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dadp

dsrem

Dynamic Spatial Random Effects Model

fgwas

FGWAS (Functional Genome Wide Association analysiS) is a Python coding based package for imaging genetic analysis. Functional phenotypes (e.g., subcortical surface representation), which commonly arise in imaging genetic studies, have been used to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. However, existing statistical methods largely ignore the functional features (e.g., functional smoothness and correlation). The aim of this toolbox is to develop a functional genome-wide association analysis framework to efficiently carry out whole-genome analyses of functional phenotypes. FGWAS consists of three components: a multivariate varying coefficient model, a global sure independence screening procedure, and a test procedure. Compared with the standard multivariate regression model, the multivariate varying coefficient model explicitly models the functional features of functional phenotypes through the integration of smooth coefficient functions and functional principal component analysis. Statistically, compared with existing methods for genomewide association studies (GWAS), FGWAS can substantially boost the detection power for discovering important genetic variants influencing brain structure and function. Simulation studies show that FGWAS outperforms existing GWAS methods for searching sparse signals in an extremely large search space, while controlling for the family-wise error rate.

fhfrm

Functional Hybrid Factor Regression Model

ghmm_long

Gaussian hidden Markov model (Long) is a Python based package for statistical nD (n=2,3) longitudinal imaging spatial heterogeneity analysis. A Gaussian hidden Markov model is introduced to build the spatial heterogeneity of imaging intensity across different patients. The statistical inference results are used in diseased region detection for each subject.

ghmm_nd

Gaussian hidden Markov model (nD) is a Matlab based package for statistical nD (n=2,3) imaging spatial heterogeneity analysis. A Gaussian hidden Markov model is introduced to build the spatial heterogeneity of imaging intensity across different patients. The statistical inference results are used in diseased region detection in both subject level and population level.

glioblastoma

lmfe

Learning based Multi-scale Feature Engineering in Partial Discharge Detection

matlab-runtime

MATLAB runtime for Linux

mfsda

Multivariate Functional Shape Data Analysis (MFSDA) is a Matlab based package for statistical shape analysis. A multivariate varying coefficient model is introduced to build the association between the multivariate shape measurements and demographic information and other clinical variables. Statistical inference, i.e., hypothesis testing, is also included in this package, which can be used in investigating whether some covariates of interest are significantly associated with the shape information. The hypothesis testing results are further used in clustering based analysis, i.e., significant suregion detection. This MFSDA package is developed by Chao Huang and Hongtu Zhu from the BIG-S2 lab.

mfsda_python

Multivariate Functional Shape Data Analysis in Python (MFSDA_Python) is a Python based package for statistical shape analysis. A multivariate varying coefficient model is introduced to build the association between the multivariate shape measurements and demographic information and other clinical, biological variables. Statistical inference, i.e., hypothesis testing, is also included in this package, which can be used in investigating whether some covariates of interest are significantly associated with the shape information. The hypothesis testing results are further used in clustering based analysis, i.e., significant suregion detection. This MFSDA package is developed by Chao Huang and Hongtu Zhu from the BIG-S2 lab.

sbisis

Standardized Ball Information Sure Independence Screening

sigma-lab

slicersalt

An open-source, free comprehensive software that will allow biomedical scientists to precisely locate shape changes in their imaging studies. This software called Slicer Shape AnaLysis Toolbox (SlicerSALT), will enhance the intuitiveness and ease of use for such studies, as well as allow researchers to find shape changes with higher statistical power. Altogether this constitutes a crucial resource for the imaging field that will enable many and important new findings in biomedical imaging studies.

svcmem