Oral squamous cell carcinoma (OSCC) has the highest incidence among all the oral neoplasms, thus widely affects people’s life. Genetics has an important effect on the OSCC’s etiopathogenesis. However, OSCC’s molecular mechanism has not been completed investigated yet. We proposed a novel method to detect genes that are associated to OSCC, where mean difference and variance difference were compared between cases and controls. This method can discover knowledge from gene data by processing multi-format information and has managed to identify disease-associated genes for many complex human diseases, like melanoma, head and neck squamous cell carcinoma (HNSCC), lung adenocarcinoma, and serous ovarian cancer. Based on Gene Expression Omnibus, two OSCC datasets were derived and gene probes were then identified, typed as differentially variable (DV) and differentially expressed (DE), with number 456 and 2,375, respectively. Those probes can be further discriminated as DE-only, DV-only and then DE-and-DV probes, with number 2,193, 274 and 182, respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were adopted to distinctly enrich genes that relevant to these three kinds of specific probes, as per the DAVID functional analysis, implying they have diverse works in OSCC. In the future, our novel method also could be applied to investigate other complex human diseases’ genetic risk factors.

Myasthenia Gravis (MG) is an autoimmune neuromuscular disease. Given that extraocular muscle manifestations are the initial and primary symptoms in most patients, ocular muscle assessment is regarded necessary early screening tool. To overcome the limitations of the manual clinical method, an intuitive idea is to collect data via imaging devices, followed by analysis or processing using Deep Learning (DL) techniques (particularly image segmentation approaches) to enable automatic MG evaluation. Unfortunately, their clinical applications in this field have not been thoroughly explored. To bridge this gap, our study prospectively establishes a new DL-based system to promote the diagnosis of MG disease, with a complete workflow including facial data acquisition, eye region localization, and ocular structure segmentation. Experimental results demonstrate that the proposed system achieves superior segmentation performance of ocular structure. Moreover, it markedly improves the diagnostic accuracy of doctors. In the future, this endeavor can offer highly promising MG monitoring tools for healthcare professionals, patients, and regions with limited medical resources.