Evaluation metrics

Lingpeng Kong; Yuanyuan Chen; Fengjiao Xu; Mingmin Xu; Zutan Li; Jingya Fang; Liangyun Zhang; Cong Pian

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LK Lingpeng Kong

YC Yuanyuan Chen

FX Fengjiao Xu

MX Mingmin Xu

ZL Zutan Li

JF Jingya Fang

LZ Liangyun Zhang

CP Cong Pian

This method is extracted from research article: BMC Bioinformatics, Jan 2021

Mining influential genes based on deep learning

DOI: 10.1186/s12859-021-03972-5

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Given a test set = (s_1…,Sm)containing samples, we use two different metrics for the evaluation of predicted expression. For each gene g_j, the definition of MAE is:

The following equation shows the definition of PCC:

where indicates the Pearson correlation coefficient for the j-th predicted gene and $μ_{j}, {\hat{μ}}_{j}$ are the mean of $g_{j}, {\hat{g}}_{j}$ respectively.

The Pearson correlation coefficient, an absolute measure of similarity between genes, does not in itself reflect how uncommon that similarity is. Hence, we apply a permutation test to aid in the interpretation of similarity. Briefly, in addition to computing thebetween $g_{j} and {\hat{g}}_{j}$ , we also compute thebetween the ${\hat{g}}_{j}$ and any gene other thanas a reference distribution of similarity values. After that, we compareto, and if the fraction of that is higher than is lower than 0.01, $g_{j} and {\hat{g}}_{j}$ are considered to be significantly correlated.

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