Nagiza F. Samatova

Classical machine learning models typically try to optimize the model based on the most discriminatory features of the data; however, they do not usually account for end user preferences. In certain applications, this can be a serious issue as models not aware of user preferences could become costly, untrustworthy, or privacy‐intrusive to use, thus becoming irrelevant and/or uninterpretable. Ideally, end users with domain knowledge could propose preferable features that the predictive model could then take into account. In this paper, we propose a generic modeling method that respects end user preferences via a relative ranking system to express multi‐criteria preferences and a regularization term in the model's objective function to incorporate the ranked preferences. In a more generic perspective, this method is able to plug user preferences into existing predictive models without creating completely new ones. We implement this method in the context of decision trees and are able to achieve a comparable classification accuracy while reducing the use of undesirable features.

Toward Personalized Network Biomarkers in Alzheimer's Disease: Computing Individualized Genomic and Protein Crosstalk Maps

Alzheimer’s disease (AD) is a major public health threat; however, despite decades of research, the disease mechanisms are not completely understood, and there is a significant dearth of predictive biomarkers. The availability of systems biology approaches has opened new avenues for understanding disease mechanisms at a pathway level. However, to the best of our knowledge, no prior study has characterized the nature of pathway crosstalks in AD, or examined their utility as biomarkers for diagnosis or prognosis. In this paper, we build the first computational crosstalk model of AD incorporating genetics, antecedent knowledge, and biomarkers from a national study to create a generic pathway crosstalk reference map and to characterize the nature of genetic and protein pathway crosstalks in mild cognitive impairment (MCI) subjects. We perform initial studies of the utility of incorporating these crosstalks as biomarkers for assessing the risk of MCI progression to AD dementia. Our analysis identified Single Nucleotide Polymorphism-enriched pathways representing six of the seven Kyoto Encyclopedia of Genes and Genomes pathway categories. Integrating pathway crosstalks as a predictor improved the accuracy by 11.7% compared to standard clinical parameters and apolipoprotein E ε4 status alone. Our findings highlight the importance of moving beyond discrete biomarkers to studying interactions among complex biological pathways.

Anomaly detection is an important problem with multiple applications, and thus has been studied for decades in various research domains. In the past decade there has been a growing interest in anomaly detection in data represented as networks, or graphs, largely because of their robust expressiveness and their natural ability to represent complex relationships. Originally, techniques focused on anomaly detection in static graphs, which do not change and are capable of representing only a single snapshot of data. As real‐world networks are constantly changing, there has been a shift in focus to dynamic graphs, which evolve over time.In this survey, we aim to provide a comprehensive overview of anomaly detection in dynamic networks, concentrating on the state‐of‐the‐art methods. We first describe four types of anomalies that arise in dynamic networks, providing an intuitive explanation, applications, and a concrete example for each. Having established an idea for what constitutes an anomaly, a general two‐stage approach to anomaly detection in dynamic networks that is common among the methods is presented. We then construct a two‐tiered taxonomy, first partitioning the methods based on the intuition behind their approach, and subsequently subdividing them based on the types of anomalies they detect. Within each of the tier one categories—community, compression, decomposition, distance, and probabilistic model based—we highlight the major similarities and differences, showing the wealth of techniques derived from similar conceptual approaches.WIREs Comput Stat2015, 7:223–247. doi: 10.1002/wics.1347This article is categorized under: Algorithms and Computational Methods > Algorithms Data: Types and Structure > Graph and Network Data Statistical Learning and Exploratory Methods of the Data Sciences > Pattern Recognition