POSREG: proteomic signature discovered by simultaneously optimizing its reproducibility and generalizability

Abstract

Mass spectrometry-based proteomic technique has become indispensable in current exploration of complex and dynamic biological processes. Instrument development has largely ensured the effective production of proteomic data, which necessitates commensurate advances in statistical framework to discover the optimal proteomic signature. Current framework mainly emphasizes the generalizability of the identified signature in predicting the independent data but neglects the reproducibility among signatures identified from independently repeated trials on different sub-dataset. These problems seriously restricted the wide application of the proteomic technique in molecular biology and other related directions. Thus, it is crucial to enable the generalizable and reproducible discovery of the proteomic signature with the subsequent indication of phenotype association. However, no such tool has been developed and available yet. Herein, an online tool, POSREG, was therefore constructed to identify the optimal signature for a set of proteomic data. It works by (i) identifying the proteomic signature of good reproducibility and aggregating them to ensemble feature ranking by ensemble learning, (ii) assessing the generalizability of ensemble feature ranking to acquire the optimal signature and (iii) indicating the phenotype association of discovered signature. POSREG is unique in its capacity of discovering the proteomic signature by simultaneously optimizing its reproducibility and generalizability. It is now accessible free of charge without any registration or login requirement at https://idrblab.org/posreg/

Identification of Vesicle Transport Proteins via Hypergraph Regularized K-Local Hyperplane Distance Nearest Neighbour Model

“…Cross-validation sets aside a small portion of the dataset for validating the model, while the rest of the dataset is used for training the model ( Zhang D. et al, 2021 ; Lv et al, 2021 ; Yang et al, 2021 ; Zheng et al, 2021 ; Li F. et al, 2022 ; Li X. et al, 2022 ). The leave-one-out cross-validation (LOOCV) is a classic cross-validation method ( Qiu et al, 2021 ).…”

DrugMAP: molecular atlas and pharma-information of all drugs

“…Because of these emerging demands on such interaction-based big data, DrugMAP made the first endeavor to weave a comprehensive network containing >200 000 interactions among >30 000 drugs/drug candidates and >5000 molecules of pharmacological importance. Such a drug-centric ‘interacting network’ for each drug can be freely viewed online and fully downloaded by all users in the popular format of Cytoscape ( 115 ), which is expected to have great implications for drug repurposing ( 57 , 116 ), target discovery ( 117–119 ) and drug development ( 120–122 ). Moreover, an overall interacting network including all interactions is downloadable from DrugMAP to facilitate network analyses ( 123–126 ).…”

Analysis of transcriptomic responses to SARS-CoV-2 reveals plausible defective pathways responsible for increased susceptibility to infection and complications and helps to develop fast-track repositioning of drugs against COVID-19

“…Due to the inherent uncertainty of this type of problems [ 36 , 37 ], the stability of the small-scale signature can be established by performing data bagging [ 38 ]. One of the novel techniques recently published in this regard was introduced by Li et al and Yang et al The first one proposes a methodology that identifies the proteomic signature (in this case it can also be applied to a genetic signature) of good reproducibility and aggregating them to ensemble feature ranking by ensemble learning, assessing the generalizability of ensemble feature ranking to acquire the optimal signature and indicating the phenotype association of discovered signature [ 39 ]. The second one introduce a novel feature selection strategy integrating repeated random sampling with consensus scoring and evaluating the consistency of gene rank among different datasets was constructed [ 40 ].…”