LRSSLMDA: Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction

<div><p>Predicting novel microRNA (miRNA)-disease associations is clinically significant due to miRNAs’ potential roles of diagnostic biomarkers and therapeutic targets for various human diseases. Previous studies have demonstrated the viability of utilizing different types of biological data to computationally infer new disease-related miRNAs. Yet researchers face the challenge of how to effectively integrate diverse datasets and make reliable predictions. In this study, we presented a computational model named Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction (LRSSLMDA), which projected miRNAs/diseases’ statistical feature profile and graph theoretical feature profile to a common subspace. It used Laplacian regularization to preserve the local structures of the training data and a <i>L</i>₁-norm constraint to select important miRNA/disease features for prediction. The strength of dimensionality reduction enabled the model to be easily extended to much higher dimensional datasets than those exploited in this study. Experimental results showed that LRSSLMDA outperformed ten previous models: the AUC of 0.9178 in global leave-one-out cross validation (LOOCV) and the AUC of 0.8418 in local LOOCV indicated the model’s superior prediction accuracy; and the average AUC of 0.9181+/-0.0004 in 5-fold cross validation justified its accuracy and stability. In addition, three types of case studies further demonstrated its predictive power. Potential miRNAs related to Colon Neoplasms, Lymphoma, Kidney Neoplasms, Esophageal Neoplasms and Breast Neoplasms were predicted by LRSSLMDA. Respectively, 98%, 88%, 96%, 98% and 98% out of the top 50 predictions were validated by experimental evidences. Therefore, we conclude that LRSSLMDA would be a valuable computational tool for miRNA-disease association prediction.</p></div

To evaluate the predictability of different feature profiles in our study, the statistical profile and the graph theoretical profile were used separately for prediction in global LOOCV, local LOOCV and 5-fold cross validation.

<p>The corresponding AUCs are shown in the second and third columns, and compared with the AUCs for LRSSLMDA with both profiles in the fourth column.</p

Performance comparison between LRSSLMDA and ten previous disease-miRNA association prediction models (PBMDA, MaxFlow, MCMDA, NCPMDA, HGIMDA, MiRAI, WBSMDA, HDMP, RLSMDA and RWRMDA) in terms of ROC curves and AUCs based on global and local LOOCV.

<p>As a result, LRSSLMDA outperformed other models by achieving an AUC of 0.9178 in global LOOCV and an AUC of 0.8418 in local LOOCV.</p

Flowchart of potential miRNA-disease association prediction based on the computational model of LRSSLMDA.

<p>1) data preparation, where statistical and graph theoretical features for miRNAs/diseases were extracted and graph Laplacian matrices were formed; 2) model formation, where a common subspace for the miRNA/disease profiles, a <i>L</i>_<i>1</i>-norm constraint and Laplacian regularization terms were joint to construct the LRSSLMDA model; 3) optimization, where the projection matrices were iteratively updated, the controlling parameter was renewed and they were combined to yield the prediction outcomes from the miRNA/disease perspective. The final predictions were made according to whether the investigated miRNA/disease had known associated diseases/miRNAs or not.</p

Prediction of the top 50 potential Breast Neoplasms-related miRNAs based on known associations in the old version of HMDD, that is, HMDD v1.0.

<p>The first column records top 1–25 related miRNAs. The third column records the top 26–50 related miRNAs. The evidences for the associations were either HMDD v2.0, dbDEMC and miR2Disease or more recent experimental literatures with the corresponding PMIDs.</p

Prediction of the top 50 potential Esophageal Neoplasms-related miRNAs based on known associations in HMDD v2.0 database.

<p>All the known miRNAs related to this cancer were removed from the training samples, and LRSSLMDA was built solely from the disease perspective. The first column records top 1–25 related miRNAs. The third column records the top 26–50 related miRNAs. The evidences for the associations were dbDEMC, miR2Disease and HMDD v2.0.</p

Prediction of the top 50 potential Colon Neoplasms-related miRNAs based on known associations in HMDD v2.0 database.

<p>The first column records top 1–25 related miRNAs. The third column records the top 26–50 related miRNAs. The evidences for the associations were either dbDEMC and miR2Disease or more recent experimental literatures with the corresponding PMIDs.</p

Prediction of the top 50 potential Lymphoma-related miRNAs based on known associations in HMDD v2.0 database.

<p>The first column records top 1–25 related miRNAs. The third column records the top 26–50 related miRNAs. The evidences for the associations were either dbDEMC and miR2Disease or more recent experimental literatures with the corresponding PMIDs.</p

Selective Imaging of Gram-Negative and Gram-Positive Microbiotas in the Mouse Gut

The diverse gut microbial communities are crucial for host health. How the interactions between microbial communities and between host and microbes influence the host, however, is not well understood. To facilitate gut microbiota research, selective imaging of specific groups of microbiotas in the gut is of great utility but remains technically challenging. Here we present a chemical approach that enables selective imaging of Gram-negative and Gram-positive microbiotas in the mouse gut by exploiting their distinctive cell wall components. Cell-selective labeling is achieved by the combined use of metabolic labeling of Gram-negative bacterial lipopolysaccharides with a clickable azidosugar and direct labeling of Gram-positive bacteria with a vancomycin-derivatized fluorescent probe. We demonstrated this strategy by two-color fluorescence imaging of Gram-negative and Gram-positive gut microbiotas in the mouse intestines. This chemical method should be broadly applicable to different gut microbiota research fields and other bacterial communities studied in microbiology

Stimulus Roving and Flankers Affect Perceptual Learning of Contrast Discrimination in <i>Macaca mulatta</i>

<div><p>‘Stimulus roving’ refers to a paradigm in which the properties of the stimuli to be discriminated vary from trial to trial, rather than being kept constant throughout a block of trials. Rhesus monkeys have previously been shown to improve their contrast discrimination performance on a non-roving task, in which they had to report the contrast of a test stimulus relative to that of a fixed-contrast sample stimulus. Human psychophysics studies indicate that roving stimuli yield little or no perceptual learning. Here, we investigate how stimulus roving influences perceptual learning in macaque monkeys and how the addition of flankers alters performance under roving conditions. Animals were initially trained on a contrast discrimination task under non-roving conditions until their performance levels stabilized. The introduction of roving contrast conditions resulted in a pronounced drop in performance, which suggested that subjects initially failed to heed the sample contrast and performed the task using an internal memory reference. With training, significant improvements occurred, demonstrating that learning is possible under roving conditions. To investigate the notion of flanker-induced perceptual learning, flanker stimuli (30% fixed-contrast iso-oriented collinear gratings) were presented jointly with central (roving) stimuli. Presentation of flanker stimuli yielded substantial performance improvements in one subject, but deteriorations in the other. Finally, after the removal of flankers, performance levels returned to their pre-flanker state in both subjects, indicating that the flanker-induced changes were contingent upon the continued presentation of flankers.</p></div