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2007 Proceedings of the 5th Asia-Pacific Bioinformatics Conference  
The result suggests that the application of the network-flow model to PPI data is useful for extracting credible interactions from noisy experimental data.  ...  Our score is calculated as the dominant eigenvector of an adjacency matrix and represents the steady state of the network flow.  ...  This work was supported by a Grant-in-Aid for Scientific Research on Priority Areas "Systems Genomics" and COE Program Grant "Genome Language" from the Ministry of Education, Culture, Sports, Science and  ... 
doi:10.1142/9781860947995_0034 fatcat:gryt5jqbrbekdlfhzhfner5blu

Supporting cognition in systems biology analysis: findings on users' processes and design implications

Barbara Mirel
2009 Journal of Biomedical Discovery and Collaboration  
Current usability studies of bioinformatics tools suggest that tools for exploratory analysis support some tasks related to finding relationships of interest but not the deep causal insights necessary  ...  Researchers interacted with the same proteinprotein interaction tools to discover possible disease mechanisms for further experimentation.  ...  I would like to thank Ben Keller, Fan Meng, Weijian Xuan, and Magesh Jayapandian for their critical reviews of or collaborative contributions to the design implications of findings.  ... 
doi:10.1186/1747-5333-4-2 pmid:19216777 pmcid:PMC2649900 fatcat:zgi75ipuorgstltuxbiph4nshm

Uncovering New Pathogen–Host Protein–Protein Interactions by Pairwise Structure Similarity

Tao Cui, Weihui Li, Lei Liu, Qiaoyun Huang, Zheng-Guo He, Byung-Jun Yoon
2016 PLoS ONE  
Analysis of the resulting network indicated that secreted proteins of the STPK, ESX-1, and PE/PPE family in M. tuberculosis targeted human proteins involved in immune response and phagocytosis.  ...  New interactions were then predicted by searching for PSS with complex structures in the SSI template library.  ...  Wrote the paper: TC ZGH.  ... 
doi:10.1371/journal.pone.0147612 pmid:26799490 pmcid:PMC4723085 fatcat:prgcytaxijcmbbmudmw66ui53a

SIDEKICK: Genomic data driven analysis and decision-making framework

Mark S Doderer, Kihoon Yoon, Kay A Robbins
2010 BMC Bioinformatics  
Sidekick enables the process of combining heterogeneous data, finding and maintaining the most up-to-date data, evaluating data sources, quantifying confidence in results based on evidence, and managing  ...  Sidekick enables scientists without training in computation and data management to pursue answers to research questions like "What are the mechanisms for disease X" or "Does the set of genes associated  ...  Sidekick uses web services and data from a number of sources including NCBI, NCIBI, and EBI.  ... 
doi:10.1186/1471-2105-11-611 pmid:21192813 pmcid:PMC3022632 fatcat:2mjyiam7zveofdd6py3fwprmza

Proteome-wide Prediction of Signal Flow Direction in Protein Interaction Networks Based on Interacting Domains

Wei Liu, Dong Li, Jian Wang, Hongwei Xie, Yunping Zhu, Fuchu He
2009 Molecular & Cellular Proteomics  
Signal flow direction is one of the most important features of the protein-protein interactions in signaling networks.  ...  However, almost all the outcomes of current high-throughout techniques for protein-protein interactions mapping are usually supposed to be non-directional.  ...  Jiangqi Li, Lei Dou, and Songfeng Wu for their excellent advice and assistance as well as all the members in the bioinformatics lab of Beijing Proteome Research Center for helpful discussions.  ... 
doi:10.1074/mcp.m800354-mcp200 pmid:19502588 pmcid:PMC2742434 fatcat:5t2aayje4zacrhutonp56udrti

Supervised maximum-likelihood weighting of composite protein networks for complex prediction

