RESUMO
Causal effect estimation of individual heterogeneity is a core issue in the field of causal inference, and its application in medicine poses an active and challenging problem. In high-risk decision-making domain such as healthcare, inappropriate treatments can have serious negative impacts on patients. Recently, machine learning-based methods have been proposed to improve the accuracy of causal effect estimation results. However, many of these methods concentrate on estimating causal effects of continuous outcome variables under binary intervention conditions, and give less consideration to multivariate intervention conditions or discrete outcome variables, thus limiting their scope of application. To tackle this issue, we combine the double machine learning framework with Light Gradient Boosting Machine (LightGBM) and propose a double LightGBM model. This model can estimate binary causal effects more accurately and in less time. Two cyclic structures were added to the model. Data correction method was introduced and improved to transform discrete outcome variables into continuous outcome variables. Multivariate Cyclic Double LightGBM model (MCD-LightGBM) was proposed to intelligently estimate multivariate treatment effects. A visual human-computer interaction system for heterogeneous causal effect estimation was designed, which can be applied to different types of data. This paper reports that the system improved the Logarithm of the Minimum Angle of Resolution (LogMAR) of visual acuity change after Vascular Endothelial Growth Factor (anti-VEGF) treatment in patients with diabetic macular degeneration. The improvement was observed in two clinical problems, from 0.05 to 0.33, and the readmission rate of diabetic patients after cure was reduced from 48.4% to 10.5%. The results above demonstrate the potential of the proposed system in predicting heterogeneous clinical drug treatment effects.
RESUMO
Colorectal cancer (CRC), the third most common cancer worldwide, poses a threat to human life. However, its underlying mechanism is unclear and no satisfactory treatment is available. The present study aimed to investigate the role of circular RNA argininosuccinate synthase 1 (circASS1) in CRC cells and tissues to identify the potential mechanism underlying the pathogenesis of CRC. The expression of circASS1 in CRC cells and tissues was determined by reverse transcription-quantitative PCR. Following circASS1 overexpression in HT29 cells, cell viability, colony formation and apoptosis were measured using MTT, colony formation and TUNEL assays, respectively. Cell invasion and migration were also assessed. After confirming the associations among circASS1, microRNA (miR)-1269a and vasohibin 1 (VASH1), the characteristics of the HT29 cell line were assessed by performing the aforementioned assays. circASS1 expression was decreased in CRC cells and tissues, and circASS1 overexpression suppressed CRC cell proliferation, invasion and migration. circASS1 adsorbed miR-1269a and regulated its expression, and VASH1 was a target protein of miR-1269a. circASS1 overexpression decreased cell proliferation, invasion and migration, but enhanced cell apoptosis in HT29 cells, which was reversed by co-transfection with miR-1269a mimic or short hairpin RNA-VASH1. In conclusion, circASS1 overexpression inhibited CRC cell proliferation, invasion and migration by regulating miR-1269a/VASH1, which indicated a potential molecular mechanism underlying the pathogenesis of CRC.
