ConSIG: consistent discovery of molecular signature from OMIC data

Abstract

The discovery of proper molecular signature from OMIC data is indispensable for determining biological state, physiological condition, disease etiology, and therapeutic response. However, the identified signature is reported to be highly inconsistent, and there is little overlap among the signatures identified from different biological datasets. Such inconsistency raises doubts about the reliability of reported signatures and significantly hampers its biological and clinical applications. Herein, an online tool, ConSIG, was constructed to realize consistent discovery of gene/protein signature from any uploaded transcriptomic/proteomic data. This tool is unique in a) integrating a novel strategy capable of significantly enhancing the consistency of signature discovery, b) determining the optimal signature by collective assessment, and c) confirming the biological relevance by enriching the disease/gene ontology. With the increasingly accumulated concerns about signature consistency and biological relevance, this online tool is expected to be used as an essential complement to other existing tools for OMIC-based signature discovery. ConSIG is freely accessible to all users without login requirement at https://idrblab.org/consig/

Multi-omics characterization of RNA binding proteins reveals disease comorbidities and potential drugs in COVID-19

“…Differential expression analysis was performed for patients with different infection stages. Several methods employed in differential expression analysis to identify important signatures in in disease progression [ 19 , 20 ]. First, the transcriptome profiles were log2 transformation.…”

Application of Machine Learning in Spatial Proteomics

“…Thus, the original feature set can be optimized by dimensionality reduction methods. Feature selection is a method to reduce the feature dimensionality by selecting the best feature subset, and there are many kinds of feature selection algorithms. − Among them, the support-vector-machine-based recursive-feature elimination (SVM-RFE) method has been applied in imaging-based spatial proteomic data analysis . The SVM-RFE algorithm iteratively removes features according to the feature weights from a SVM classifier until the model achieves the highest performance.…”

Strategy for Identifying a Robust Metabolomic Signature Reveals the Altered Lipid Metabolism in Pituitary Adenoma

“…Support vector machine-recursive feature elimination (SVM-RFE) has been introduced as a valuable approach for biomarker identification in metabolomic data, specifically designed to tackle issues such as noisy training examples, a limited number of biological replicates, and nonlinearity among metabolites . Moreover, increasing evidence has demonstrated that there is very little consistency in the identified biomarkers among published metabolomic studies. − This inconsistency among metabolomic signatures from different studies was attributed to several factors, including (i) the inconsistency of samples, , (ii) the differences of analytical platform, (iii) the insufficiency of sample sizes, and (iv) the subtle variation of the statistical methods employed to identify differential features. , Therefore, it is essential to develop a novel strategy to discover the highly robust metabolomic signatures of PA and to facilitate the understanding of molecular mechanisms underlying PA pathogenesis based on the robust metabolomic signatures. ,, …”