Chern Han Yong, Guimei Liu, Hon Nian Chua, Limsoon Wong
2012 BMC Systems Biology  
Protein complexes participate in many important cellular functions, so finding the set of existent complexes is essential for understanding the organization and regulation of processes in the cell.  ...  With the availability of large amounts of high-throughput protein-protein interaction (PPI) data, many algorithms have been proposed to discover protein complexes from PPI networks.  ...  For SWC and BOOST, learning is performed using these labels, and the edges of the entire network are then weighted using the learned models.  ... 
doi:10.1186/1752-0509-6-s2-s13 pmid:23281936 pmcid:PMC3521185 fatcat:zyqvljwfxve35f6vg2r4jzbegi

Scientists' sense making when hypothesizing about disease mechanisms from expression data and their needs for visualization support

Barbara Mirel, Carsten Görg
2014 BMC Bioinformatics  
From findings, we abstracted a preliminary sense making model. Here we describe its stages and suggest guidelines for developing visualization tools that we derived from this case.  ...  In it, scientists use interactive data visualizations and read deeply in the research literature.  ...  We also thank Martin Krzywinski for helping to design the representation for the model in the supplemental Material and Paul Trombley for helping to render the model in the body of the text.  ... 
doi:10.1186/1471-2105-15-117 pmid:24766796 pmcid:PMC4021544 fatcat:xzbczkxh4ngp3jquj2gpt5kn54

Loss of CHGA protein as a potential biomarker for colon cancer diagnosis: a study on biomarker discovery by machine learning and confirmation by immunohistochemistry in colorectal cancer tissue microarrays [article]

Xueli Zhang, Hong Zhang, Chuanwen Fan, Bairong Shen, Xiao-Feng Sun
2022 medRxiv   pre-print
based on our colorectal biomarker database (CBD), finding differential expressed genes (GEGs) and non-DEGs from RNA sequencing (RNA-seq) data, and further predicted new biomarkers on protein-protein interaction  ...  Precise early diagnosis for colorectal cancer has been a great challenge in order to win chances for the best choices of cancer therapies.Patients and methodsWe have started with searching protein biomarkers  ...  The String database contains highly credible human protein-protein interaction (PPI) networks collected from different resources, which could be the effective data source for protein-related network topology  ... 
doi:10.1101/2022.04.04.22271362 fatcat:jzf55hcdjzbqbfutvlc6lqafqa

Loss of CHGA Protein as a Potential Biomarker for Colon Cancer Diagnosis: A Study on Biomarker Discovery by Machine Learning and Confirmation by Immunohistochemistry in Colorectal Cancer Tissue Microarrays

Xueli Zhang, Hong Zhang, Chuanwen Fan, Camilla Hildesjö, Bairong Shen, Xiao-Feng Sun
2022 Cancers  
further predicted new biomarkers of proteinprotein interaction (PPI) networks by machine learning (ML) methods.  ...  These predicted biomarkers showed close relationships with reported biomarkers of the PPI network and shared some pathways.  ...  The authors would like to express their gratitude to Siyu Qiao and Yu Shao for their help in network topology knowledge and Guang Hu, Dirk Repsilber, Xuye Yuan, and Shunming Liu in Machine Learning.  ... 
doi:10.3390/cancers14112664 pmid:35681650 pmcid:PMC9179857 fatcat:ltm6o7l6qzbrvpkujk7ual33xy

Predicted protein-protein interactions in the moss Physcomitrella patens: a new bioinformatic resource

Scott Schuette, Brian Piatkowski, Aaron Corley, Daniel Lang, Matt Geisler
2015 BMC Bioinformatics  
This work helps demonstrate the utility of "guilt-by-association" models for predicting protein interactions, providing provisional roadmaps that can be explored using experimental approaches.  ...  insight into network evolution of land plants.  ...  Acknowledgements The authors extend thanks to Elisabeth Fitzek for valuable comments on and assistance with assembling earlier versions of this manuscript.  ... 
doi:10.1186/s12859-015-0524-1 pmid:25885037 pmcid:PMC4384322 fatcat:fj6jumym4fgidktqruc24asdxe

