Comprehensive Analysis of TICRR in Hepatocellular Carcinoma Based on Bioinformatics Analysis

GBDTSVM: Combined Support Vector Machine and Gradient Boosting Decision Tree Framework for efficient snoRNA-disease association prediction

Small nucleolar RNAs (snoRNAs) are increasingly recognized for their critical role in the pathogenesis and characterization of various human diseases. Consequently, the precise identification of snoRNA-disease associations (SDAs) is essential for the progression of diseases and the advancement of treatment strategies. However, conventional biological experimental approaches are costly, time-consuming, and resource-intensive; therefore, machine learning-based computational methods offer a promising solution to mitigate these limitations. This paper proposes a model called 'GBDTSVM', representing a novel and efficient machine learning approach for predicting snoRNA-disease associations by leveraging a Gradient Boosting Decision Tree (GBDT) and Support Vector Machine (SVM). 'GBDTSVM' effectively extracts integrated snoRNA-disease feature representations utilizing GBDT, and SVM is subsequently utilized to classify and identify potential associations. Furthermore, the method enhances the accuracy of these predictions by incorporating Gaussian integrated profile kernel similarity for both snoRNAs and diseases. Experimental evaluation of the GBDTSVM model demonstrates superior performance compared to state-of-the-art methods in the field, achieving an AUROC of 0.96 and an AUPRC of 0.95 on the 'MDRF' dataset. Moreover, our model shows superior performance on two more datasets named 'LSGT' and 'PsnoD'. Additionally, a case study conducted on the predicted snoRNA-disease associations verified the top-ranked snoRNAs across twelve prevalent diseases, further validating the efficacy of the GBDTSVM approach. These results underscore the model's potential as a robust tool for advancing snoRNA-related disease research. Source codes and datasets for our proposed framework can be obtained from: https://github.com/mariamuna04/gbdtsvm.

The genetic dissection of fetal haemoglobin persistence in sickle cell disease in Nigeria

The clinical severity of sickle cell disease (SCD) is strongly influenced by the level of fetal haemoglobin (HbF) persistent in each patient. Three major HbF loci (BCL11A, HBS1L-MYB, and Xmn1-HBG2) have been reported, but a considerable hidden heritability remains. We conducted a genome-wide association study for HbF levels in 1006 Nigerian patients with SCD (HbSS/HbSβ0), followed by a replication and meta-analysis exercise in four independent SCD cohorts (3,582 patients). To dissect association signals at the major loci, we performed stepwise conditional and haplotype association analyses and included public functional annotation datasets. Association signals were detected for BCL11A (lead SNP rs6706648, β = -0.39, P = 4.96 × 10-34) and HBS1L-MYB (lead SNP rs61028892, β = 0.73, P = 1.18 × 10-9), whereas the variant allele for Xmn1-HBG2 was found to be very rare. In addition, we detected three putative new trait-associated regions. Genetically, dissecting the two major loci BCL11A and HBS1L-MYB, we defined trait-increasing haplotypes (P < 0.0001) containing so far unidentified causal variants. At BCL11A, in addition to a haplotype harbouring the putative functional variant rs1427407-'T', we identified a second haplotype, tagged by the rs7565301-'A' allele, where a yet-to-be-discovered causal DNA variant may reside. Similarly, at HBS1L-MYB, one HbF-increasing haplotype contains the likely functional small indel rs66650371, and a second tagged by rs61028892-'C' is likely to harbour a presently unknown functional allele. Together, variants at BCL11A and HBS1L-MYB SNPs explained 24.1% of the trait variance. Our findings provide a path for further investigation of the causes of variable fetal haemoglobin persistence in sickle cell disease.