Integration of prior biological knowledge and epigenetic information enhances the prediction accuracy of the Bayesian Wnt pathway

2014 ◽  
Vol 6 (11) ◽  
pp. 1034-1048 ◽  
Author(s):  
Shriprakash Sinha
Keyword(s):  
Prediction Accuracy ◽  
Wnt Pathway ◽  
Biological Knowledge ◽  
Epigenetic Information ◽  
Prior Biological Knowledge

scholarly journals Prior Biological Knowledge Improves Genomic Prediction of Growth-Related Traits in Arabidopsis thaliana

2021 ◽  
Vol 11 ◽  
Author(s):  
Muhammad Farooq ◽  
Aalt D. J. van Dijk ◽  
Harm Nijveen ◽  
Mark G. M. Aarts ◽  
Willem Kruijer ◽  
...  
Keyword(s):  
Arabidopsis Thaliana ◽  
Gene Ontology ◽  
Genomic Prediction ◽  
Complex Traits ◽  
Prediction Accuracy ◽  
Plant Performance ◽  
Biological Knowledge ◽  
Genomic Feature ◽  
Crop Breeding ◽  
Prior Biological Knowledge

Prediction of growth-related complex traits is highly important for crop breeding. Photosynthesis efficiency and biomass are direct indicators of overall plant performance and therefore even minor improvements in these traits can result in significant breeding gains. Crop breeding for complex traits has been revolutionized by technological developments in genomics and phenomics. Capitalizing on the growing availability of genomics data, genome-wide marker-based prediction models allow for efficient selection of the best parents for the next generation without the need for phenotypic information. Until now such models mostly predict the phenotype directly from the genotype and fail to make use of relevant biological knowledge. It is an open question to what extent the use of such biological knowledge is beneficial for improving genomic prediction accuracy and reliability. In this study, we explored the use of publicly available biological information for genomic prediction of photosynthetic light use efficiency (ΦPSII) and projected leaf area (PLA) in Arabidopsis thaliana. To explore the use of various types of knowledge, we mapped genomic polymorphisms to Gene Ontology (GO) terms and transcriptomics-based gene clusters, and applied these in a Genomic Feature Best Linear Unbiased Predictor (GFBLUP) model, which is an extension to the traditional Genomic BLUP (GBLUP) benchmark. Our results suggest that incorporation of prior biological knowledge can improve genomic prediction accuracy for both ΦPSII and PLA. The improvement achieved depends on the trait, type of knowledge and trait heritability. Moreover, transcriptomics offers complementary evidence to the Gene Ontology for improvement when used to define functional groups of genes. In conclusion, prior knowledge about trait-specific groups of genes can be directly translated into improved genomic prediction.

scholarly journals driveR: a novel method for prioritizing cancer driver genes using somatic genomics data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ege Ülgen ◽  
O. Uğur Sezerman
Keyword(s):  
Biological Knowledge ◽  
Driver Gene ◽  
Driver Genes ◽  
Cancer Driver ◽  
Prior Biological Knowledge ◽  
Wilcoxon Rank Sum Test ◽  
Cancer Genomes ◽  
Novel Method ◽  
Cancer Driver Genes ◽  
Batch Analysis

Abstract Background Cancer develops due to “driver” alterations. Numerous approaches exist for predicting cancer drivers from cohort-scale genomics data. However, methods for personalized analysis of driver genes are underdeveloped. In this study, we developed a novel personalized/batch analysis approach for driver gene prioritization utilizing somatic genomics data, called driveR. Results Combining genomics information and prior biological knowledge, driveR accurately prioritizes cancer driver genes via a multi-task learning model. Testing on 28 different datasets, this study demonstrates that driveR performs adequately, achieving a median AUC of 0.684 (range 0.651–0.861) on the 28 batch analysis test datasets, and a median AUC of 0.773 (range 0–1) on the 5157 personalized analysis test samples. Moreover, it outperforms existing approaches, achieving a significantly higher median AUC than all of MutSigCV (Wilcoxon rank-sum test p < 0.001), DriverNet (p < 0.001), OncodriveFML (p < 0.001) and MutPanning (p < 0.001) on batch analysis test datasets, and a significantly higher median AUC than DawnRank (p < 0.001) and PRODIGY (p < 0.001) on personalized analysis datasets. Conclusions This study demonstrates that the proposed method is an accurate and easy-to-utilize approach for prioritizing driver genes in cancer genomes in personalized or batch analyses. driveR is available on CRAN: https://cran.r-project.org/package=driveR.

