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Data analysis methods for defining biomarkers from omics data

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Abstract

Omics mainly includes genomics, epigenomics, transcriptomics, proteomics and metabolomics. The rapid development of omics technology has opened up new ways to study disease diagnosis and prognosis and to define prospective information of complex diseases. Since omics data are usually large and complex, the method used to analyze the data and to define important information is crucial in omics study. In this review, we focus on advances in biomarker discovery methods based on omics data in the last decade, and categorize them as individual feature analysis, combinatorial feature analysis and network analysis. We also discuss the challenges and perspectives in this field.

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Abbreviations

ANN:

Artificial neural network

ANOVA:

Analysis of variance

ATSD-DN:

Analyzing time-series data based on dynamic networks

AUC:

Area under the receiver operating characteristic curve

AUCTSP:

AUC-based TSP

BPCA:

Bayesian principal component analysis

CFC-CM:

Construct feature combinations and a classification model

Chi-TSG:

Chi-square statistic-based top-scoring genes

CRV:

Carcinogenesis relevance value

DCEN:

Differential co-expression network

DFS:

Deep feature selection

DiSNEP:

Disease-specific network enhancement prioritization

DN:

Differential network

DNB:

Dynamic network biomarker

DNB-HC:

Defining network biomarkers based on horizontal comparison

DNN:

Deep neural network

EMDN:

Epigenetic module based on differential networks

ERGS:

Effective range-based gene selection

GA:

Genetic algorithm

GEDFN:

Graph-embedded deep feedforward networks

GGM:

Gaussian graphical modeling

GNFS:

Gene-network-based feature set

GO:

Gene ontology

GSNFS:

Gene subnetwork-based feature selection

HCC:

Hepatocellular carcinoma

HFS-SLPEE:

Hierarchical feature selection and second learning probability error ensemble model

IFSER:

Improved feature selection based on effective range

IG:

Information gain

ImRml:

Information maximization and redundancy minimization through feature interaction

INDEED:

Integrated differential expression and differential network analysis

ISFLA:

Improved shuffled frog leaping algorithm

kNN:

k-Nearest neighbors

kNN-TN:

kNN truncation

k-TSP:

k Top-scoring pairs

LASSO:

Least absolute shrinkage and selection operator

LC-k-TSP:

Linear combination of k top-scoring pairs

l-DNB:

Landscape dynamic network biomarker

LOD:

Limit of detection

LOPC:

Low-order partial correlation

MI:

Mutual information

MIC:

Maximal information coefficient

MIMAGA:

Hybrid feature selection algorithm based on mutual information maximization and the adaptive genetic algorithm

missForest:

Nonparametric missing value imputation using random forest

MPeMR:

Minimum projection error minimum redundancy

N-CSI:

Network-based metabolic feature selection method based on combinational significance index

ND:

Network diffusion

NFSM:

Network-based feature selection method

NS-kNN:

No-skip kNN

PB-DSN:

Potential biomarkers based on differential subnetworks

PCA:

Principal component analysis

PermFIT:

Permutation-based feature importance test

PLS-DA:

Partial-least-squares discriminant analysis

PNN:

Probabilistic neural network

PPI:

Protein–protein interaction

QC:

Quality control

RNGCS:

Reduced number of genes for combination selection

SDAE:

Stacked denoising autoencoder

SE1DCNN:

Sample expansion-based one-dimensional convolutional neural network

SESAE:

Sample expansion-based stacked autoencoder

SFLA:

Shuffled frog leaping algorithm

SR:

SpectralRank

SU-HAS:

Symmetrical uncertainty filter and harmony search algorithm wrapper

SVD:

Singular value decomposition

SVM:

Support vector machine

SVM-RFE:

Support vector machine-recursive feature elimination

T2DM:

Type 2 diabetes mellitus

TSN:

Top-scoring ‘N’

TSP:

Top-scoring pair

TST:

Top-scoring triplet

UGFS:

Unsupervised graph-based feature selection

VH-k-TSP:

Vertical and horizontal k-TSP

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Funding

This study is supported by the Fundamental Research Funds for the Central Universities (DUT21YG115) and the foundation (No. 21876169) from the National Natural Science Foundation of China.

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Authors and Affiliations

  1. School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China

    Chao Li, Zhenbo Gao, Benzhe Su & Xiaohui Lin

  2. CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, Liaoning, China

    Chao Li & Guowang Xu

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  1. Chao Li
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Published in the topical collection celebrating ABCs 20th Anniversary. Chao Li and Zhenbo Gao contributed equally to this paper.

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Li, C., Gao, Z., Su, B. et al. Data analysis methods for defining biomarkers from omics data. Anal Bioanal Chem 414, 235–250 (2022). https://doi.org/10.1007/s00216-021-03813-7

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