention and treatment. In this study, we introduce the use of an inter-disease interactivity network to discover and prioritize comorbidities. By considering phenotype associations, we develop a comorbidity scoring algorithm and predict the priority of comorbid diseases. The findings highlight the importance of considering interaction when predicting comorbidity.
Article
Computer Science, Artificial Intelligence
Jeong-Hyeon Moon, Namhyuk Ahn, Kyung-Ah Sohn
Summary: This article discusses the impact of distribution mismatch on the reliability of AI systems and proposes a solution by decomposing the definition of in-distribution into texture and semantics. A new benchmark is introduced to measure the robustness and precision of OOD detection methods, and a divide-and-conquer approach is presented to achieve a balance between performance and robustness.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)