A data-fusion approach to identifying developmental dyslexia from multi-omics datasets.

This exploratory study tested and validated the use of data fusion and machine learning techniques to probe high-throughput omics and clinical data with a goal of exploring the etiology of developmental dyslexia. Developmental dyslexia is the leading learning disability in school aged children affecting roughly 5-10% of the US population. The complex biological and neurological phenotype of this life altering disability complicates its diagnosis. Phenome, exome, and metabolome data was collected allowing us to fully explore this system from a behavioral, cellular, and molecular point of view. This study provides a proof of concept showing that data fusion and ensemble learning techniques can outperform traditional machine learning techniques when provided small and complex multi-omics and clinical datasets. Heterogenous stacking classifiers consisting of single-omic experts/models achieved an accuracy of 86%, F1 score of 0.89, and AUC value of 0.83. Ensemble methods also provided a ranked list of important features that suggests exome single nucleotide polymorphisms found in the thalamus and cerebellum could be potential biomarkers for developmental dyslexia and heavily influenced the classification of DD within our machine learning models.

Conspecific Identity Determines Interactive Space Area in Zebrafish Shoal.

Sex ratio of shoals has been shown to influence shoaling behavior in many fishes. This study tests whether the conspecific identity influences shoal performance (shoal area, interactive distances, distance traveled, and thigmotaxis) of zebrafish (Danio rerio) via group tracking. We conducted a two-dimensional analysis of shoals with different sex ratios (male only, female only, male rich, and female rich) of a five-membered shoal. Parameters describing the shoal structure and individual behavior were derived using video tracking and a custom-made program. We found that mixed-sex shoals had significantly lesser shoal area and interactive distance compared to single-sex shoals (approximate difference of 80% for shoal area and 50% for interactive distance). Our findings shed light on complex interactive behaviors of zebrafish in a shoal that are affected by differences in sex ratios of interacting individuals. The outcomes from this study can be used to design better zebrafish shoaling experiments for clinically relevant research like human nerve disorders and social deficits.

Developing a machine learning model to identify protein-protein interaction hotspots to facilitate drug discovery.

Throughout the history of drug discovery, an enzymatic-based approach for identifying new drug molecules has been primarily utilized. Recently, protein-protein interfaces that can be disrupted to identify small molecules that could be viable targets for certain diseases, such as cancer and the human immunodeficiency virus, have been identified. Existing studies computationally identify hotspots on these interfaces, with most models attaining accuracies of ~70%. Many studies do not effectively integrate information relating to amino acid chains and other structural information relating to the complex. Herein, (1) a machine learning model has been created and (2) its ability to integrate multiple features, such as those associated with amino-acid chains, has been evaluated to enhance the ability to predict protein-protein interface hotspots. Virtual drug screening analysis of a set of hotspots determined on the EphB2-ephrinB2 complex has also been performed. The predictive capabilities of this model offer an AUROC of 0.842, sensitivity/recall of 0.833, and specificity of 0.850. Virtual screening of a set of hotspots identified by the machine learning model developed in this study has identified potential medications to treat diseases caused by the overexpression of the EphB2-ephrinB2 complex, including prostate, gastric, colorectal and melanoma cancers which are linked to EphB2 mutations. The efficacy of this model has been demonstrated through its successful ability to predict drug-disease associations previously identified in literature, including cimetidine, idarubicin, pralatrexate for these conditions. In addition, nadolol, a beta blocker, has also been identified in this study to bind to the EphB2-ephrinB2 complex, and the possibility of this drug treating multiple cancers is still relatively unexplored.