TTE prediction model performance comparison on TCGA data.

TTE prediction model performance comparison on TCGA data.</p

TTE prediction model performance comparison on SHHS data.

TTE prediction model performance comparison on SHHS data.</p

TTE prediction performance comparison between models with and without the related outcome incorporator (ROI).

Each box plot shows the distribution of C-index values for the 19 TTE prediction tasks (Table 1). The p values were calculated using one-sided Wilcoxon signed-rank test.</p

Annotations for cancer type abbreviations.

(XLSX)</p

Fig 2 -

The four TTE prediction models: (A) CPH Model, (B) CPH_ROI Model, (C) CPH_DL Model and (D) CPH_DL_ROI Model. X represents the input features, C is the Cox regression layer, L is the partial hazard loss function, λ is the weight to balance outcomes, and F is the feature extractor to map the input feature into an embedding space. CPH: Cox proportional hazards. ROI: related outcome incorporator. DL: deep learning.</p

TTE prediction model performance comparison on synthetic data.

TTE prediction model performance comparison on synthetic data.</p

Results of genes analysis in gene clusters uncovered by LRR from yeast dataset.

<p>Only selected two enriched functional categories and the corresponding annotated genes are presented. The columns of the table summarize the number of annotated genes in the module versus the total size of the cluster (numbers in the parentheses), the GO categories associated with the cluster, and a set of annotated genes.</p

Two heatmaps of expression values of genes analyzed by the proposed algorithm from the yeast_Spellman dataset: (A) a heatmap of expression values of genes in Cluster C27, and the heatmap shows similar expression patterns of genes in different samples, (B) a heatmap of expression values of genes in Cluster C10, and the heatmap shows different expression patterns of genes in different samples (denoted as a and b).

<p>Two heatmaps of expression values of genes analyzed by the proposed algorithm from the yeast_Spellman dataset: (A) a heatmap of expression values of genes in Cluster C27, and the heatmap shows similar expression patterns of genes in different samples, (B) a heatmap of expression values of genes in Cluster C10, and the heatmap shows different expression patterns of genes in different samples (denoted as a and b).</p

Systematic Analysis of microRNA Targeting Impacted by Small Insertions and Deletions in Human Genome

<div><p>MicroRNAs (miRNAs) are small noncoding RNA that play an important role in posttranscriptional regulation of mRNA. Genetic variations in miRNAs or their target sites have been shown to alter miRNA function and have been associated with risk for several diseases. Previous studies have focused on the most abundant type of genetic variations, single nucleotide polymorphisms (SNPs) that affect miRNA-mRNA interactions. Here, we systematically identified small insertions and deletions (indels) in miRNAs and their target sites, and investigated the effects of indels on miRNA targeting. We studied the distribution of indels in miRNAs and their target sites and found that indels in mature miRNAs, experimentally supported miRNA target sites and PAR-CLIP footprints have significantly lower density compared to flanking regions. We identified over 20 indels in the seed regions of miRNAs, which may disrupt the interactions between these miRNAs and their target genes. We also identified hundreds of indels that alter experimentally supported miRNA target sites. We mapped these genes to human disease pathways to identify indels that affect miRNA targeting in these pathways. We also used the results of genome-wide association studies (GWAS) to identify potential links between miRNA-related indels and diseases.</p> </div

Identifying Subspace Gene Clusters from Microarray Data Using Low-Rank Representation

<div><p>Identifying subspace gene clusters from the gene expression data is useful for discovering novel functional gene interactions. In this paper, we propose to use low-rank representation (LRR) to identify the subspace gene clusters from microarray data. LRR seeks the lowest-rank representation among all the candidates that can represent the genes as linear combinations of the bases in the dataset. The clusters can be extracted based on the block diagonal representation matrix obtained using LRR, and they can well capture the intrinsic patterns of genes with similar functions. Meanwhile, the parameter of LRR can balance the effect of noise so that the method is capable of extracting useful information from the data with high level of background noise. Compared with traditional methods, our approach can identify genes with similar functions yet without similar expression profiles. Also, it could assign one gene into different clusters. Moreover, our method is robust to the noise and can identify more biologically relevant gene clusters. When applied to three public datasets, the results show that the LRR based method is superior to existing methods for identifying subspace gene clusters.</p> </div