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Diagnostic biases in translational bioinformatics.

Han H - BMC Med Genomics (2015)

Bottom Line: With the surge of translational medicine and computational omics research, complex disease diagnosis is more and more relying on massive omics data-driven molecular signature detection.Our work identifies and solves an important but less addressed problem in translational research.It also has a positive impact on machine learning for adding new results to kernel-based learning for omics data.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer and Information Science, Fordham University, New York, 10023, NY, USA. xhan9@fordham.edu.

ABSTRACT

Background: With the surge of translational medicine and computational omics research, complex disease diagnosis is more and more relying on massive omics data-driven molecular signature detection. However, how to detect and prevent possible diagnostic biases in translational bioinformatics remains an unsolved problem despite its importance in the coming era of personalized medicine.

Methods: In this study, we comprehensively investigate the diagnostic bias problem by analyzing benchmark gene array, protein array, RNA-Seq and miRNA-Seq data under the framework of support vector machines for different model selection methods. We further categorize the diagnostic biases into different types by conducting rigorous kernel matrix analysis and provide effective machine learning methods to conquer the diagnostic biases.

Results: In this study, we comprehensively investigate the diagnostic bias problem by analyzing benchmark gene array, protein array, RNA-Seq and miRNA-Seq data under the framework of support vector machines. We have found that the diagnostic biases happen for data with different distributions and SVM with different kernels. Moreover, we identify total three types of diagnostic biases: overfitting bias, label skewness bias, and underfitting bias in SVM diagnostics, and present corresponding reasons through rigorous analysis. Compared with the overfitting and underfitting biases, the label skewness bias is more challenging to detect and conquer because it can be easily confused as a normal diagnostic case from its deceptive accuracy. To tackle this problem, we propose a derivative component analysis based support vector machines to conquer the label skewness bias by achieving the rivaling clinical diagnostic results.

Conclusions: Our studies demonstrate that the diagnostic biases are mainly caused by the three major factors, i.e. kernel selection, signal amplification mechanism in high-throughput profiling, and training data label distribution. Moreover, the proposed DCA-SVM diagnosis provides a generic solution for the label skewness bias overcome due to the powerful feature extraction capability from derivative component analysis. Our work identifies and solves an important but less addressed problem in translational research. It also has a positive impact on machine learning for adding new results to kernel-based learning for omics data.

No MeSH data available.


Related in: MedlinePlus

The kernel matrices of the overfitting bias. The first row illustrates the box-plots of all pairwise sample distance squares in each data. The second row lists the kernel matrices of the three data sets under the ‘rbf’ kernel (σ=1), where each data is viewed as the population of training data, are identity matrices
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Fig1: The kernel matrices of the overfitting bias. The first row illustrates the box-plots of all pairwise sample distance squares in each data. The second row lists the kernel matrices of the three data sets under the ‘rbf’ kernel (σ=1), where each data is viewed as the population of training data, are identity matrices

Mentions: Figure 1 illustrates the box-plots of all pairwise sample distance squares in each data set in the first row of plots and kernel matrices of the three data sets under the ‘rbf’ kernel in the second row of plots by viewing each data set as the population of training data. It is interesting to see that the the minimum are greater than 102, which means the distance between any two samples in the feature space will be approximately zero: k(xi,xj)≤ exp(−102/2)∼10−22. As a result, the corresponding kernel matrix will be an identity matrix as illustrated by the corresponding plot in the second row.Fig. 1


Diagnostic biases in translational bioinformatics.

Han H - BMC Med Genomics (2015)

The kernel matrices of the overfitting bias. The first row illustrates the box-plots of all pairwise sample distance squares in each data. The second row lists the kernel matrices of the three data sets under the ‘rbf’ kernel (σ=1), where each data is viewed as the population of training data, are identity matrices
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4522082&req=5

Fig1: The kernel matrices of the overfitting bias. The first row illustrates the box-plots of all pairwise sample distance squares in each data. The second row lists the kernel matrices of the three data sets under the ‘rbf’ kernel (σ=1), where each data is viewed as the population of training data, are identity matrices
Mentions: Figure 1 illustrates the box-plots of all pairwise sample distance squares in each data set in the first row of plots and kernel matrices of the three data sets under the ‘rbf’ kernel in the second row of plots by viewing each data set as the population of training data. It is interesting to see that the the minimum are greater than 102, which means the distance between any two samples in the feature space will be approximately zero: k(xi,xj)≤ exp(−102/2)∼10−22. As a result, the corresponding kernel matrix will be an identity matrix as illustrated by the corresponding plot in the second row.Fig. 1

Bottom Line: With the surge of translational medicine and computational omics research, complex disease diagnosis is more and more relying on massive omics data-driven molecular signature detection.Our work identifies and solves an important but less addressed problem in translational research.It also has a positive impact on machine learning for adding new results to kernel-based learning for omics data.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer and Information Science, Fordham University, New York, 10023, NY, USA. xhan9@fordham.edu.

ABSTRACT

Background: With the surge of translational medicine and computational omics research, complex disease diagnosis is more and more relying on massive omics data-driven molecular signature detection. However, how to detect and prevent possible diagnostic biases in translational bioinformatics remains an unsolved problem despite its importance in the coming era of personalized medicine.

Methods: In this study, we comprehensively investigate the diagnostic bias problem by analyzing benchmark gene array, protein array, RNA-Seq and miRNA-Seq data under the framework of support vector machines for different model selection methods. We further categorize the diagnostic biases into different types by conducting rigorous kernel matrix analysis and provide effective machine learning methods to conquer the diagnostic biases.

Results: In this study, we comprehensively investigate the diagnostic bias problem by analyzing benchmark gene array, protein array, RNA-Seq and miRNA-Seq data under the framework of support vector machines. We have found that the diagnostic biases happen for data with different distributions and SVM with different kernels. Moreover, we identify total three types of diagnostic biases: overfitting bias, label skewness bias, and underfitting bias in SVM diagnostics, and present corresponding reasons through rigorous analysis. Compared with the overfitting and underfitting biases, the label skewness bias is more challenging to detect and conquer because it can be easily confused as a normal diagnostic case from its deceptive accuracy. To tackle this problem, we propose a derivative component analysis based support vector machines to conquer the label skewness bias by achieving the rivaling clinical diagnostic results.

Conclusions: Our studies demonstrate that the diagnostic biases are mainly caused by the three major factors, i.e. kernel selection, signal amplification mechanism in high-throughput profiling, and training data label distribution. Moreover, the proposed DCA-SVM diagnosis provides a generic solution for the label skewness bias overcome due to the powerful feature extraction capability from derivative component analysis. Our work identifies and solves an important but less addressed problem in translational research. It also has a positive impact on machine learning for adding new results to kernel-based learning for omics data.

No MeSH data available.


Related in: MedlinePlus