Inference Attacks on Genomic Data Based on Probabilistic Graphical Models by Zaobo He, Junxiu Zhou

“…We propose new inference attacks to predict unknown Single Nucleotide Polymorphisms (SNPs) and human traits of individuals in a familial genomic dataset based on probabilistic graphical models and belief propagation. With this method, the adversary can predict the unobserved genomes or traits of targeted individuals in a family genomic dataset where some individuals’ genomes and traits are observed, relying on SNP-trait association from Genome-Wide Association Study (GWAS), Mendel’s Laws, and statistical relations between SNPs. …”
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Anatomy Ontology Matching Using Markov Logic Networks by Chunhua Li, Pengpeng Zhao, Jian Wu, Zhiming Cui

“…Markov logic networks which unify probabilistic graphical model and first-order logic provide an excellent framework for ontology matching. …”
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Noise-robust linear prediction analysis of speech based on super-Gaussian excitation by Bin ZHOU, Xia ZOU, Xiong-wei ZHANG, Gai-hua ZHAO

“…To overcome the problem that the performance of the traditional linear prediction (LP) analysis of speech dete-riorates significantly in the presence of background noise,a novel algorithm for robust LP analysis of speech based on super-Gaussian excitation was proposed.The excitation noise of LP was modeled as a Student-t distribution,which was shown to be super-Gaussian.Then a novel probabilistic graphical model for robust LP analysis of speech was built by in-corporating the effect of additive noise explicitly.Furthermore,variational Bayesian inference was adopted to approxi-mate the intractable posterior distributions of the model parameters,based on which the LP coefficients of the noisy speech were estimated iteratively.The experimental results show that the developed algorithm performs well in terms of LP coefficients estimation of speech and is much more robust to ambient noise than several other algorithms.…”
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Progressive Detection of Uncertainty in Natural Language Processing Using a Labeled Variable Dimension Kalman Filter by Lingaraj K., S. Supreeth, Yerriswamy T., Dayananda P., Rohith S., Shruthi G.

“…This work focuses on a particular method for detecting uncertainty, termed progressive detection, which can be accomplished using a dynamic probabilistic graphical model to solve these challenges. We iteratively upgraded the Kalman Filter to Labeled Variable Dimension Kalman Filter (LVDKF) based on observations from real datasets. …”
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Automatic Emergence Detection in Complex Systems by Eugene Santos, Yan Zhao

“…This framework, based on a probabilistic graphical model called Bayesian Knowledge Bases (BKBs), learns individual subsystem dynamics from data, probabilistically and structurally fuses said dynamics into a single complex system dynamics, and detects emergent properties. …”
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AI-based analysis of fetal growth restriction in a prospective obstetric cohort quantifies compound risks for perinatal morbidity and mortality and identifies previously unrecogniz... by Raquel M. Zimmerman, Edgar J. Hernandez, Mark Yandell, Martin Tristani-Firouzi, Robert M. Silver, William Grobman, David Haas, George Saade, Jonathan Steller, Nathan R. Blue

“…Our objective was to use data from a large, deeply phenotyped observational obstetric cohort to develop a probabilistic graphical model (PGM), a type of “explainable artificial intelligence (AI)”, as a potential framework to better understand how interrelated variables contribute to perinatal morbidity risk in FGR. …”
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