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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Environmental Factors Associated With Fall Phytoplankton Blooms in the Northern Bering and Chukchi Seas

This study investigates environmental drivers of fall phytoplankton blooms in the Arctic, focusing on the northern Bering and Chukchi seas. Random Forests models were used to analyze covariates of fall phytoplankton blooms from 2013 to 2018, incorporating shipboard, remote sensing, and modeled environmental properties. Four regional models and one comprehensive all-station model considered fall as well as midsummer conditions. Midsummer properties included suspended particulate matter, chlorophyll-a, and the proportion of degraded pheophytin to chlorophyll-a used as a proxy for bloom stage. Open water duration was one of the highest ranked factors in predicting fall blooms. Open water duration also influences the stage of midsummer (July) blooms as indicated by pheophytin proportions, which in turn were the highest-ranked factor for predicting fall bloom events in the Chirikov Basin (northern Bering Sea between St. Lawrence Island and the Bering Strait) and the Chukchi Sea. Wind direction, specifically easterly winds, was an important predictor in the northern Bering Sea. Maximum wind speed ranked highly at stations located within the nutrient-poor Alaska Coastal Current in the Chukchi Sea. However, stormy days, average and maximum wind speeds generally ranked low in importance as a predictor of fall bloom events. Other parameters, including photosynthetic active radiation, modeled nutrient concentrations, mixed layer depth, and time since sea ice breakup date showed strong but regionally varying relationships with fall blooms. Altogether, results from these Random Forests models suggest that high wind events and storms in the absence of sea ice provide an incomplete narrative for initiating fall bloom events.

Gaffey, C. B. [Clark University, Worcester, MA (Un↗
DOE OSTI2025

Environmental Controls on Water Vapor Deuterium Excess in the Coastal Boundary Layer: An Information Theory Perspective

We use information theory to quantify the environmental controls on water vapor deuterium excess (D-excess) in coastal Southern California from June 2023 through February 2024. Using Shannon entropy, mutual information (MI), and joint mutual information, metrics that capture both linear and nonlinear relationships, we identify the most informative variables and variable combinations governing D-excess across contrasting marine and continental regimes. Relative humidity with respect to sea surface temperature (RHS) is consistently the strongest individual predictor, explaining up to 27% of D-excess variability during marine conditions but only 10% in continental air masses. The Relative humidity(RHS) + sea surface temperature (SST) combination demonstrates synergistic effects, where their joint influence (explaining up to 36% of D-excess variability) exceeds what either variable achieves individually, confirming their coupled influence on deuterium excess. Wind direction complements RHS most effectively during continental conditions. The best three-variable combination (RHS + SST + Planetary Boundary Layer height) explains 38% of D-excess variability in marine air, while no combination exceeds 20% explanatory power during continental periods. Information theory shows that heteroscedasticity in D-excess relationships indicates regime shifts in controlling processes and quantifies fundamental constraints on predictor variables: some environmental factors like surface pressure or water vapor flux contain insufficient information content to explain D-excess variability regardless of their physical relevance. These results highlight the different predictability limits between marine and continental regimes, challenging the adequacy of linear models and providing a rigorous framework for quantifying the information content of isotope-climate relationships with implications for both modern and paleoclimate applications.

information theory↗
DOE OSTI2026

Dominant Controls on Preferential Flow and Their Implications for Future Soil Water Fluxes

