Titlebook: Bioinformatics and Biomedical Engineering; 11th International C Ignacio Rojas,Francisco Ortuño,Olga Valenzuela Conference proceedings 2024

忠诚

书目名称Bioinformatics and Biomedical Engineering影响因子(影响力)




书目名称Bioinformatics and Biomedical Engineering影响因子(影响力)学科排名




书目名称Bioinformatics and Biomedical Engineering网络公开度




书目名称Bioinformatics and Biomedical Engineering网络公开度学科排名




书目名称Bioinformatics and Biomedical Engineering被引频次




书目名称Bioinformatics and Biomedical Engineering被引频次学科排名




书目名称Bioinformatics and Biomedical Engineering年度引用




书目名称Bioinformatics and Biomedical Engineering年度引用学科排名




书目名称Bioinformatics and Biomedical Engineering读者反馈




书目名称Bioinformatics and Biomedical Engineering读者反馈学科排名

领袖气质

HILAR

Iron Metabolism in the Bone Marrow features extracted from speech signals of three- to six-year children. These were tested with seven classification models. Experiments showed that these features of speech, combined with decision tree, can be employed for early screening of ASD for children with an error rate of 0.25%.

Communal

Frequency-Range

CLAM

P. J. Marie,M.-T. Garba,M. Hott,L. Miravetthod with two other tools, NCPCDA and DWNCPCDA, using a subset of the CDASOR dataset and showed that GPACDA overcomes its competitors in terms of true association ranks. Our method’s code and predictions are publicly accessible.

Assignment

GPACDA – circRNA-Disease Association Prediction with Generating Polynomialsthod with two other tools, NCPCDA and DWNCPCDA, using a subset of the CDASOR dataset and showed that GPACDA overcomes its competitors in terms of true association ranks. Our method’s code and predictions are publicly accessible.

拖债

狗窝

Executive Summary and Conclusions,The positive correlation between WB BMC and 1-RM deadlift remained significant after controlling for age using a multiple linear regression analysis. In conclusion, the current study suggests that maximal deadlift strength is an independent determinant of bone mass in elderly men.

Aromatic

Elucidating Cancer Subtypes by Using Epigenome and Genome Cross-Talkmonstrate that data integration results in statistically significant subtypes similar to the TCGA subtypes and outperforms existing classification efforts. The significance of this study lies in enhancement in the subtyping of prostate cancer, as well as statistical integration of epigenomic and transcriptomic data.