Bayesian network modeling (BN modeling, or BNM) is an interpretable machine learning method for constructing probabilistic graphical models from the data. In recent years, it has been extensively applied to diverse types of biomedical data sets. Concurrently, our ability perform long-time scale molecular dynamics (MD) simulations on proteins and other materials increased exponentially. However, analysis MD simulation trajectories not data-driven but rather dependent user's prior knowledge...

Identifying target-specific drugs remains a challenge in pharmacology, especially for highly homologous proteins such as dopamine receptors D2R and D3R. Differences cryptic druggable sites arise from the distinct conformational ensembles underlying their dynamic behavior. While Molecular Dynamics (MD) simulations has emerged powerful tool dissecting protein dynamics, sheer volume of MD data requires scalable unbiased analysis strategies to pinpoint residue communities regulating state...

Bayesian Network (BN) modeling is a prominent methodology in computational systems biology. However, the incommensurability of datasets frequently encountered life science domains gives rise to contextual dependence and numerical irregularities behavior model selection criteria (such as MDL, Minimum Description Length) used BN reconstruction. This renders features, first foremost dependency strengths, incomparable difficult interpret. In this study, we derive evaluate principle that...

Data on biological mechanisms of aging are mostly obtained from cross-sectional study designs. An inherent disadvantage this design is that inter-individual differences can mask small but biologically significant age-dependent changes. A serially sampled (same individual at different time points) would overcome problem often limited by the relatively numbers available paired samples and statistics being used. To these limitations, we have developed a new vector-based approach, termed...

Bayesian network (BN) reconstruction is a prototypical systems biology data analysis approach that has been successfully used to reverse engineer and model networks reflecting different layers of biological organization (ranging from genetic epigenetic cellular pathway metabolomic). It especially relevant in the context modern (ongoing prospective) studies generate heterogeneous high-throughput omics datasets. However, there are both theoretical practical obstacles seamless application BN...

Cancer immunotherapy, specifically immune checkpoint blockade, has been found to be effective in the treatment of metastatic cancers. However, only a subset patients achieve clinical responses. Elucidating pretreatment biomarkers predictive sustained response is major research priority. Another priority evaluating changes system before and after responders vs. nonresponders. Our group studying networks as an accurate reflection global state. Flow cytometry (FACS, fluorescence-activated cell...

Significance We report here that a recently developed Bayesian network (BN) methodology and software platform yield useful information when applied to the analysis of intrachromosomal interaction datasets combined with Encyclopedia DNA Elements publicly available for B-lymphocyte cell line GM12878. Of 106 variables analyzed, strength between segments was found be directly dependent on only four types variables: distance, Rad21 or SMC3 (cohesin components), transcription at start sites,...

<abstract><p>Bayesian Network (BN) modeling is a prominent and increasingly popular computational systems biology method. It aims to construct network graphs from the large heterogeneous biological datasets that reflect underlying relationships. Currently, variety of strategies exist for evaluating BN methodology performance, ranging utilizing artificial benchmark models, specialized datasets, simulation studies generate synthetic data predefined models. The last arguably most...

Modern artificial neural networks (ANNs) have long been designed on foundations of mathematics as opposed to their original biomimicry. However, the structure and function these modern ANNs are often analogous real-life biological networks. We propose that ubiquitous information-theoretic principles underlying development similar guiding macro-evolution insights gained from one field can be applied other. generate hypotheses bow-tie network Janus kinase - signal transducers activators...

While there are currently over 40 replicated genes with mapped risk alleles for Late Onset Alzheimer's disease (LOAD), the Apolipoprotein E locus E4 haplotype is still biggest driver of risk, odds ratios neuropathologically confirmed E44 carriers exceeding 30 (95% confidence interval 16.59-58.75). We sought to address whether APOE modifies expression globally through networks increase LOAD risk. have used Human Brainome data build comparing non-carriers using scalable mixed-datatypes...

Background: Bayesian Network (BN) modeling is a prominent methodology in computational systems biology. However, the incommensurability of datasets fre- quently encountered life science domains gives rise to contextual dependence and numerical irregularities behavior model selection criteria (such as MDL, Minimum Description Length) used BN reconstruction. This renders features, first foremost dependency strengths, incomparable diffi- cult interpret. In this study, we derive evaluate...

Bayesian network modeling (BN modeling, or BNM) is an interpretable machine learning method for constructing probabilistic graphical models from the data. In recent years, it has been extensively applied to diverse types of biomedical datasets. Concurrently, our ability perform long-timescale molecular dynamics (MD) simulations on proteins and other materials increased exponentially. However, analysis MD simulation trajectories not data-driven but rather dependent user's prior knowledge...

The identity/recognition of tRNAs, in the context aminoacyl tRNA synthetases (and other molecules), is a complex phenomenon that has major implications ranging from origins and evolution translation machinery genetic code to speciation tRNAs themselves human mitochondrial diseases artificial engineering. Deciphering it via laboratory experiments, however, difficult necessarily time- resource-consuming. In this study, we propose mathematically rigorous two-pronged silico approach identifying...

Abstract In this article we present a nonseparable multiresolution structure based on frames which is defined by radial frame scaling functions. The Fourier transform of these functions the indicator (characteristic) function measurable set. We also construct resulting multiwavelets, can be isotropic as well. Our construction carried out in any number dimensions and for big variety dilation matrices.

We report on electric and magnetic nondestructive testing (NDT) of proton exchange membrane (PEM) fuel cells. Fuel cells are electrochemical devices that convert hydrogen oxygen gas into water, heat useable electricity. cell health can affect the overall performance lifetime. have explored several NDT techniques employing highly sensitive HTS LTS SQUID fluxgate magnetometers. Magnetic fields generated by currents flowing in studied spatial, time frequency domain under various operating...

We propose a novel two-stage analysis strategy to discover candidate genes associated with the particular cancer outcomes in large multimodal genomic cancers databases, such as The Cancer Genome Atlas (TCGA). During first stage, we use mixed mutual information perform variable selection; during second scalable Bayesian network (BN) modeling identify and their interactions. Two crucial features of proposed approach are (i) ability handle data types (continuous discrete, genomic, epigenomic,...

Abstract In addition to genetic variation, epigenetic variation and transposons can greatly affect the evolutionary fitnesses landscape gene expression. Previously we proposed a mathematical treatment of general model that called Stochastic Epigenetic Modification (SEM) model. this study follow up with special case, Transposon Silencing Model (TSM), with, once again, emphasis on quantitative treatment. We have investigated effects changes due transposon (T) insertions; in particular,...

The motion of the high temperature front during combustion synthesis ferrite materials generates residual magnetization in cylindrical product samples. wave created a current density up to 10 A/cm2, which influenced distribution. measured peak magnetic field intensity was 8 μT. Qualitatively different maps were generated samples synthesized by modes. average vector either planar or pulsating oriented at smaller angle with respect pellet axis (φ⩽45°) than those spin (60°⩽φ⩽80°). We estimate...

In this paper study, we develop a Bayesian Network model selection principle that address addresses the incommensurability of network features obtained from incongruous datasets and overcomes performance irregularities Minimum Description Length principle. This is achieved (i) by approaching evaluation as classification problem, (ii) estimating effect sampling error has on satisfiability conditional independence criterion, reflected Mutual Information, (iii) utilizing estimate to penalize...