Abstract Soil water flow, particularly preferential flow (PF), is a critical control on hydrological and biogeochemical processes, including groundwater recharge, contaminant transport, and carbon cycling. However, it remains challenging to predict PF occurrence across large environmental gradients. Here, we developed a deep learning (DL) model to estimate event‐scale soil water flow velocity and the probability of PF occurrence using high‐frequency soil moisture and precipitation data from 33 sites across the National Ecological Observatory Network. The model demonstrated high skill in predicting the binary occurrence of PF (91% F1‐score; 85% accuracy) but the performance was limited in predicting soil water velocity ( R 2 = 0.31). We found that precipitation characteristics (duration, volume, and intensity) were the most important predictors for soil water velocity. Among the non‐precipitation event variables, sand content showed relatively high predictive skill, though differences among non‐event climate variables were generally modest. Lower sand content was associated with increased predicted soil water velocity, a finding that highlights the role of soil structure in producing more non‐uniform flow, which contrasts with traditional uniform flow models. Projecting a reduced DL model under both moderate and high‐emissions future climate scenarios (2060–2099 Representative Concentration Pathways 4.5 and 8.5), we found ∼7.3% increase under RCP4.5 and ∼15% under RCP8.5 of soil water velocities compared to the historical simulation, while modeled likelihood of PF changed little. These findings suggest climate change is not making PF more frequent, but it is making existing PF pathways more efficient with important consequences for associated nutrient and contaminant transport under climate change. Plain Language Summary Water movement in soil is critical for water quality. While often modeled as a uniform flow process, in reality water moves rapidly through cracks and burrows in what is called “preferential flow” (PF), which limits natural filtration and can transport pollutants. We developed a deep learning model, trained on data from 33 U.S. sites, to predict when and how fast this PF occurs based on precipitation, soil, and climate data. The model showed that precipitation characteristics (duration, intensity, volume) were the most important predictors of PF. Lower soil sand content/higher clay content was associated with faster water flow, likely due to clay soils forming aggregates and cracks that water moves through rather than infiltrating uniformly. Further analyses based on climate projections suggest that the speed at which PF occurs will become more rapid under future climate scenarios compared to historical simulation. This highlights the need to represent PF in soil water models when assessing future water quality. Key Points The effect of precipitation peak intensity on soil water velocities declined with increasing precipitation intensity Antecedent soil moisture failed to predict preferential flow (PF), contrasting the high predictive power of sand content Climate predictions suggest that soil water velocities through PF paths will increase ∼15% by 2099

Li, Bonan↗
DOE OSTI2020

Quantifying the drivers and predictability of seasonal changes in African fire

Africa contains some of the most vulnerable ecosystems to fires. Successful seasonal prediction of fire activity over these fire-prone regions remains a challenge and relies heavily on in-depth understanding of various driving mechanisms underlying fire evolution. Here, we assess the seasonal environmental drivers and predictability of African fire using the analytical framework of Stepwise Generalized Equilibrium Feedback Assessment (SGEFA) and machine learning techniques (MLTs). The impacts of sea-surface temperature, soil moisture, and leaf area index are quantified and found to dominate the fire seasonal variability by regulating regional burning condition and fuel supply. Compared with previously-identified atmospheric and socioeconomic predictors, these slowly evolving oceanic and terrestrial predictors are further identified to determine the seasonal predictability of fire activity in Africa. Our combined SGEFA-MLT approach achieves skillful prediction of African fire one month in advance and can be generalized to provide seasonal estimates of regional and global fire risk.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Basin-wide variation in tree hydraulic safety margins predicts the carbon balance of Amazon forests

Abstract Tropical forests face increasing climate risk 1,2 , yet our ability to predict their response to climate change is limited by poor understanding of their resistance to water stress. Although xylem embolism resistance thresholds (for example, $$\varPsi $$ Ψ 50 ) and hydraulic safety margins (for example, HSM 50 ) are important predictors of drought-induced mortality risk 3–5 , little is known about how these vary across Earth’s largest tropical forest. Here, we present a pan-Amazon, fully standardized hydraulic traits dataset and use it to assess regional variation in drought sensitivity and hydraulic trait ability to predict species distributions and long-term forest biomass accumulation. Parameters $$\varPsi $$ Ψ 50 and HSM 50 vary markedly across the Amazon and are related to average long-term rainfall characteristics. Both $$\varPsi $$ Ψ 50 and HSM 50 influence the biogeographical distribution of Amazon tree species. However, HSM 50 was the only significant predictor of observed decadal-scale changes in forest biomass. Old-growth forests with wide HSM 50 are gaining more biomass than are low HSM 50 forests. We propose that this may be associated with a growth–mortality trade-off whereby trees in forests consisting of fast-growing species take greater hydraulic risks and face greater mortality risk. Moreover, in regions of more pronounced climatic change, we find evidence that forests are losing biomass, suggesting that species in these regions may be operating beyond their hydraulic limits. Continued climate change is likely to further reduce HSM 50 in the Amazon 6,7 , with strong implications for the Amazon carbon sink.

