Late-onset Alzheimer's Disease (LOAD) is the most common form of dementia in the elderly. Genome-wide association studies (GWAS) for LOAD have open new avenues to identify genetic causes and to provide diagnostic tools for early detection. Although several predictive models have been proposed using the few detected GWAS markers, there is still a need for improvement and identification of potential markers. Commonly, polygenic risk scores are being used for prediction. Nevertheless, other methods to generate predictive models have been suggested. In this research, we compared three machine learning methods that have been proved to construct powerful predictive models (genetic algorithms, LASSO, and step-wise) and propose the inclusion of markers from misclassified samples to improve overall prediction accuracy. Our results show that the addition of markers from an initial model plus the markers of the model fitted to misclassified samples improves the area under the receiving operative curve by around 5%, reaching~0.84, which is highly competitive using only genetic information. The computational strategy used here can help to devise better methods to improve classification models for AD. Our results could have a positive impact on the early diagnosis of Alzheimer's disease.

In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies. From digital mammograms, a semi-automated radiogenomics analysis generated 1,078 features describing the shape, signal distribution, and texture of tumors along their contralateral image used as control. From tumor biopsy, we estimated the OncotypeDX and PAM50 recurrence scores using gene expression microarrays. Then, we used multivariate analysis under stringent cross-validation to train models predicting recurrence scores. Few univariate features reached Spearman correlation coefficients above 0.4. Nevertheless, multivariate analysis yielded significantly correlated models for both signatures (correlation of OncotypeDX = 0.49 ± 0.07 and PAM50 = 0.32 ± 0.10 in stringent cross-validation and OncotypeDX = 0.83 and PAM50 = 0.78 for a unique model). Equivalent models trained from the unaffected contralateral breast were not correlated suggesting that the image signatures were tumor-specific and that overfitting was not a considerable issue. We also noted that models were improved by combining clinical information (triple negative status and progesterone receptor). The models used mostly wavelets and fractal features suggesting their importance to capture tumor information. Our results suggest that molecular-based recurrence risk and breast cancer subtypes have observable radiographic phenotypes. To our knowledge, this is the first study associating mammographic information to gene expression recurrence signatures.

Results: MRI and clinical data were obtained in 95 patients. The typical patient participating in this study was a 65-year old woman with a body mass index (BMI) of 32 kg/m 2. The bivariate association between KOOS pain and each of the DCE-MRI perfusion variables showed statistically significant correlations: S IRE x (N-plateau þ N-washout) ("Inflammation") (r ¼ À0.42, p < 0.0001), S ME x (N-plateauþN-washout) (r ¼-0.43, p < 0.0001), N-plateau þ N-washout (r ¼ À0.43, p < 0.0001), volume of the IPFP (r ¼-0.39, p ¼ 0.0001), and "Inflammation" divided by the volume of the IPFP (r ¼ À0.37, p ¼ 0.0002). A statistically significant correlation was also seen between KOOS pain and MOAKS Hoffa-synovitis (r ¼ À0.21, p ¼ 0.046). Conclusions: Perfusion variables on DCE-MRI reflecting inflammation in the IPFP and MOAKS Hoffa-synovitis were associated with knee pain in obese KOA patients. These results suggest that severe inflammation in the IPFP is associated with severe pain in KOA and that DCE-MRI is a robust and promising method to study the impact of inflammation in KOA.