AIMC Topic: Computational Biology

Clear Filters Showing 4551 to 4560 of 4583 articles

Feature selection using feature dissimilarity measure and density-based clustering: application to biological data.

Journal of biosciences
Reduction of dimensionality has emerged as a routine process in modelling complex biological systems. A large number of feature selection techniques have been reported in the literature to improve model performance in terms of accuracy and speed. In ...

Active Batch Selection via Convex Relaxations with Guaranteed Solution Bounds.

IEEE transactions on pattern analysis and machine intelligence
Active learning techniques have gained popularity to reduce human effort in labeling data instances for inducing a classifier. When faced with large amounts of unlabeled data, such algorithms automatically identify the exemplar instances for manual a...

Classifying pairs with trees for supervised biological network inference.

Molecular bioSystems
Networks are ubiquitous in biology, and computational approaches have been largely investigated for their inference. In particular, supervised machine learning methods can be used to complete a partially known network by integrating various measureme...

Exploiting ontology graph for predicting sparsely annotated gene function.

Bioinformatics (Oxford, England)
MOTIVATION: Systematically predicting gene (or protein) function based on molecular interaction networks has become an important tool in refining and enhancing the existing annotation catalogs, such as the Gene Ontology (GO) database. However, functi...

Semi-Supervised Affinity Propagation with Soft Instance-Level Constraints.

IEEE transactions on pattern analysis and machine intelligence
Soft-constraint semi-supervised affinity propagation (SCSSAP) adds supervision to the affinity propagation (AP) clustering algorithm without strictly enforcing instance-level constraints. Constraint violations lead to an adjustment of the AP similari...

A scalable projective scaling algorithm for l(p) loss with convex penalizations.

IEEE transactions on neural networks and learning systems
This paper presents an accurate, efficient, and scalable algorithm for minimizing a special family of convex functions, which have a lp loss function as an additive component. For this problem, well-known learning algorithms often have well-establish...

[Comparative Efficiency of Algorithms Based on Support Vector Machines for Regression].

Biofizika
Methods of construction of support vector machines do not require additional a priori information and can be used to process large scale data set. It is especially important for various problems in computational biology. The main set of algorithms of...

Dynamic partial reconfiguration implementation of the SVM/KNN multi-classifier on FPGA for bioinformatics application.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Bioinformatics data tend to be highly dimensional in nature thus impose significant computational demands. To resolve limitations of conventional computing methods, several alternative high performance computing solutions have been proposed by scient...