AI Article Synopsis
- * Previous research has identified many different genes associated with psoriasis, but a reliable prediction model was lacking until this study.
- * This study developed a classification model using an innovative feature selection algorithm that accurately predicts psoriasis with a high stability rate (99.81%) using only three features from two genes, IGFL1 and C10orf99, which are important in the growth signaling pathways related to psoriasis.
Article Abstract
Psoriasis is an autoimmune disease, which symptoms can significantly impair the patient's life quality. It is mainly diagnosed through the visual inspection of the lesion skin by experienced dermatologists. Currently no cure for psoriasis is available due to limited knowledge about its pathogenesis and development mechanisms. Previous studies have profiled hundreds of differentially expressed genes related to psoriasis, however with no robust psoriasis prediction model available. This study integrated the knowledge of three feature selection algorithms that revealed 21 features belonging to 18 genes as candidate markers. The final psoriasis classification model was established using the novel Incremental Feature Selection algorithm that utilizes only 3 features from 2 unique genes, IGFL1 and C10orf99. This model has demonstrated highly stable prediction accuracy (averaged at 99.81%) over three independent validation strategies. The two marker genes, IGFL1 and C10orf99, were revealed as the upstream components of growth signal transduction pathway of psoriatic pathogenesis.
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| http://dx.doi.org/10.1016/j.ygeno.2013.11.001 | DOI Listing |
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