Science & Technology - Other Topics↗
DOE OSTI2019

Gene expression imputation across multiple brain regions provides insights into schizophrenia risk.

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.

Huckins, Laura M↗
DOE OSTI2022

Bioplastic design using multitask deep neural networks

Abstract Non-degradable plastic waste jeopardizes our environment, yet our modern lifestyle and current technologies are impossible to sustain without plastics. Bio-synthesized and biodegradable alternatives such as polyhydroxyalkanoates (PHAs) have the potential to replace large portions of the world’s plastic supply with cradle-to-cradle materials, but their chemical complexity and diversity limit traditional resource-intensive experimentation. Here, we develop multitask deep neural network property predictors using available experimental data for a diverse set of nearly 23,000 homo- and copolymer chemistries. Using the predictors, we identify 14 PHA-based bioplastics from a search space of almost 1.4 million candidates which could serve as potential replacements for seven petroleum-based commodity plastics that account for 75% of the world’s yearly plastic production. We also discuss possible synthesis routes for the identified promising materials.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Integrated analysis reveals that STAT3 is central to the crosstalk between HER/ErbB receptor signaling pathways in human mammary epithelial cells

Human epidermal growth factor receptors (HER, also known as ErbB) drive cellular proliferation, pro-survival and stress responses by activating several downstream kinases, in particular ERK, p38, JNK (SAPK), the PI3K/AKT, as well as various transcriptional regulators such as STAT3. When co-expressed, first three members of HER family (HER1-3) can form homo- and hetero-dimers. Based on the considerable evidence which suggest that every receptor dimer activates intracellular signaling pathways differentially, we hypothesized that the HER dimerization pattern is a better predictor of downstream signaling than the total receptor activation levels. We validated our hypothesis using a combination of model-based analysis to quantify the HER dimerization patterns and multi-factorial experiments where HER dimerization patterns and signaling crosstalk were rationally perturbed. We have measured the activation of HER1-3 receptors and of the sentinel signaling proteins ERK, AKT, p38, JNK, STAT3 as a function of time in a panel of human mammary epithelial (HME) cells expressing different levels of HER1-3 stimulated with various ligand combinations. Our analysis using multiple ways of clustering the activation data has confirmed that the HER receptor dimer is a better predictor of the signaling through p38, ERK and AKT pathways than the total HER receptor expression and activation levels. Targeted inhibition studies to identify the causal effects allowed us to obtain a consensus regulatory interaction model, which revealed that STAT3 occupies a central role in the crosstalk between the studied pathways.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Binding energetics of substitutional and interstitial helium and di-helium defects with grain boundary structure in α-Fe

The formation/binding energetics and length scales associated with the interaction between He atoms and grain boundaries in BCC α-Fe were explored. Ten different low Σ grain boundaries from the 〈100〉 and 〈110〉 symmetric tilt grain boundary systems were used. In this work, we then calculated formation/binding energies for 1–2 He atoms in the substitutional and interstitial sites (HeV, He{sub 2}V, HeInt, He{sub 2}Int) at all potential grain boundary sites within 15 Å of the boundary (52 826 simulations total). The present results provide detailed information about the interaction energies and length scales of 1–2 He atoms with grain boundaries for the structures examined. A number of interesting new findings emerge from the present study. For instance, the Σ3(112) twin boundary in BCC Fe possesses a much smaller binding energy than other boundaries, which corresponds in long time dynamics simulations to the ability of an interstitial He defect to break away from the boundary in simulations on the order of nanoseconds. Additionally, positive correlations between the calculated formation/binding energies of the He defects (R > 0.9) asserts that the local environment surrounding each site strongly influences the He defect energies and that highly accurate quantum mechanics calculations of lower order defects may be an adequate predictor of higher order defects. Various metrics to quantify or classify the local environment were compared with the He defect binding energies. The present work shows that the binding and formation energies for He defects are important for understanding the physics of He diffusion and trapping by grain boundaries, which can be important for modeling He interactions in polycrystalline steels.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Rare event molecular dynamics simulations of plasma induced surface ablation

