Prediction of lncRNA-disease Associations Based on Robust Multi-label Learning

Abstract

Background: Long non-coding RNAs (lncRNAs) are nonprotein-coding transcripts of more than 200 nucleotides in length. In recent years, studies have shown that long non-coding RNAs (lncRNA) play a vital role in various biological processes, complex disease diagnosis, prognosis, and treatment. Objective: Analysis of known lncRNA-disease associations and the prediction of potential lncRNA-disease associations are necessary to provide the most probable candidates for subsequent experimental validation. Methods: In this paper, we present a novel robust computational framework for lncRNA-disease association prediction by combining the ℓ1-norm graph with multi-label learning. Specifically, we first construct a set of similarity matrices for lncRNAs and diseases using known associations. Then, both lncRNA and disease similarity matrices are adaptively re-weighted to enhance the robustness via the ℓ1-norm graph. Lastly, the association matrix is updated with a graph-based multi-label learning framework to uncover the underlying consistency between the lncRNA space and the disease space. Results : We compared the proposed method with the four latest methods on five widely used data sets. The experimental results show that our method can achieve comparable performance in both five-fold cross-validation and leave-one-disease-out cross-validation prediction tasks. The case study of prostate cancer further confirms the practicability of our approach in identifying lncRNAs as potential prognostic biomarkers. Conclusion: Our method can serve as a useful tool for the prediction of novel lncRNA-disease associations.

PredMHC: An Effective Predictor of Major Histocompatibility Complex Using Mixed Features

“…To evaluate the performance of the proposed method, we introduced four indicators commonly used in bioinformatics: sensitivity (SE), specificity (SP), accuracy (ACC), and Matthew’s correlation coefficient (MCC). The formulae of these indicators are as follows ( Zhang et al, 2021a ; Lv et al, 2021b ; Zhang et al, 2021b ; Zhang et al, 2021c ; Zhang et al, 2021d ; Zhang et al, 2021e ; Zhao et al, 2021 ; Zhu et al, 2021 ; Zou et al, 2021 ; Zhao et al, 2022 ). where TP is an abbreviation for true positives, representing the number of MHC proteins predicted in positive examples; FP is an abbreviation for false positives, representing the number of MHC proteins predicted in negative examples; TN is an abbreviation for true negatives, representing nonMHC proteins predicted in negative examples; and FN is an abbreviation for false negatives and indicates the number of predicted nonMHC proteins in positive examples.…”

MNNMDA: Predicting human microbe-disease association via a method to minimize matrix nuclear norm

“…Recently, machine learning technologies have been widely used and achieved remarkable performances in association predictions, such as lncRNA-disease association prediction [8] , [9] , [10] , [11] , [12] , [13] , drug repositioning [14] , [15] , [16] , [17] , [18] , [19] , miRNA-disease association prediction [20] , [21] , [22] , [23] and so on. Simultaneously, many computational methods have been developed to help identify the relationship between microbes and diseases.…”

iSnoDi-LSGT: identifying snoRNA-disease associations based on local similarity constraint and global topological constraint

“…Therefore, the MNDR v3.0 database provided an opportunity to efficiently identify disease-related snoRNAs by computational methods. In recent years, some computational methods have been proposed to identify unknown RNA and disease associations, such as miRNA-disease associations ( Chen et al 2018 ; Tang et al 2018 ; Huang et al 2019 ), lncRNA-disease associations ( Lu et al 2020 ; Zeng et al 2020a ; Zhang et al 2021 ) and circRNA-disease associations ( Wang et al 2020 ; Zeng et al 2020b ; Lei et al 2021 ; Wang et al 2021a ; Yang and Lei 2021 ). These methods demonstrated that it is efficient for identifying RNA-disease associations by computational methods.…”