SERIAL NONRIGID VASCULAR REGISTRATION USING WEIGHTED NORMALIZED MUTUAL INFORMATION

Weighted mutual information analysis substantially improves domain-based functional network models

Motivation: Functional protein–protein interaction (PPI) networks elucidate molecular pathways underlying complex phenotypes, including those of human diseases. Extrapolation of domain–domain interactions (DDIs) from known PPIs is a major domain-based method for inferring functional PPI networks. However, the protein domain is a functional unit of the protein. Therefore, we should be able to effectively infer functional interactions between proteins based on the co-occurrence of domains.

Results: Here, we present a method for inferring accurate functional PPIs based on the similarity of domain composition between proteins by weighted mutual information (MI) that assigned different weights to the domains based on their genome-wide frequencies. Weighted MI outperforms other domain-based network inference methods and is highly predictive for pathways as well as phenotypes. A genome-scale human functional network determined by our method reveals numerous communities that are significantly associated with known pathways and diseases. Domain-based functional networks may, therefore, have potential applications in mapping domain-to-pathway or domain-to-phenotype associations.

Availability and Implementation: Source code for calculating weighted mutual information based on the domain profile matrix is available from www.netbiolab.org/w/WMI.

Contact:Insuklee@yonsei.ac.kr

Supplementary information:Supplementary data are available at Bioinformatics online.

Low-Dimensional Non-Rigid Image Registration Using Statistical Deformation Models From Semi-Supervised Training Data

Accurate and robust image registration is a fundamental task in medical image analysis applications, and requires non-rigid transformations with a large number of degrees of freedom. Statistical deformation models (SDMs) attempt to learn the distribution of non-rigid deformations, and can be used both to reduce the transformation dimensionality and to constrain the registration process. However, high-dimensional SDMs are difficult to train given orders of magnitude fewer training samples. In this paper, we utilize both a small set of annotated imaging data and a large set of unlabeled data to effectively learn an SDM of non-rigid transformations in a semi-supervised training (SST) framework. We demonstrate results applying this framework towards inter-subject registration of skull-stripped, magnetic resonance (MR) brain images. Our approach makes use of 39 labeled MR datasets to create a set of supervised registrations, which we augment with a set of over 1200 unsupervised registrations using unlabeled MRIs. Through leave-one-out cross validation, we show that SST of a non-rigid SDM results in a robust registration algorithm with significantly improved accuracy compared to standard, intensity-based registration, and does so with a 99% reduction in transformation dimensionality.