The interaction of thermal Ar plasma particles with Si and W surfaces is modeled using classical molecular dynamics (MD) simulations. At plasma energies above the threshold for ablation, the ablation yield can be calculated directly from MD. For plasma energies below threshold, the ablation yield becomes exponentially low, and direct MD simulations are inefficient. Instead, we propose an integration method where the yield is calculated as a function of the Ar incident kinetic energy. Subsequent integration with a Boltzmann distribution at the temperature of interest gives the thermal ablation yield. At low plasma temperatures, the ablation yield follows an Arrhenius form in which the activation energy is shown to be the threshold energy for ablation. Interestingly, equilibrium material properties, including the surface and bulk cohesive energy, are not good predictors of the threshold energy for ablation. The surface vacancy formation energy is better, but is still not a quantitative predictor. An analysis of the trajectories near threshold shows that ablation occurs by different mechanisms on different material surfaces, and both the mechanism and the binding of surface atoms determine the threshold energy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

A non-modal analytical method to predict turbulent properties applied to the Hasegawa-Wakatani model

Linear eigenmode analysis often fails to describe turbulence in model systems that have non-normal linear operators and thus nonorthogonal eigenmodes, which can cause fluctuations to transiently grow faster than expected from eigenmode analysis. When combined with energetically conservative nonlinear mode mixing, transient growth can lead to sustained turbulence even in the absence of eigenmode instability. Since linear operators ultimately provide the turbulent fluctuations with energy, it is useful to define a growth rate that takes into account non-modal effects, allowing for prediction of energy injection, transport levels, and possibly even turbulent onset in the subcritical regime. We define such a non-modal growth rate using a relatively simple model of the statistical effect that the nonlinearities have on cross-phases and amplitude ratios of the system state variables. In particular, we model the nonlinearities as delta-function-like, periodic forces that randomize the state variables once every eddy turnover time. Furthermore, we estimate the eddy turnover time to be the inverse of the least stable eigenmode frequency or growth rate, which allows for prediction without nonlinear numerical simulation. We test this procedure on the 2D and 3D Hasegawa-Wakatani model [A. Hasegawa and M. Wakatani, Phys. Rev. Lett. 50, 682 (1983)] and find that the non-modal growth rate is a good predictor of energy injection rates, especially in the strongly non-normal, fully developed turbulence regime.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2013

THE FATE OF PLANETESIMALS IN TURBULENT DISKS WITH DEAD ZONES. I. THE TURBULENT STIRRING RECIPE

Turbulence in protoplanetary disks affects planet formation in many ways. While small dust particles are mainly affected by the aerodynamical coupling with turbulent gas velocity fields, planetesimals and larger bodies are more affected by gravitational interaction with gas density fluctuations. For the latter process, a number of numerical simulations have been performed in recent years, but a fully parameter-independent understanding has not been yet established. In this study, we present simple scaling relations for the planetesimal stirring rate in turbulence driven by magnetorotational instability (MRI), taking into account the stabilization of MRI due to ohmic resistivity. We begin with order-of-magnitude estimates of the turbulence-induced gravitational force acting on solid bodies and associated diffusion coefficients for their orbital elements. We then test the predicted scaling relations using the results of recent ohmic-resistive MHD simulations by Gressel et al. We find that these relations successfully explain the simulation results if we properly fix order-of-unity uncertainties within the estimates. We also update the saturation predictor for the density fluctuation amplitude in MRI-driven turbulence originally proposed by Okuzumi and Hirose. Combination of the scaling relations and saturation predictor allows us to know how the turbulent stirring rate of planetesimals depends on disk parameters such as the gas column density, distance from the central star, vertical resistivity distribution, and net vertical magnetic flux. In Paper II, we apply our recipe to planetesimal accretion to discuss its viability in turbulent disks.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Fully coupled simulation of cosmic reionization. II. Recombinations, clumping factors, and the photon budget for reionization

