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Research describing data mining by means of data bases represented as graphs is presented. In particular the use of the Subdue system for pattern discovery is investigated.

In this paper we study the inference of node and edge replacement graph grammars. We search for frequent subgraphs and then check for an overlap among the instances of the subgraphs in the input graph. If the subgraphs overlap by one node, we propose a node replacement graph grammar production. If the subgraphs overlap by two nodes or two nodes and an edge, we propose an edge replacement graph grammar production. We can also infer a hierarchy of productions by compressing portions of a graph described by a production and then inferring new productions on the compressed graph. We validate the approach in experiments where we generate graphs from known grammars and measure how well the approach infers the original grammar from the generated graph. We show graph grammars found in biological molecules, biological networks, and analyze learning curves of the algorithm. [ABSTRACT FROM AUTHOR]

Focuses on how to realize the benefits of domain-dependent discovery approaches in artificial intelligence by adding domain-specific knowledge to domain-independent discovery system. Benefits of using domain-specific information; Features of the minimum description length principle; Features of the Subdue system; Scalability of structure discovery using the Subdue system.

A preface for the December 1, 2006 of the issue of the "International Journal on Artificial Intelligence Tools" is presented.

Washington State University (WSU) is using hi-tech solutions to address One Health challenges in veterinary medicine. They have integrated digital pathology and AI to improve the speed and efficiency of generating diagnostic reports. WSU is also developing algorithms to automate the reading of chronic wasting disease surveillance slides and using deep learning for health monitoring and disease diagnosis in veterinary species. Additionally, WSU is working on innovative technologies to prevent rabies, including thermotolerant vaccines and facial recognition technology for identifying vaccinated dogs. These advancements have the potential to enhance diagnostic precision and outcomes in veterinary medicine. [Extracted from the article]

Algorithms for automated novelty detection and management are of growing interest but must address the inherent uncertainty from variations in non-novel environments while detecting the changes from the novelty. This paper expands on a recent unified framework to develop an operational theory for novelty that includes multiple (sub)types of novelty. As an example, this paper explores the problem of multi-type novelty detection in a 3D version of CartPole, wherein the cart Weibull-Open-World control-agent (WOW-agent) is confronted by different sub-types/levels of novelty from multiple independent agents moving in the environment. The WOW-agent must balance the pole and detect and characterize the novelties while adapting to maintain that balance. The approach develops static, dynamic, and prediction-error measures of dissimilarity to address different signals/sources of novelty. The WOW-agent uses the Extreme Value Theory, applied per dimension of the dissimilarity measures, to detect outliers and combines different dimensions to characterize the novelty. In blind/sequestered testing, the system detects nearly 100% of the non-nuisance novelties, detects many nuisance novelties, and shows it is better than novelty detection using a Gaussian-based approach. We also show the WOW-agent's lookahead collision avoiding control is significantly better than a baseline Deep-Q-learning Networktrained controller. [ABSTRACT FROM AUTHOR]

Hierarchical conceptual clustering has proven to be a useful,although under-explored,data mining technique. A graph-based representation of structural information combined with a substructure discovery technique has been shown to be successful in knowledge discovery. The SUBDUE substructure discovery system provides one such combinat on of approaches. This work presents SUBDUE and the development of its clustering functionalities. Several examples are used to illustrate the validity of the approach both in structured and unstructured domains,as well as to compare SUBDUE to the Cobweb clustering algorithm. We also develop a new metric for comparing structurally-def ned clusterings. Results show that SUBDUE successfully discovers hierarchical clusterings in both structured and unstructured data. [ABSTRACT FROM AUTHOR]

Three successive multiple generations of rats were exposed to different toxicants and then bred to the transgenerational F5 generation to assess the impacts of multiple generation different exposures. The current study examines the actions of the agricultural fungicide vinclozolin on the F0 generation, followed by jet fuel hydrocarbon mixture exposure of the F1 generation, and then pesticide dichlorodiphenyltrichloroethane on the F2 generation gestating females. The subsequent F3 and F4 generations and F5 transgenerational generation were obtained and F1-F5 generations examined for male sperm epigenetic alterations and pathology in males and females. Significant impacts on the male sperm differential DNA methylation regions were observed. The F3-F5 generations were similar in ∼50% of the DNA methylation regions. The pathology of each generation was assessed in the testis, ovary, kidney, and prostate, as well as the presence of obesity and tumors. The pathology used a newly developed Deep Learning, artificial intelligence-based histopathology analysis. Observations demonstrated compounded disease impacts in obesity and metabolic parameters, but other pathologies plateaued with smaller increases at the F5 transgenerational generation. Observations demonstrate that multiple generational exposures, which occur in human populations, appear to increase epigenetic impacts and disease susceptibility., Competing Interests: None declared., (© The Author(s) 2023. Published by Oxford University Press.)

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 paper, we aim to extract behavior markers from two modalities, a smart home environment and a custom digital memory notebook app, for mapping to ten 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.

Background: Behavior and health are inextricably linked. As a result, continuous wearable sensor data offer the potential to predict clinical measures. However, interruptions in the data collection occur, which create a need for strategic data imputation., Objective: The objective of this work is to adapt a data generation algorithm to impute multivariate time series data. This will allow us to create digital behavior markers that can predict clinical health measures., Methods: We created a bidirectional time series generative adversarial network to impute missing sensor readings. Values are imputed based on relationships between multiple fields and multiple points in time, for single time points or larger time gaps. From the complete data, digital behavior markers are extracted and are mapped to predicted clinical measures., Results: We validate our approach using continuous smartwatch data for n  = 14 participants. When reconstructing omitted data, we observe an average normalized mean absolute error of 0.0197. We then create machine learning models to predict clinical measures from the reconstructed, complete data with correlations ranging from r  = 0.1230 to r  = 0.7623. This work indicates that wearable sensor data collected in the wild can be used to offer insights on a person's health in natural settings., Competing Interests: None declared., (Thieme. All rights reserved.)