Effective diagnosis of Alzheimer's disease (AD) is of primary importance in biomedical research. Recent studies have demonstrated that neuroimaging parameters are sensitive and consistent measures of AD. In addition, genetic and demographic information have also been successfully used for detecting the onset and progression of AD. The research so far has mainly focused on studying one type of data source only. It is expected that the integration of heterogeneous data (neuroimages, demographic, and genetic measures) will improve the prediction accuracy and enhance knowledge discovery from the data, such as the detection of biomarkers. In this paper, we propose to integrate heterogeneous data for AD prediction based on a kernel method. We further extend the kernel framework for selecting features (biomarkers) from heterogeneous data sources. The proposed method is applied to a collection of MRI data from 59 normal healthy controls and 59 AD patients. The MRI data are pre-processed using tensor factorization. In this study, we treat the complementary voxel-based data and region of interest (ROI) data from MRI as two data sources, and attempt to integrate the complementary information by the proposed method. Experimental results show that the integration of multiple data sources leads to a considerable improvement in the prediction accuracy. Results also show that the proposed algorithm identifies biomarkers that play more significant roles than others in AD diagnosis.

Effective diagnosis of Alzheimer's disease (AD), the most common type of dementia in elderly patients, is of primary importance in biomedical research. Recent studies have demonstrated that AD is closely related to the structure change of the brain network, i.e., the connectivity among different brain regions. The connectivity patterns will provide useful imaging-based biomarkers to distinguish Normal Controls (NC), patients with Mild Cognitive Impairment (MCI), and patients with AD. In this paper, we investigate the sparse inverse covariance estimation technique for identifying the connectivity among different brain regions. In particular, a novel algorithm based on the block coordinate descent approach is proposed for the direct estimation of the inverse covariance matrix. One appealing feature of the proposed algorithm is that it allows the user feedback (e.g., prior domain knowledge) to be incorporated into the estimation process, while the connectivity patterns can be discovered automatically. We apply the proposed algorithm to a collection of FDG-PET images from 232 NC, MCI, and AD subjects. Our experimental results demonstrate that the proposed algorithm is promising in revealing the brain region connectivity differences among these groups.

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Disease resistance prediction using genotypic data has been widely pursued in animal as well as plant research, mostly in cases where genotypic data can be readily available for a large number of subjects. With the evolution of SNP marker genotyping technology and the consequent cost reduction for genotyping thousands of SNP markers, significant research effort is being undertaken in the statistics and machine learning community to perform efficient analysis of these multidimensional datasets. For large plant breeding programs, besides identifying biomarkers associated with disease resistance, developing accurate predictive models of the phenotype based on the genotype alone is one of the most relevant scientific goals, as it allows for efficient selection without having to grow and phenotype every individual. While the importance of interactions for understanding diseases has been shown in many studies, the majority of the existing methods are limited by considering each biomarker as an independent variable, completely ignoring complex interactions among biomarkers. In this study, logistic regression p-value, Pearson correlation and mutual information were calculated for all two-way SNP interactions with respect to the Grey Leaf Spot (GLS) disease resistance phenotype. These interactions were subsequently ranked based on these measures and the performance of the LASSO algorithm for GLS disease resistance prediction was then shown to be maximized by adding the top 10 000 two-way interactions from the logistic regression p-value based rank. The logistic regression p-value based rank also led to an error rate of more than 3 percentual points lower than not adding any interaction and more than 3.5 percentual points lower than adding interactions chosen at random.

Data Mining is the process of extracting useful knowledge from large set of data. There are number of data mining techniques available to find hidden knowledge from huge set of Data. Among these techniques classification is one of the techniques to predict the class label for unknown data based on previously known class labeled dataset. Several classification techniques like decision tree induction, Naivy Bayes model, rough set approach, fuzzy set theory and neural network are used for pattern extraction. Now a day's most of the real world data stored in relational database but the decision tree induction method is used to find knowledge from flat data relations only, but can't discover pattern from relational database. So to extract multi-relational pattern from relational tables we use MRDTL approach. In real world Missing value problem are common in many data mining application. This paper provides survey of multi-relational decision tree learning algorithm to discover hidden multi-relational pattern from relational data sets and also includes some simple technique to deal with missing value.