We use a fully coupled cosmological simulation including dark matter dynamics, multispecies hydrodynamics, nonequilibrium chemical ionization, flux-limited diffusion radiation transport, and a parameterized model of star formation and feedback (thermal and radiative) to investigate the epoch of hydrogen reionization in detail. In this paper, the first of several application papers, we investigate the mechanics of reionization from stellar sources forming in high-z galaxies, the utility of various formulations for the gas clumping factor on accurately estimating the effective recombination time in the intergalactic medium (IGM), and the photon budget required to achieve reionization. We also test the accuracy of the static and time-dependent models of Madau et al. as predictors of reionization completion/maintenance. We simulate a WMAP7 ΛCDM cosmological model in a 20 comoving Mpc cube, resolved with 800{sup 3} uniform fluid cells and dark matter particles. By tuning our star formation recipe to approximately match the observed high-redshift star formation rate density and galaxy luminosity function, we have created a fully coupled radiation hydrodynamical realization of hydrogen reionization, which begins to ionize at z ≈ 10 and is completed at z ≈ 5.8 without further tuning. We find that roughly two ionizing photons per H atom are required to convert the neutral IGM to a highly ionized state. After reionization concludes, we find that the quantity n-dot{sub ion}×(1 Gyr)/n{sub H} is ∼9 at z = 5, in rough agreement with measurements of the ionizing emissivity by Becker and Bolton. The complicated events during reionization that lead to this number can be generally described as inside-out, but in reality, the narrative depends on the level of ionization of the gas one attributes as being ionized. We find that the formula for the ionizing photon production rate needed to maintain the IGM in an ionized state derived by Madau et al. should not be used to predict the epoch of reionization completion because it ignores history-dependent terms in the global ionization balance which are not ignorable. We find that the time-dependent model for the ionized volume fraction Q{sub HII} is more predictive, but overestimates the redshift of reionization completion z{sub reion} by Δz ≈ 1. We propose a revised formulation of the time-dependent model that agrees with our simulation to a few percent accuracy. Finally, we use our simulation data to measure the absorption of ionizing radiation due to circumgalactic gas resolved on our mesh and find f-bar{sub esc}(CGM)≈0.7.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Faint X-ray binaries and their optical counterparts in M31

X-ray binaries (XRBs) are probes of both star formation and stellar mass, but more importantly remain one of the only direct tracers of the compact object population. To investigate the XRB population in M31, we utilized all 121 publicly available observations of M31 totalling over 1 Ms from Chandra's ACIS instrument. We studied 83 star clusters in the bulge using the year 1 star cluster catalogue from the Panchromatic Hubble Andromeda Treasury Survey. We found 15 unique star clusters that matched to 17 X-ray point sources within 1'' (3.8 pc). This population is composed predominantly of globular cluster low-mass XRBs, with one previously unidentified star cluster X-ray source. Star clusters that were brighter and more compact preferentially hosted an X-ray source. Specifically, logistic regression showed that the F475W magnitude was the most important predictor followed by the effective radius, while color (F475W–F814W) was not statistically significant. We also completed a matching analysis of 1566 H II regions and found 10 unique matches to 9 X-ray point sources within 3'' (11 pc). The H II regions hosting X-ray point sources were on average more compact than unmatched H II regions, but logistic regression concluded that neither the radius nor Hα luminosity was a significant predictor. Four matches have no previous classification and thus are high-mass XRB candidates. A stacking analysis of both star clusters and H II regions resulted in non-detections, giving typical upper limits of ≈10{sup 32} erg s{sup –1}, which probes the quiescent XRB regime.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Unbound particles in dark matter halos

We investigate unbound dark matter particles in halos by tracing particle trajectories in a simulation run to the far future (a = 100). We find that the traditional sum of kinetic and potential energies is a very poor predictor of which dark matter particles will eventually become unbound from halos. We also study the mass fraction of unbound particles, which increases strongly towards the edges of halos, and decreases significantly at higher redshifts. We discuss implications for dark matter detection experiments, precision calibrations of the halo mass function, the use of baryon fractions to constrain dark energy, and searches for intergalactic supernovae.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Toward active disruption avoidance via real-time estimation of the safe operating region and disruption proximity in tokamaks

