Functional data for the diagnosis of patellofemoral laxity obtained by MRI during quadriceps isometric contraction.

Patellofemoral instability is related to anatomy. Magnetic resonance imaging (MRI) provides anatomic detail, but spoiled gradient echo (SPGR) imaging during isometric quadriceps contraction provides objective functional data for diagnosing patellofemoral laxity. Knee MRI studies and medical charts of 398 patients were retrospectively reviewed.

Classification approaches for microarray gene expression data analysis.

Classification approaches have been developed, adopted, and applied to distinguish disease classes at the molecular level using microarray data. Recently, a novel class of hierarchical probabilistic models based on a kernel-imbedding technique has become one of the best classification tools for microarray data analysis. These models were first developed as kernel-imbedded Gaussian processes (KIGPs) for binary class classification problems using microarray gene expression data, then they were further improved for multiclass classification problems under a unifying Bayesian framework.

Multiclass kernel-imbedded Gaussian processes for microarray data analysis.

Identifying significant differentially expressed genes of a disease can help understand the disease at the genomic level. A hierarchical statistical model named multiclass kernel-imbedded Gaussian process (mKIGP) is developed under a Bayesian framework for a multiclass classification problem using microarray gene expression data. Specifically, based on a multinomial probit regression setting, an empirically adaptive algorithm with a cascading structure is designed to find appropriate featuring kernels, to discover potentially significant genes, and to make optimal tumor/cancer class predictions.

A comparison of two systems for entering and assessing dietary data for a research study.

To reduce the cost of analyzing dietary data for research studies, we evaluated the accuracy of an entry and assessment system that could be used by trained food and nutrition professionals who did not routinely perform this type of task. We compared intakes from 24-hour recalls for 175 adult women and 185 schoolchildren using two methods for entry of dietary data. For the standard method, registered dietitians who routinely evaluate dietary data entered the recalls using a professional data entry program, RapidCalc.

Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data.

Background: Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more important for our understanding of diseases at genomic level. Although many machine learning methods have been developed and applied to the area of microarray gene expression data analysis, the majority of them are based on linear models, which however are not necessarily appropriate for the underlying connection between the target disease and its associated explanatory genes. Linear model based methods usually also bring in false positive significant features more easily.

Patterns of childhood cancer in Hawai'i between 1975 and 2000.

This study investigated the 25-year incidence of childhood cancer in Hawai'i, including sex, age, and ethnic differences and time trends. Leukemia was the most common diagnosis. Japanese in Hawai'i have lower pediatric cancer rates than for the United States.

New probabilistic graphical models for genetic regulatory networks studies.

This paper introduces two new probabilistic graphical models for reconstruction of genetic regulatory networks using DNA microarray data. One is an independence graph (IG) model with either a forward or a backward search algorithm and the other one is a Gaussian network (GN) model with a novel greedy search method. The performances of both models were evaluated on four MAPK pathways in yeast and three simulated data sets.

Use of runs statistics for pattern recognition in genomic DNA sequences.

In this article, the use of the finite Markov chain imbedding (FMCI) technique to study patterns in DNA under a hidden Markov model (HMM) is introduced. With a vision of studying multiple runs-related statistics simultaneously under an HMM through the FMCI technique, this work establishes an investigation of a bivariate runs statistic under a binary HMM for DNA pattern recognition. An FMCI-based recursive algorithm is derived and implemented for the determination of the exact distribution of this bivariate runs statistic under an independent identically distributed (IID) framework, a Markov chain (MC) framework, and a binary HMM framework.