Racial-Ethnic Differences in ADHD Diagnosis and Treatment During Adolescence and Early Adulthood.

OBJECTIVE: This study examined racial-ethnic differences in attention-deficit hyperactivity disorder (ADHD) diagnosis and treatment during adolescence and early adulthood. METHODS: A national health care claims database was used to identify a cohort of 4,216,757 commercially insured youths with at least 1 year of coverage during 2014-2019. Racial-ethnic differences in the prevalence of visits with a recorded ADHD diagnosis (identified through ICD-9-CM and ICD-10-CM codes) and of ADHD treatment (identified through medical claims for psychosocial treatments and pharmacy claims for ADHD medications) were examined. Period prevalence rates were determined within five age categories, stratified by race-ethnicity. Poisson regression with a natural log link was used within each age category to estimate prevalence ratios (PRs) comparing prevalence in each racially and ethnically minoritized group with prevalence in the White group. RESULTS: The overall prevalence of ADHD diagnosis was 9.1% at ages 12-14 and 5.3% at ages 24-25. In each age category, Asian, Black, and Hispanic youths had lower prevalence of ADHD diagnosis than did White youths (PR=0.29-0.77). Among youths with an ADHD diagnosis, relative racial-ethnic differences in treatment were small (PR=0.92-1.03). CONCLUSIONS: Throughout adolescence and early adulthood, racially and ethnically minoritized youths were less likely than White youths to have health care visits with recorded ADHD diagnoses and, among those with diagnoses, were also slightly less likely to receive treatment. More research is needed to understand the processes underlying these differences and their potential health consequences among racially and ethnically minoritized youths.

PSoL: a positive sample only learning algorithm for finding non-coding RNA genes.

MOTIVATION: Small non-coding RNA (ncRNA) genes play important regulatory roles in a variety of cellular processes. However, detection of ncRNA genes is a great challenge to both experimental and computational approaches. In this study, we describe a new approach called positive sample only learning (PSoL) to predict ncRNA genes in the Escherichia coli genome. Although PSoL is a machine learning method for classification, it requires no negative training data, which, in general, is hard to define properly and affects the performance of machine learning dramatically. In addition, using the support vector machine (SVM) as the core learning algorithm, PSoL can integrate many different kinds of information to improve the accuracy of prediction. Besides the application of PSoL for predicting ncRNAs, PSoL is applicable to many other bioinformatics problems as well. RESULTS: The PSoL method is assessed by 5-fold cross-validation experiments which show that PSoL can achieve about 80% accuracy in recovery of known ncRNAs. We compared PSoL predictions with five previously published results. The PSoL method has the highest percentage of predictions overlapping with those from other methods.

A unified representation of multiprotein complex data for modeling interaction networks.

The protein interaction network presents one perspective for understanding cellular processes. Recent experiments employing high-throughput mass spectrometric characterizations have resulted in large data sets of physiologically relevant multiprotein complexes. We present a unified representation of such data sets based on an underlying bipartite graph model that is an advance over existing models of the network. Our unified representation allows for weighting of connections between proteins shared in more than one complex, as well as addressing the higher level organization that occurs when the network is viewed as consisting of protein complexes that share components. This representation also allows for the application of the rigorous MinMaxCut graph clustering algorithm for the determination of relevant protein modules in the networks. Statistically significant annotations of clusters in the protein-protein and complex-complex networks using terms from the Gene Ontology indicate that this method will be useful for posing hypotheses about uncharacterized components of protein complexes or uncharacterized relationships between protein complexes.

Classification of non-coding RNA using graph representations of secondary structure.

Some genes produce transcripts that function directly in regulatory, catalytic, or structural roles in the cell. These non-coding RNAs are prevalent in all living organisms, and methods that aid the understanding of their functional roles are essential. RNA secondary structure, the pattern of base-pairing, contains the critical information for determining the three dimensional structure and function of the molecule. In this work we examine whether the basic geometric and topological properties of secondary structure are sufficient to distinguish between RNA families in a learning framework. First, we develop a labeled dual graph representation of RNA secondary structure by adding biologically meaningful labels to the dual graphs proposed by Gan et al [1]. Next, we define a similarity measure directly on the labeled dual graphs using the recently developed marginalized kernels [2]. Using this similarity measure, we were able to train Support Vector Machine classifiers to distinguish RNAs of known families from random RNAs with similar statistics. For 22 of the 25 families tested, the classifier achieved better than 70% accuracy, with much higher accuracy rates for some families. Training a set of classifiers to automatically assign family labels to RNAs using a one vs. all multi-class scheme also yielded encouraging results. From these initial learning experiments, we suggest that the labeled dual graph representation, together with kernel machine methods, has potential for use in automated analysis and classification of uncharacterized RNA molecules or efficient genome-wide screens for RNA molecules from existing families.