scholarly journals Characterization of the Effectiveness of Reporting Lists of Small Feature Sets Relative to the Accuracy of the Prior Biological Knowledge

2010 ◽  
Vol 9 ◽  
pp. CIN.S4020 ◽  
Author(s):  
Chen Zhao ◽  
Michael L. Bittner ◽  
Robert S. Chapkin ◽  
Edward R. Dougherty
Keyword(s):  
Small Sample ◽  
Training Data ◽  
Small Samples ◽  
Classification Error ◽  
Biological Knowledge ◽  
Feature Sets ◽  
Good Feature ◽  
Prior Biological Knowledge ◽  
Selection Algorithms ◽  
Power Curves

When confronted with a small sample, feature-selection algorithms often fail to find good feature sets, a problem exacerbated for high-dimensional data and large feature sets. The problem is compounded by the fact that, if one obtains a feature set with a low error estimate, the estimate is unreliable because training-data-based error estimators typically perform poorly on small samples, exhibiting optimistic bias or high variance. One way around the problem is limit the number of features being considered, restrict features sets to sizes such that all feature sets can be examined by exhaustive search, and report a list of the best performing feature sets. If the list is short, then it greatly restricts the possible feature sets to be considered as candidates; however, one can expect the lowest error estimates obtained to be optimistically biased so that there may not be a close-to-optimal feature set on the list. This paper provides a power analysis of this methodology; in particular, it examines the kind of results one should expect to obtain relative to the length of the list and the number of discriminating features among those considered. Two measures are employed. The first is the probability that there is at least one feature set on the list whose true classification error is within some given tolerance of the best feature set and the second is the expected number of feature sets on the list whose true errors are within the given tolerance of the best feature set. These values are plotted as functions of the list length to generate power curves. The results show that, if the number of discriminating features is not too small—that is, the prior biological knowledge is not too poor—then one should expect, with high probability, to find good feature sets. Availability: companion website at http://gsp.tamu.edu/Publications/supplementary/zhao09a/

scholarly journals PALMER: improving pathway annotation based on the biomedical literature mining with a constrained latent block model

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Jin Hyun Nam ◽  
Daniel Couch ◽  
Willian A. da Silveira ◽  
Zhenning Yu ◽  
Dongjun Chung
Keyword(s):  
Clustering Algorithms ◽  
Biomedical Literature ◽  
Literature Mining ◽  
Biological Knowledge ◽  
Block Model ◽  
Prior Biological Knowledge ◽  
Gene Associations ◽  
Biological Interpretation ◽  
Biomedical Literature Mining ◽  
Block Models

