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Ashtawy HM, Mahapatra NR: A comparative assessment of ranking accuracies of conventional and machine-learning-based scoring functions for protein-ligand binding affinity prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB). 2012, 9 (5): 1301-1313. Article Google Schola...
Most of the classifiers used, as well as the sampling and feature selection methods, require us to specify parameters, such as the number of neighbors for the kNN classifier or the number of majority class instances to remove in undersampling. While learning these from data may improve performan...
All of the predicted drug-cancer pairs ranking in the top 100 are involved in validation. We visualize the network of the top predictions in Fig. 14. After searching for the drug-cancer association predictions in PubMed, we find literature support for 23 out of 25 pairs. For example, Predn...
Recursive Feature Elimination with SVM (RFE-SVM) By starting with the complete set of features, RFE-SVM repeats the following three steps until no more features are left: 1) train a SVM model; 2) compute a ranking of features as the squares of the hyperplane coefficients of the SVM model...
requiring an additional method to match each named entity to a kb. entity linking can also be modeled as a ranking task, where a list of candidate matches of an entity is ordered from highest to lowest confidence level. the input data consists of a graph, where nodes represent associations ...
The filtering method assesses the relevance of features by looking only at the intrinsic properties of the data, and selects high-ranking features based on a statistical or information measure, such as information gain and gain ratio [13]. There are two drawbacks of filter-based selection: ...
Once ranking is complete, use local averaging to create non-zero values for any N r that are zero (frequencies at which no species was observed). 3. Fit linear regression to the resulting ranked list. 4. Compute the probability of seeing an unobserved species as N1N, with N being the...
A goal of future BioCreatives will be to perform experiments to measure time saved through the use of automated document ranking enhanced by evidence summaries. Now suppose one has arrived at a document that gives promise of having curatable PPIs. Let us consider first the GN task for this ...
Additional file 2: Evaluation of MeSH term ranking per document. The first sheet shows a summary of the results. The following sheets show the results according to the method used to index the full text, the summaries and MEDLINE. The data has been obtained using the trec_eval evaluation pro...