Article
Biology
Arwa Raies, Ewa Tulodziecka, James Stainer, Lawrence Middleton, Ryan S. Dhindsa, Pamela Hill, Ola Engkvist, Andrew R. Harper, Slave Petrovski, Dimitrios Vitsios
Summary: In this study, a stochastic semi-supervised ML framework called DrugnomeAI was developed to estimate the druggability likelihood for every protein-coding gene in the human exome. The tool generates exome-wide predictions based on known drug targets and provides specialized models stratified by disease type or drug therapeutic modality. The results show enrichment of genes previously selected for clinical development programs, as well as genome-wide significance in phenome-wide association studies.
COMMUNICATIONS BIOLOGY
(2022)
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
Xiao-Hong Xin, Ying-Ying Zhang, Chu-Qiao Gao, Hui Min, Likun Wang, Pu-Feng Du
Summary: Long noncoding RNAs (lncRNAs) play important roles in biological processes and identifying essential lncRNAs is crucial for disease diagnosis and treatment. Experimental methods for identification are costly and time consuming, thus computational methods can be an alternative approach. This study proposes a method that combines network centrality measures and lncRNA sequence information to identify essential lncRNAs and finds that network information significantly improves the predictive performance of sequence-based methods.
FRONTIERS IN GENETICS
(2022)
Article
Biotechnology & Applied Microbiology
Wenqi Chen, Shuang Wang, Tao Song, Xue Li, Peifu Han, Changnan Gao
Summary: In this study, a novel sequence-based computational approach called DCSE was proposed to predict potential protein-protein interactions (PPIs). The method utilized NLP-based encoding and feature extraction using multi-layer neural networks. Comparison with other models demonstrated the superior performance of the proposed method across all evaluation criteria.
Review
Biochemistry & Molecular Biology
Sara Omranian, Zoran Nikoloski, Dominik G. Grimm
Summary: This article provides a systematic review of state-of-the-art algorithms for protein complex prediction from protein-protein interaction networks. The existing approaches are categorized and compared, and the performance of eighteen methods is analyzed on benchmark networks. The limitations, drawbacks, and potential solutions in the field are discussed, emphasizing future research efforts.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2022)
Article
Biology
Youlin Zhan, Jiahan Liu, Min Wu, Chris Soon Heng Tan, Xiaoli Li, Le Ou-Yang
Summary: Detecting protein complexes is crucial for studying cellular organizations and functions. Existing computational methods for identifying protein complexes from protein-protein interaction (PPI) networks often ignore the signs of PPIs and do not consider joint clustering of multiple PPI networks. In this study, we propose a novel partially shared signed network clustering (PS-SNC) model that takes into account the signs of PPIs and can identify protein complexes from multiple state-specific signed PPI networks jointly.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Review
Biochemistry & Molecular Biology
Apurva Badkas, Sebastien De Landtsheer, Thomas Sauter
Summary: Protein-protein interaction network (PPIN) analysis is a method widely used to study protein functions and discover drug targets. This review discusses contextualization methods in PPIN analysis and explores ways to improve the quality of context-specific networks.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2022)
Article
Pharmacology & Pharmacy
Melissa Alegria-Arcos, Tabata Barbosa, Felipe Sepulveda, German Combariza, Janneth Gonzalez, Carmen Gil, Ana Martinez, David Ramirez
Summary: The COVID-19 pandemic has accelerated the development of drugs/vaccines by integrating scientists globally. In this study, researchers collected information on the interactions between proteins from SARS-CoV-2 and humans, as well as information on protein-drug interactions from public databases. By representing this data as networks, they were able to gain insights into the interactions between proteins from both organisms. Through data analysis, they identified important proteins and drugs from a network pharmacology perspective, suggesting potential therapeutic options for COVID-19. This study also revealed the potential importance of human proteins in drug repurposing campaigns.
FRONTIERS IN PHARMACOLOGY
(2022)
Article
Biotechnology & Applied Microbiology
Xinguo Lu, Fang Liu, Qiumai Miao, Ping Liu, Yan Gao, Keren He
Summary: Our study aimed to reveal functional overlapping patterns in gene modules to elucidate regulatory relationships between overlapping genes and communities, as well as explore cancer formation and progression. We analyzed six cancer datasets and identified three types of gene functional modules for each cancer, with our method outperforming others in terms of identifying distinguishing communities and survival prognostics for patients. In conclusion, overlapping genes play a crucial role in constructing comprehensive carcinogenesis by establishing communication bridges between different specific functional groups.
Article
Multidisciplinary Sciences
Simon Gosset, Annie Glatigny, Melina Gallopin, Zhou Yi, Marion Sale, Marie-Helene Mucchielli-Giorgi
Summary: Protein-protein interactions are crucial for cell processes and analyzing PPI networks can provide insights into protein functions. APPINetwork is a user-friendly open-source package for building and analyzing PPI networks from any species.
Article
Biotechnology & Applied Microbiology
Tim Downing, Min Jie Lee, Conor Archbold, Adam McDonnell, Alexander Rahm
Summary: The study highlights the importance of compatibility between plasmids and new host cells in the spread of antimicrobial resistance and virulence genes. Computational analysis of protein-protein interactions suggests that an excess rate of PPIs may be an indicator of host-plasmid compatibility.
Article
Biochemical Research Methods
Khalique Newaz, Tijana Milenkovic
Summary: Gene expression data and biological network data have the potential for inferring condition-specific gene networks, but current methods fail to capture dynamic processes. This study utilizes network propagation to infer a dynamic aging-related gene subnetwork, and predicts new aging-related protein candidates by studying the evolution of network structure with age.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Michael A. Skinnider, Charley Cai, R. Greg Stacey, Leonard J. Foster
Summary: PrInCE is an R/Bioconductor package that uses machine-learning to infer protein-protein interaction networks from CF-MS data, with improvements in runtime and memory requirements and new features such as tests for co-eluting protein complexes and differential network analysis. Extensively documented and fully compatible with Bioconductor classes, PrInCE can seamlessly fit into existing proteomics workflows.
Article
Biochemistry & Molecular Biology
Sara Omranian, Angela Angeleska, Zoran Nikoloski
Summary: GCC-v is an efficient, parameter-free algorithm that accurately predicts protein complexes, outperforming twelve state-of-the-art methods in multiple experimental scenarios. Its robustness to network perturbations is demonstrated in pan-plant PPI networks and Arabidopsis thaliana, highlighting its potential for impact assessment on predicted protein complexes.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2021)
Review
Biochemistry & Molecular Biology
Ahood Al-Eidan, Yihua Wang, Paul Skipp, Rob M. Ewing
Summary: USP7, also known as HAUSP, is a deubiquitinase that plays a crucial role in regulating the p53-MDM2 pathway and various biological processes, especially in cancer development. Its interactions with diverse proteins are important for its cancer-associated roles.