Search Results for "support vector machine classifier"

We developed a novel clustering-free method, microarray-associated motif analyzer (MAMA), to predict novel cis-acting elements based on weighted sequence similarities and gene expression profiles in microarray analyses. Simulation of gene expression was performed using a support vector machine and based on the presence of predicted motifs and motif pairs. The accuracy of simulated gene expression was used to evaluate the quality of prediction and to optimize the parameters used in this method. After optimization, MAMA accurately simulated more than 87% of gene expression. See Kakei Y, Ogo Y, Itai RN, et al. (2013) Development of a novel prediction method of cis-elements to hypothesize collaborative functions of cis-element pairs in iron-deficient. ...

...Given an input FASTA file of the target sites and queries the reference genome as well as a CRISPR system with a defined spacer length and PAM sequence, this standalone tool will identify putative sites and assign a predicted activity based on support vector machine model which conducted by sgRNA Scorer 2.0. In addition, sgRNAs with minimal off-target activity were predicted by Cas-OFFinder, and score with Off-Target Cutting Frequency Determination (CFD).

Applying the LIBSVM, a package of support vector machine, SAMSVM was developed to correctly detect and filter the misaligned reads of SAM format. Such filtration can reduce false positives in alignment and the following variant analysis.

A feature selection and classification algorithm. It is based on L1 Norm Support Vector Machine with t-test and Recursive Feature Elimination.

HIVM, An Application for Prediction of HIV Drug Resistance using Support Vector Machine Algorithms