Abstract Background In systems biology, it is of great interest to identify previously unreported associations between genes. Recently, biomedical literature has been considered as a valuable resource for this purpose. While classical clustering algorithms have popularly been used to investigate associations among genes, they are not tuned for the literature mining data and are also based on strong assumptions, which are often violated in this type of data. For example, these approaches often assume homogeneity and independence among observations. However, these assumptions are often violated due to both redundancies in functional descriptions and biological functions shared among genes. Latent block models can be alternatives in this case but they also often show suboptimal performances, especially when signals are weak. In addition, they do not allow to utilize valuable prior biological knowledge, such as those available in existing databases. Results In order to address these limitations, here we propose PALMER, a constrained latent block model that allows to identify indirect relationships among genes based on the biomedical literature mining data. By automatically associating relevant Gene Ontology terms, PALMER facilitates biological interpretation of novel findings without laborious downstream analyses. PALMER also allows researchers to utilize prior biological knowledge about known gene-pathway relationships to guide identification of gene–gene associations. We evaluated PALMER with simulation studies and applications to studies of pathway-modulating genes relevant to cancer signaling pathways, while utilizing biological pathway annotations available in the KEGG database as prior knowledge. Conclusions We showed that PALMER outperforms traditional latent block models and it provides reliable identification of novel gene–gene associations by utilizing prior biological knowledge, especially when signals are weak in the biomedical literature mining dataset. We believe that PALMER and its relevant user-friendly software will be powerful tools that can be used to improve existing pathway annotations and identify novel pathway-modulating genes.

Incorporating Biological Information as a Prior in an Empirical Bayes Approach to Analyzing Microarray Data

2005 ◽  
Vol 4 (1) ◽  
Author(s):  
Wei Pan
Keyword(s):  
Mixture Model ◽  
Microarray Data ◽  
Statistical Power ◽  
Empirical Bayes ◽  
Biological Information ◽  
Biological Knowledge ◽  
False Discovery Rates ◽  
Proteomic Data ◽  
Prior Biological Knowledge ◽  
A Genome

Currently the practice of using existing biological knowledge in analyzing high throughput genomic and proteomic data is mainly for the purpose of validations. Here we take a different approach of incorporating biological knowledge into statistical analysis to improve statistical power and efficiency. Specifically, we consider how to fuse biological information into a mixture model to analyze microarray data. In contrast to a standard mixture model where it is assumed that all the genes come from the same (marginal) distribution, including an equal prior probability of having an event, such as having differential expression or being bound by a transcription factor (TF), our proposed mixture model allows the genes in different groups to have different distributions while the grouping of the genes reflects biological information. Using a list of about 800 putative cell cycle-regulated genes as prior biological knowledge, we analyze a genome-wide location data to detect binding sites of TF Fkh1. We find that our proposal improves over the standard approach, resulting in reduced false discovery rates (FDR), and hence it is a useful alternative to the current practice.

scholarly journals Meta-analysis and profiling of cardiac expression modules

2008 ◽  
Vol 35 (3) ◽  
pp. 305-315 ◽  
Author(s):  
Uri David Akavia ◽  
Dafna Benayahu
Keyword(s):  
Heart Failure ◽  
Large Scale ◽  
Meta Analysis ◽  
Gene Expression Omnibus ◽  
Biological Knowledge ◽  
Map Algorithm ◽  
Large Scale Analysis ◽  
Expression Of Genes ◽  
Prior Biological Knowledge ◽  
Clinical Conditions

Heart failure is a complex, complicated disease that is not yet fully understood. We used the Module Map algorithm to uncover groups of genes that have a similar pattern of expression under various conditions of heart stress. These groups of genes are called modules and may serve as computational predictions of biological pathways for the various clinical situations. The Module Map algorithm allows a large-scale analysis of genes expressed. We applied this algorithm to 700 different mouse experiments downloaded from the Gene Expression Omnibus database, which identified 884 modules. The analysis reconstructed partially known principles that play a role in governing the response of heart to stress, thus demonstrating the strength of the method. We have shown a role of genes related to the immune system in conditions of heart remodeling and failure. We have also shown changes in the expression of genes involved with energy metabolism and changes in the expression of contractile proteins of the heart following myocardial infarction. When focusing on another module we noted a new correlation between genes related to osteogenesis and heart failure, including Runx2 and Ahsg, whose role in heart failure was unknown so far. Despite a lack of prior biological knowledge, the Module Map algorithm has reconstructed known pathways, which demonstrates the strength of this new method for analyzing gene profiles related to clinical phenomenon. The method and the analysis presented are a new avenue to uncover the correlation of clinical conditions to the molecular level.

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