An important area of data mining is anomaly detection, particularly for fraud. However, little work has been done in terms of detecting anomalies in data that is represented as a graph. In this paper we present graph-based approaches to uncovering an

A variety of environmental factors have been shown to promote the epigenetic transgenerational inheritance of disease and phenotypic variation in numerous species. Exposure to environmental factors such as toxicants can promote epigenetic changes (epimutations) involving alterations in DNA methylation to produce specific differential DNA methylation regions (DMRs). The germline (e.g. sperm) transmission of epimutations is associated with epigenetic transgenerational inheritance phenomena. The current study was designed to determine the genomic locations of environmentally induced transgenerational DMRs and assess their potential clustering. The exposure specific DMRs (epimutations) from a number of different studies were used. The clustering approach identified areas of the genome that have statistically significant over represented numbers of epimutations. The location of DMR clusters was compared to the gene clusters of differentially expressed genes found in tissues and cells associated with the transgenerational inheritance of disease. Such gene clusters, termed epigenetic control regions (ECRs), have been previously suggested to regulate gene expression in regions spanning up to 2-5 million bases. DMR clusters were often found to associate with inherent gene clusters within the genome. The current study used a number of epigenetic datasets from previous studies to identify novel DMR clusters across the genome. Observations suggest these clustered DMR within an ECR may be susceptible to epigenetic reprogramming and dramatically influence genome activity.

Deep learning involves an artificial intelligence (AI) approach and has been shown to provide superior performance for automating image recognition tasks, as well as exceeding human capabilities in both time and accuracy. Histopathology diagnostics is one of the more popular challenges at the intersection of artificial intelligence, computer vision, and medicine. Developing methods to automatically detect and identify pathologies in digitized histology slides imposes unique challenges due to the large size of these images and the complexity of the features present in biological tissue. Most methods that are capable of human-level recognition in histopathology are tuned to a specific problem since the computational complexity exceeds that of traditional image classification problems. In the current study, a deep learning approach is developed and presented that can be trained to locate and accurately classify different types of pathologies in gigapixel digitized histology slides along with completing the binary disease classification for the entire image. The approach uses a novel pyramid tiling approach to take advantage of spatial awareness around the area to be classified, while maintaining efficiency and scalability for gigapixel images. The approach is trained and validated on a wide variety of tissue types (i.e., testis, ovary, prostate, kidney) and pathologies taken from an epigenetically altered histology study at Washington State University. The newly developed procedure was optimized and validated along with comparison and validation on public histology datasets. The current developed procedure was found to be optimal and more reproducible when compared to manual procedures, and optimal to previous protocols that used fragmented tissue or slide analysis. Observations demonstrate that the deep learning histopathology analysis is significantly more efficient and accurate than standard manual histopathology analysis.

New modes of technology are offering unprecedented opportunities to unobtrusively collect data about people's behavior. While there are many use cases for such information, we explore its utility for predicting multiple clinical assessment scores. Because clinical assessments are typically used as screening tools for impairment and disease, such as mild cognitive impairment (MCI), automatically mapping behavioral data to assessment scores can help detect changes in health and behavior across time. In this article, we aim to extract behavior markers from two modalities, a smart home environment and a custom digital memory notebook app, for mapping to 10 clinical assessments that are relevant for monitoring MCI onset and changes in cognitive health. Smart-home-based behavior markers reflect hourly, daily, and weekly activity patterns, while app-based behavior markers reflect app usage and writing content/style derived from free-form journal entries. We describe machine learning techniques for fusing these multimodal behavior markers and utilizing joint prediction. We evaluate our approach using three regression algorithms and data from 14 participants with MCI living in a smart-home environment. We observed moderate to large correlations between predicted and ground-truth assessment scores, ranging from r = 0.601 to r = 0.871 for each clinical assessment.

Artificial Intelligence (AI) systems, trained in controlled environments, often struggle in real-world complexities. We propose a general framework for estimating domain complexity across diverse environments, like open-world learning and real-world applications. This framework distinguishes between intrinsic complexity (inherent to the domain) and extrinsic complexity (dependent on the AI agent). By analyzing dimensionality, sparsity, and diversity within these categories, we offer a comprehensive view of domain challenges. This approach enables quantitative predictions of AI difficulty during environment transitions, avoids bias in novel situations, and helps navigate the vast search spaces of open-world domains.