This paper describes a real-time capable algorithm for identifying the safe operating region around a tokamak operating point. The region is defined by a convex set of linear constraints, from which the distance of a point from a disruptive boundary can be calculated. The disruptivity of points is calculated from an empirical machine learning predictor that generates the likelihood of disruption. While the likelihood generated by such empirical models can be compared to a threshold to trigger a disruption mitigation system, the safe operating region calculation enables active optimization of the operating point to maintain a safe margin from disruptive boundaries. The proposed algorithm is tested using a random forest disruption predictor fit on data from DIII-D. The safe operating region identification algorithm is applied to historical data from DIII-D showing the evolution of disruptive boundaries and the potential impact of optimization of the operating point. Real-time relevant execution times are made possible by parallelizing many of the calculation steps and implementing the algorithm on a graphics processing unit. Lastly, a real-time capable algorithm for optimizing the target operating point within the identified constraints is also proposed and simulated.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

CONNECTING STAR FORMATION QUENCHING WITH GALAXY STRUCTURE AND SUPERMASSIVE BLACK HOLES THROUGH GRAVITATIONAL HEATING OF COOLING FLOWS

Recent observations suggested that star formation quenching in galaxies is related to galaxy structure. Here we propose a new mechanism to explain the physical origin of this correlation. We assume that while quenching is maintained in quiescent galaxies by a feedback mechanism, cooling flows in the hot halo gas can still develop intermittently. We study cooling flows in a large suite of around 90 hydrodynamic simulations of an isolated galaxy group, and find that the flow development depends significantly on the gravitational potential well in the central galaxy. If the galaxy's gravity is not strong enough, cooling flows result in a central cooling catastrophe, supplying cold gas and feeding star formation to galactic bulges. When the bulge grows prominent enough, compressional heating starts to offset radiative cooling and maintains cooling flows in a long-term hot mode without producing a cooling catastrophe. Our model thus describes a self-limited growth channel for galaxy bulges and naturally explains the connection between quenching and bulge prominence. In particular, we explicitly demonstrate that M{sub ∗}/R{sub eff}{sup 1.5} is a good structural predictor of quenching. We further find that the gravity from the central supermassive black hole also affects the bimodal fate of cooling flows, and we predict a more general quenching predictor to be M{sub bh}{sup 1.6}M{sub ∗}/R{sub eff}{sup 1.5}, which may be tested in future observational studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Optimal Bayesian supervised domain adaptation for RNA sequencing data

Abstract Motivation When learning to subtype complex disease based on next-generation sequencing data, the amount of available data is often limited. Recent works have tried to leverage data from other domains to design better predictors in the target domain of interest with varying degrees of success. But they are either limited to the cases requiring the outcome label correspondence across domains or cannot leverage the label information at all. Moreover, the existing methods cannot usually benefit from other information available a priori such as gene interaction networks. Results In this article, we develop a generative optimal Bayesian supervised domain adaptation (OBSDA) model that can integrate RNA sequencing (RNA-Seq) data from different domains along with their labels for improving prediction accuracy in the target domain. Our model can be applied in cases where different domains share the same labels or have different ones. OBSDA is based on a hierarchical Bayesian negative binomial model with parameter factorization, for which the optimal predictor can be derived by marginalization of likelihood over the posterior of the parameters. We first provide an efficient Gibbs sampler for parameter inference in OBSDA. Then, we leverage the gene-gene network prior information and construct an informed and flexible variational family to infer the posterior distributions of model parameters. Comprehensive experiments on real-world RNA-Seq data demonstrate the superior performance of OBSDA, in terms of accuracy in identifying cancer subtypes by utilizing data from different domains. Moreover, we show that by taking advantage of the prior network information we can further improve the performance. Availability and implementation The source code for implementations of OBSDA and SI-OBSDA are available at the following link. https://github.com/SHBLK/BSDA. Supplementary information Supplementary data are available at Bioinformatics online.

Biochemistry & Molecular Biology↗