Attention Deficit Hyperactivity Disorder (ADHD) is a common neurobehavioral disorder worldwide. While extensive research has focused on machine learning methods for ADHD diagnosis, most research relies on high-cost equipment, e.g., MRI machine and EEG patch. Therefore, low-cost diagnostic methods based on the action characteristics of ADHD are desired. Skeleton-based action recognition has gained attention due to the action-focused nature and robustness. In this work, we propose a novel ADHD diagnosis system with a skeleton-based action recognition framework, utilizing a real multi-modal ADHD dataset and state-of-the-art detection algorithms. Compared to conventional methods, the proposed method shows cost-efficiency and significant performance improvement, making it more accessible for a broad range of initial ADHD diagnoses. Through the experiment results, the proposed method outperforms the conventional methods in accuracy and AUC. Meanwhile, our method is widely applicable for mass screening.

The rate at which expert annotators add the experimental information into more or less controlled vocabularies of databases snails along at an even slower pace. Most methods that annotate protein function exploit sequence similarity by transferring experimental information for homologues. A crucial development aiding such transfer is large-scale, work- and management-intensive projects aimed at developing a comprehensive ontology for gene-protein function, such as the Gene Ontology project. Some of these tools target parsing controlled vocabulary from databases; others venture at mining free texts from MEDLINE abstracts or full scientific papers.

We now know the full genomes of more than 60 organisms. The experimental characterization of the newly sequenced proteins is deemed to lack behind this explosion of naked sequences (sequencefunction gap). The rate at which expert annotators add the experimental information into more or less controlled vocabularies of databases snails along at an even slower pace. Most methods that annotate protein function exploit sequence similarity by transferring experimental information for homologues. A crucial development aiding such transfer is large-scale, work- and management-intensive projects aimed at developing a comprehensive ontology for gene-protein function, such as the Gene Ontology project. In parallel, fully automatic or semiautomatic methods have successfully begun to mine the existing data through lexical analysis. Some of these tools target parsing controlled vocabulary from databases; others venture at mining free texts from MEDLINE abstracts or full scientific papers. Automated text analysis has become a rapidly expanding discipline in bioinformatics. A few of these tools have already been embedded in research projects.