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PURPOSE: The gender, age, lens culinaris agglutinin-reactive fraction of alphafetoprotein, alphafetoprotein, des-gamma-carboxyprothrombin (GALAD) score is a biomarker-based statistical model for the serologic diagnosis of hepatocellular carcinoma (HCC) that has been developed and validated using the case-control approach with a view to early detection. Performance has, however, been suboptimal in the first prospective studies which better reflect the real-world situation. In this article, we report the application of machine learning to a large, prospectively accrued, HCC surveillance data set. PATIENTS AND METHODS: Models were built on a cohort of 3,473 patients with chronic liver disease within a rigorous surveillance program between 1998 and 2014, during which 459 patients with HCC were detected. Two random forest (RF) models were trained. The first RF model uses the same variables as the original GALAD model (GALAD-RF); the second is based on routinely available clinical and laboratory features (RF-practical). For comparison, we evaluated a logistic regression GALAD model trained on this longitudinal prospective data set (termed GALAD-Ogaki). RESULTS: Models were evaluated using a repetitive cross-validation approach with the metrics averaged over 100 independent runs. As judged by area under the receiver operator curve (AUROC) and F1 score, the GALAD RF model significantly outperformed the original GALAD model. The RF-practical model also outperformed the original GALAD model in terms of both AUROC and F1 score, and both models outperformed the individual biomarkers. An online web application that implemented the GALAD-RF and RF-practical models is presented. CONCLUSION: RF-based models improve on the diagnostic performance of the original GALAD model in the setting of a standard HCC surveillance program. Further prospective validation studies are warranted using these models and could be expanded to offer prediction of risk of HCC development over defined periods of time. Machine learning is used to predict development of hepatocellular carcinoma in those with cirrhosis [ABSTRACT FROM AUTHOR]

In Pakistan the work on genetic of different populations of Channa marulius have not been performed so for and the Clustering analysis was done to find out the intraspecific variations among different populations of Channa marulius using Pearson Correlation Coefficient method based on Unweighted Pair Group Method with Arithmetic Mean (UPGMA) by XLSTAT 2012 (version 1.02). 50 samples of C. marulius populations were collected from five different geographical sites of natural waters viz., Chashma Barrage at River Indus (District Mianwali), Qadirabad Barrage at the River Chenab (District Gujranwala), Balloki Barrage at River Ravi (District Kasur), Trimmu Barrage at the junction of Chenab and Jhelum Rivers (District Jhang) and Taunsa Barrage at Indus River (Tehsil Kot Addu District Muzaffar Garh). Dendrogram generated by RAPD data of representative samples of each site divided the populations of C. marulius into four major clusters. The results of the studies clearly indicate that variance decomposition for the optimal classification values remained as, 65.90% for within class variation while 34.10% for the between class differences. The distance between the class/cluster centroids was 1.737 for class 1 and 2, 1.105 for 1 and 3, 1.009 for 1 and 4, 1.500 for class 2 and 3, 2.000 for 2 and 4, while this distance between class 3 and 4 was 1.500. Quantifiable differentiation among various species of C. marulius revealed the impacts of changing environment and other possible factors. [ABSTRACT FROM AUTHOR]