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Constructing higher-order miRNA-mRNA interaction networks in prostate cancer via hypergraph-based learning

Soo-Jin Kim, Jung-Woo Ha, Byoung-Tak Zhang
2013 BMC Systems Biology  
It adopts an evolutionary method based on information-theoretic criteria.  ...  Therefore, the hypergraph-based model can assist hypothesis formulation for the molecular pathogenesis of cancer.  ...  clusters and regulators based on probabilistically optimized trees.  ... 
doi:10.1186/1752-0509-7-47 pmid:23782521 pmcid:PMC3733828 fatcat:g2ofko2dfjduji2suwkkku4trm

Dissecting trait heterogeneity: a comparison of three clustering methods applied to genotypic data

Tricia A Thornton-Wells, Jason H Moore, Jonathan L Haines
2006 BMC Bioinformatics  
Also tested was the ability of these methods to detect trait heterogeneity in the presence of locus heterogeneity and/or gene-gene interaction, which are two other complicating factors in discovering genetic  ...  The performance of three such methods--Bayesian Classification, Hypergraph-Based Clustering, and Fuzzy k-Modes Clustering--appropriate for categorical data were compared.  ...  The authors would also like to thank Marylyn Ritchie, Lance Hahn and Bill White for their thoughtful input on study design.  ... 
doi:10.1186/1471-2105-7-204 pmid:16611359 pmcid:PMC1525209 fatcat:5psx4fmwvvgsje5eddbost4weq

From Information Networks to Bisociative Information Networks [chapter]

Tobias Kötter, Michael R. Berthold
2012 Lecture Notes in Computer Science  
Finally based on this data structure three different patterns are described that fulfill the requirements of a bisociation by connecting concepts from seemingly unrelated domains.  ...  Information networks, due to their flexible data structure, lend themselves perfectly to the integration of these heterogeneous data sources.  ...  The resulting network called Genenetwork can be used to detect genes that are related to a disease based on genetic mutation. Li et al.  ... 
doi:10.1007/978-3-642-31830-6_3 fatcat:tqjmyg6pevhcncgfaspvymzaby

INGOT: Towards network-driven in silico combination therapy

Sourav S Bhowmick, Huey-Eng Chua, Jie Zheng
2014 2014 International Conference on Big Data and Smart Computing (BIGCOMP)  
Specifically, in ingot, a disease-related probabilistic signaling network (psn) is constructed by integrating publicly-available disease-specific signaling networks with expression data.  ...  Finally, optimal candidate drug combinations to modulate these targets are predicted by integrating and analyzing drug information (e.g., DrugBank) with the target nodes.  ...  For instance, targets that have higher prioritization ranks are preferentially selected as candidates (the ranks can be converted to selection probabilities by using a rank-based fitness function).  ... 
doi:10.1109/bigcomp.2014.6741401 dblp:conf/bigcomp/BhowmickCZ14 fatcat:my6ou66ghrb2tf4tjj3cf3qgaa

Breast cancer patient stratification using a molecular regularized consensus clustering method

Chao Wang, Raghu Machiraju, Kun Huang
2014 Methods  
We apply this new method by applying it on the TCGA breast cancer datasets and evaluate using both statistical criteria and clinical relevance on predicting prognosis.  ...  Specifically, we formulate the problem as a novel consensus clustering method called Molecular Regularized Consensus Patient Stratification (MRCPS) based on an optimization process with regularization.  ...  Shapiro for helps on this study.  ... 
doi:10.1016/j.ymeth.2014.03.005 pmid:24657666 pmcid:PMC4151565 fatcat:qgdissyws5fbvabqf66prrhqyy

Discovering MicroRNA-Regulatory Modules in Multi-Dimensional Cancer Genomic Data: A Survey of Computational Methods

Christopher J. Walsh, Pingzhao Hu, Jane Batt, Claudia C. Dos Santos
2016 Cancer Informatics  
MicroRNAs (miRs) are small single-stranded noncoding RNA that function in RNA silencing and post-transcriptional regulation of gene expression.  ...  An increasing number of studies have shown that miRs play an important role in tumorigenesis, and understanding the regulatory mechanism of miRs in this gene regulatory network will help elucidate the  ...  SEqUENCE bASED METhoDS foR miR TARGET PREDICTIoN (PREDICTING whEThER A GIvEN mRNA IS TARGETED bY A miR) METhoD/REfERENCE/SofTwARE DATA TYPES CoMMENTS For miR and gene expression profiles from heterogeneous  ... 
doi:10.4137/cin.s39369 pmid:27721651 pmcid:PMC5051584 fatcat:hcnnw6gs5bgkziaw37iubr4eei

Genetics, statistics and human disease: analytical retooling for complexity

Tricia A. Thornton-Wells, Jason H. Moore, Jonathan L. Haines
2004 Trends in Genetics  
'The difficulty lies, not in the new ideas, but in escaping the old ones.' John Maynard Keynes, English economist.  ...  Molecular biologists and geneticists alike now acknowledge that most common human diseases with a genetic component are likely to have complex etiologies.  ...  Cheverud and Routman [26] developed an alternative parameterization of gene-gene interactions based on its effects on genetic-variance components (additive, dominance and interaction); however, it is  ... 
doi:10.1016/j.tig.2004.09.007 pmid:15522460 fatcat:e4ta4tz7mfa4rjeffqxucbqtby

Network-based machine learning and graph theory algorithms for precision oncology

