Article
Genetics & Heredity
Jessica Schuster, George A. Tollefson, Valeria Zarate, Anthony Agudelo, Joan Stabila, Ashok Ragavendran, James Padbury, Alper Uzun
Summary: This study used whole exome sequencing and network analysis to identify shared gene networks associated with severe preeclampsia, as well as specific genes related to the pathogenic mechanisms of preeclampsia.
FRONTIERS IN GENETICS
(2022)
Article
Biotechnology & Applied Microbiology
Chongyang Ma, Kai Yan, Zisong Wang, Qiuyun Zhang, Lianyin Gao, Tian Xu, Jiayang Sai, Fafeng Cheng, Yuqiong Du
Summary: The study revealed a complex relationship between hypertension and NAFLD, identifying shared genes and potential mechanisms. The genes were associated with inflammatory, metabolic, and endocrine signals, suggesting a multi-target therapeutic strategy for future clinical research.
Article
Genetics & Heredity
Zixuan Meng, Linai Kuang, Zhiping Chen, Zhen Zhang, Yihong Tan, Xueyong Li, Lei Wang
Summary: A prediction model called WPDINM is proposed in this study to detect key proteins based on a novel weighted protein-domain interaction network. Experimental results show that WPDINM achieves significantly higher predictive accuracy for key protein identification compared to traditional competing measures.
FRONTIERS IN GENETICS
(2021)
Article
Genetics & Heredity
Basdeo Kushwaha, Neha Srivastava, Murali S. Kumar, Ravindra Kumar
Summary: This study aimed to understand the interaction of the brain-gonad system in Clarias magur reproduction by analyzing differentially expressed genes (DEG). Thirteen key genes in female brain & ovary and twelve key genes in male brain & testis were identified based on network analysis. Functional annotation revealed their involvement in important reproduction-related pathways. This study provides insights into the interaction between the brain and gonads in the regulation of Clarias magur reproduction through a protein-protein interaction network.
Article
Biochemical Research Methods
L. F. Signorini, T. Almozlino, R. Sharan
Summary: ANAT3.0 is a powerful Cytoscape plugin that offers updated PPI network databases and a new machine-learning layer to better reconstruct known signaling pathways.
BMC BIOINFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Peizhen Bai, Filip Miljkovic, Bino John, Haiping Lu
Summary: DrugBAN is a deep bilinear attention network framework that explicitly learns the local interactions between drugs and targets, and adapts to out-of-distribution data. It achieves the best performance on three benchmark datasets compared to five state-of-the-art baseline models. The visualized bilinear attention map provides interpretable insights from prediction results.
NATURE MACHINE INTELLIGENCE
(2023)
Article
Biochemistry & Molecular Biology
Su-Bin Yoon, Yu-Chien (Calvin) Ma, Akaash Venkat, Chun-Yu (Audi) Liu, Jie J. Zheng
Summary: This study investigated the genetic basis of Retinitis Pigmentosa (RP) and found that all causal genes of RP may belong within a complex network. By analyzing gene connections and protein interaction networks, the research successfully established network connections among RP genes and identified novel potential causal genes. The results suggest an interconnectedness causing RP at the molecular level.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Biochemistry & Molecular Biology
Caitlyn L. McCafferty, Edward M. Marcotte, David W. Taylor
Summary: Protein-protein interactions and the prediction of pairwise protein interaction interfaces were studied, utilizing a reduced representation of protein geometry and mapping of molecular properties. The research demonstrated comparable predictions to other structure-based tools and tolerance to conformational changes, highlighting the enhanced speed and capabilities of the simplified approach.
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
(2021)
Article
Physics, Multidisciplinary
Nehemiah T. Zewde, Rohaine V. Hsu, Dimitrios Morikis, Giulia Palermo
Summary: The complement system, an important defense mechanism against pathogens, can be hijacked by both complement deficiencies and invasive pathogens. A mathematical model was developed to study the interactions between complement and invading microbes, predicting complement imbalance in the nasopharynx associated with disruption to homeostasis. The model also suggests potential early infection biomarkers for sporadic meningococcal disease.
FRONTIERS IN PHYSICS
(2021)
Article
Genetics & Heredity
Guoqing Zhao, Pengpai Li, Xu Qiao, Xianhua Han, Zhi-Ping Liu
Summary: lncRNA-protein interactions play essential roles in cellular processes, but experimental methods are time-consuming and expensive. This study proposes a computational method called LncPNet that predicts potential interactions by embedding an lncRNA-protein network. Experimental results show promising performance and superior prediction compared to other methods.
FRONTIERS IN GENETICS
(2022)
Article
Biotechnology & Applied Microbiology
Marc Horlacher, Nils Wagner, Lambert Moyon, Klara Kuret, Nicolas Goedert, Marco Salvatore, Jernej Ule, Julien Gagneur, Ole Winther, Annalisa Marsico
Summary: RBPNet is a new deep learning method that predicts CLIP-seq crosslink count distribution from RNA sequence. Training on millions of regions, RBPNet shows high generalization on eCLIP, iCLIP, and miCLIP assays, outperforming state-of-the-art classifiers. RBPNet performs bias correction by modeling the raw signal as a mixture of protein-specific and background signal. By using Integrated Gradients for model interrogation, RBPNet identifies predictive sub-sequences corresponding to known and novel binding motifs and enables variant-impact scoring through in silico mutagenesis. Overall, RBPNet improves the imputation of protein-RNA interactions and enhances mechanistic interpretation of predictions.
Article
Engineering, Electrical & Electronic
Xiaoqin YANG, Xiujuan Lei, Jie ZHAO
Summary: The paper proposes a novel essential proteins prediction method, EPSFLA, which utilizes the Shuffled frog-leaping algorithm and integrates biological information with network topological structure to identify essential proteins.
CHINESE JOURNAL OF ELECTRONICS
(2021)
Review
Genetics & Heredity
Thomas David Daniel Kazmirchuk, Calvin Bradbury-Jost, Taylor Ann Withey, Tadesse Gessese, Taha Azad, Bahram Samanfar, Frank Dehne, Ashkan Golshani
Summary: The use of computation in peptide therapeutic development is increasingly recognized as a valuable tool for generating novel therapeutics. Computation has transformed the field of peptide design by identifying therapeutics with improved pharmacokinetic properties and reduced toxicity. The process involves molecular docking, molecular dynamics simulations, and machine learning algorithms. Despite the progress made, challenges remain in enhancing computational accuracy, improving success rates in preclinical and clinical trials, and predicting pharmacokinetics and toxicity. This review discusses past and current research in in-silico peptide therapeutics and highlights the potential of computation and artificial intelligence in future disease therapeutics.
Review
Cardiac & Cardiovascular Systems
Abhijeet Rajendra Sonawane, Elena Aikawa, Masanori Aikawa
Summary: Cardiovascular diseases (CVD) affect millions of people worldwide and can be studied using multiomics biomedical data and the approaches of systems biology and network medicine to understand their causes, mechanisms, and potential treatments.
FRONTIERS IN CARDIOVASCULAR MEDICINE
(2022)
Article
Biochemistry & Molecular Biology
Nikolai Koehler, Tim Daniel Rose, Lisa Falk, Josch Konstantin Pauling
Summary: Lipids play a crucial role in biological systems and can serve as biomarkers in medical applications. Although advances in lipidomics allow for the identification of numerous lipid species from biological samples, integrating pathway information into the systematic biological analysis of the lipidome remains challenging. The development of tools like LINEX, which combines statistical correlation and testing analysis with network-based approaches, shows promise in bridging this gap and facilitating a more comprehensive understanding of lipid metabolism.