Protein Interaction Networks: Protein Domain Interaction and Protein Function Prediction [chapter]

Yanjun Qi, William Stafford Noble
2011 Handbook of Statistical Bioinformatics  
The second part of this chapter reviews recent computational approaches to predict protein functions from PPI networks.  ...  These inferences are based on the premise that the function of a protein may be discovered by studying its interaction with one or more proteins of known functions.  ...  Similarly, Nabieva et al. in [59] developed a network flow algorithm that exploits the underlying structure of protein interaction maps in Fig. 17 A schematic illustration of the function prediction  ... 
doi:10.1007/978-3-642-16345-6_21 fatcat:whl2kgd3rbfcjm3ljkd56tj7vq

Identifying Protein Biomarkers in Blood for Alzheimer's Disease

Tianyi Zhao, Yang Hu, Tianyi Zang, Yadong Wang
2020 Frontiers in Cell and Developmental Biology  
We combined Elastic Network (EN) with Minimum angle regression (MAR) to find the optimal solution. Finally, we used case studies and Summary data Mendelian Random (SMR) to verify our method.  ...  At present, the main diagnostic methods for Alzheimer's disease (AD) are positron emission tomography (PET) scanning of the brain and analysis of cerebrospinal fluid (CSF) sample, but these methods are  ...  Although PPI network has excellent performance, it mainly has two drawbacks. Firstly, most studies of protein networks are based on static network models.  ... 
doi:10.3389/fcell.2020.00472 pmid:32626709 pmcid:PMC7314983 fatcat:bpmb5j7owbhlxiholvrmxvm4ci

Roles of mTOR in thoracic aortopathy understood by complex intracellular signaling interactions

Ana C. Estrada, Linda Irons, Bruno V. Rego, Guangxin Li, George Tellides, Jay D. Humphrey, James R. Faeder
2021 PLoS Computational Biology  
We present a new quantitative network model that includes many of the key smooth muscle cell signaling pathways and validate the model using a detailed data set that focuses on hyperactivation of the mechanistic  ...  We show that the model can be parameterized to capture the primary experimental findings both qualitatively and quantitatively.  ...  Quantitative comparison between network model results (bars) and experimental data (shown as point estimates (filled black circles) and 95% credible intervals (error bars) for the ratio of median expressions  ... 
doi:10.1371/journal.pcbi.1009683 pmid:34898595 pmcid:PMC8700007 fatcat:kp7lrmk2cjhxzctbu7xdkji5gi

Integrating automated workflows, human intelligence and collaboration

Barbara Mirel, Felix Eichinger, Viji Nair, Matthias Kretzler
2009 Summit on translational bioinformatics  
Little is known, however, about optimizing this flow of analysis for the flexible reasoning biomedical researchers need for hypothesizing about disease mechanisms.  ...  We present our workflow for the translational problem of classifying new sub-types of renal diseases.  ...  We thank Sebastian Martini and Christian Albiker for their assistance  ... 
pmid:21347175 pmcid:PMC3041562 fatcat:mfonrmwapngz5c6nquue2ofsja

Modeling Gene Expression Networks Using Fuzzy Logic

P. Du, J. Gong, E. SyrkinWurtele, J.A. Dickerson
2005 IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)  
Gene regulatory networks model regulation in living organisms. Fuzzy logic can effectively model gene regulation and interaction to accurately reflect the underlying biology.  ...  Fuzzy measures weight expert knowledge and help quantify uncertainty about the functions of genes using annotations and the gene ontology database to confirm some of the interactions.  ...  Interactions stand for causal flow. The sign of an interaction (+ or -) shows causal coregulation between entities. The fuzzy structure allows the Figure 1 .  ... 
doi:10.1109/tsmcb.2005.855590 pmid:16366260 fatcat:gwvopeqq6rfqxcb3c5kvyvaqi4
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