 Connection
Mary Yang to Artificial Intelligence
This is a "connection" page, showing publications Mary Yang has written about Artificial Intelligence.
|
|
| |
Connection Strength |
|
 |
|
 |
| |
1.184 |
|
|
|
-
Li GZ, Meng HH, Lu WC, Yang JY, Yang MQ. Asymmetric bagging and feature selection for activities prediction of drug molecules. BMC Bioinformatics. 2008 May 28; 9 Suppl 6:S7.
Score: 0.289
-
Yang JY, Yang MQ. Identification of Intrinsically Unstructured Proteins using hierarchical classifier. Int J Data Min Bioinform. 2008; 2(2):121-33.
Score: 0.281
-
Wang L, Yang MQ, Yang JY. Prediction of DNA-binding residues from protein sequence information using random forests. BMC Genomics. 2009 Jul 07; 10 Suppl 1:S1.
Score: 0.078
-
Liu Q, Sung AH, Qiao M, Chen Z, Yang JY, Yang MQ, Huang X, Deng Y. Comparison of feature selection and classification for MALDI-MS data. BMC Genomics. 2009 Jul 07; 10 Suppl 1:S3.
Score: 0.078
-
Yang JY, Yang MQ. Predicting protein disorder by analyzing amino acid sequence. BMC Genomics. 2008 Sep 16; 9 Suppl 2:S8.
Score: 0.074
-
Pirooznia M, Yang JY, Yang MQ, Deng Y. A comparative study of different machine learning methods on microarray gene expression data. BMC Genomics. 2008; 9 Suppl 1:S13.
Score: 0.070
-
Yang MQ, Elnitski LL. Prediction-based approaches to characterize bidirectional promoters in the mammalian genome. BMC Genomics. 2008; 9 Suppl 1:S2.
Score: 0.070
-
Yang JY, Yang MQ, Luo Z, Ma Y, Li J, Deng Y, Huang X. A hybrid machine learning-based method for classifying the Cushing's Syndrome with comorbid adrenocortical lesions. BMC Genomics. 2008; 9 Suppl 1:S23.
Score: 0.070
-
Li GZ, Yang JY, Lu WC, Li D, Yang MQ. Improving prediction accuracy of drug activities by utilising unlabelled instances with feature selection. Int J Comput Biol Drug Des. 2008; 1(1):1-13.
Score: 0.070
-
Yang F, Darsey JA, Ghosh A, Li HY, Yang MQ, Wang S. Artificial Intelligence and Cancer Drug Development. Recent Pat Anticancer Drug Discov. 2022; 17(1):2-8.
Score: 0.046
-
Wang L, Huang C, Yang MQ, Yang JY. BindN+ for accurate prediction of DNA and RNA-binding residues from protein sequence features. BMC Syst Biol. 2010 May 28; 4 Suppl 1:S3.
Score: 0.021
-
Liu Q, Yang J, Chen Z, Yang MQ, Sung AH, Huang X. Supervised learning-based tagSNP selection for genome-wide disease classifications. BMC Genomics. 2008; 9 Suppl 1:S6.
Score: 0.018
-
Yang JY, Yang MQ, Dunker AK, Deng Y, Huang X. Investigation of transmembrane proteins using a computational approach. BMC Genomics. 2008; 9 Suppl 1:S7.
Score: 0.018
|
Connection Strength
The connection strength for concepts is the sum of the scores for each matching publication.
Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.
|