Wei Zhang, Jeremy Chien, Jeongsik Yong, Rui Kuang
2017 npj Precision Oncology  
drugs in drug-disease-gene networks.  ...  The review focuses on the algorithmic design and mathematical formulation of these methods to facilitate applications and implementations of network-based analysis in the practice of precision oncology  ...  ACKNOWLEDGEMENTS The results are based upon data generated by The Cancer Genome Atlas established by the NCI and NHGRI.  ... 
doi:10.1038/s41698-017-0029-7 pmid:29872707 pmcid:PMC5871915 fatcat:yqeb4ntx7rgy3g5yep53u57wgq

Report on the 2nd International Workshop on Data Integration in the Life Sciences

Amarnath Gupta, Bertram Ludäscher, Louiqa Raschid
2006 SIGMOD record  
The authors present an experiment where INDUS is used to learn probabilistic models to predict GO functional classifications; mappings such as EC2GO and MIPS2GO are used in this task.  ...  The AutoMed tool designed with a hypergraph data model for warehouse-based integration has been in existence for some time.  ... 
doi:10.1145/1147376.1147386 fatcat:bvagx3p33rbyzd7dfpqruikc6i

ILPMDA: Predicting miRNA–Disease Association Based on Improved Label Propagation

Yu-Tian Wang, Lei Li, Cun-Mei Ji, Chun-Hou Zheng, Jian-Cheng Ni
2021 Frontiers in Genetics  
To evaluate the prediction performance of ILPMDA, two types of cross-validation methods and case studies on three significant human diseases were implemented to determine the accuracy and effectiveness  ...  In this study, we presented an improved label propagation for miRNA–disease association prediction (ILPMDA) method to observe disease-related miRNAs.  ...  Here, multisimilarity-based combinative hypergraph learning for predicting miRNA-disease association (MSCHLMDA) applied the KNN and k-means algorithms to establish different hypergraphs, which were combined  ... 
doi:10.3389/fgene.2021.743665 pmid:34659364 pmcid:PMC8514753 fatcat:2nwq3xnembfqtes36gfffjpi3m

SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization

Lei Li, Zhen Gao, Yu-Tian Wang, Ming-Wen Zhang, Jian-Cheng Ni, Chun-Hou Zheng, Quan Zou
2021 PLoS Computational Biology  
In this study, we proposed a computational model based on Similarity Constrained Matrix Factorization for miRNA-Disease Association Prediction (SCMFMDA).  ...  SCMFMDA achieved AUCs of 0.9675 and 0.9447 based on global Leave-one-out cross validation and five-fold cross validation, respectively.  ...  Therefore, we could calculate disease functional similarity based on the functional information of gene.  ... 
doi:10.1371/journal.pcbi.1009165 pmid:34252084 fatcat:nqaod54lnvbcro6ibyi5jrw7rq

Learning from Label and Feature Heterogeneity

Pei Yang, Jingrui He, Hongxia Yang, Haoda Fu
2014 2014 IEEE International Conference on Data Mining  
that view-based classifiers generate similar predictions on the same examples.  ...  Furthermore, we analyze its generalization performance based on Rademacher complexity, which sheds light on the benefits of jointly modeling the label and feature heterogeneity.  ...  diseases.  ... 
doi:10.1109/icdm.2014.42 dblp:conf/icdm/YangHYF14 fatcat:tjrxassbfvcsboro7wjj5zc4je

Network modeling of single-cell omics data: challenges, opportunities, and progresses

Montgomery Blencowe, Douglas Arneson, Jessica Ding, Yen-Wei Chen, Zara Saleem, Xia Yang
2019 Emerging Topics in Life Sciences  
Finally, we outline the remaining gaps in single-cell gene network modeling and the outlooks of the field moving forward.  ...  Gene regulatory network modeling has been used as a powerful approach to elucidate the complex molecular interactions underlying biological processes and systems, yet its application in single-cell omics  ...  SCINGE also uses kernel-based Granger Causality regression on ordered single-cell data to predict regulator-target gene interactions and then ranks the predicted interactions by aggregating the regression  ... 
doi:10.1042/etls20180176 pmid:32270049 pmcid:PMC7141415 fatcat:x47pn56f6fbhxkzb3vcze3cuuq

Large-scale regulatory and signaling network assembly through linked open data

M Lefebvre, A Gaignard, M Folschette, J Bourdon, C Guziolowski
2021 Database: The Journal of Biological Databases and Curation  
We propose a framework based on Semantic Web technologies to automate the reconstruction of large-scale regulatory and signaling networks in the context of tumor cells modeling and drug screening.  ...  The proposed tool is pyBRAvo (python Biological netwoRk Assembly), and here we have applied it to a dataset of 910 gene expression measurements issued from liver cancer patients.  ...  This can largely enrich the construction of systems biology models based on disease descriptions for instance.  ... 
doi:10.1093/database/baaa113 pmid:33459761 pmcid:PMC7812716 fatcat:qtxpgtgljvb4hdmck6i3w4yzwy

2020 Index IEEE Journal of Biomedical and Health Informatics Vol. 24

2020 IEEE journal of biomedical and health informatics  
., A Globalized Model for Mapping Wearable Seismocardiogram Signals to Whole-Body Ballistocardiogram Signals Based on Deep Learning; JBHI May 2020 1296-1309 Herskovic, V., see Saint-Pierre, C., JBHI Jan  ...  Method Based on Matrix Completion Algorithm; JBHI Dec. 2020 3630-3641 Jiang, S., see Zhou, Z., JBHI Jan. 2020 194-204 Jiang, X., see Zhou, Z., JBHI April 2020 943-956 Jiang, Y., see Yu, R., JBHI Feb  ...  Kretowska, M., JBHI Jan. 2020 247-258 Predicting Human lncRNA-Disease Associations Based on Geometric Matrix Completion.  ... 
doi:10.1109/jbhi.2020.3048808 fatcat:iifrkwtzazdmboabdqii7x5ukm
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