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Yu, Bin

Publications and source records attributed to Yu, Bin.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Learning epistatic polygenic phenotypes with Boolean interactions

Detecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests for interactions, based on a stabilized likelihood ratio test, by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that probabilisticly quantify improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline in two case studies using data from the UK Biobank: predicting red hair and multiple sclerosis (MS). In the case of predicting red hair, epiTree recovers known epistatic interactions surrounding MC1R and novel interactions, representing non-linearities not captured by logistic regression models. In the case of predicting MS, a more complex phenotype than red hair, epiTree rankings prioritize novel interactions surrounding HLA-DRB1 , a variant previously associated with MS in several populations. Taken together, these results highlight the potential for epiTree rankings to help reduce the design space for follow up experiments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Northern Hemisphere Extratropical Cyclone Activity in the Twentieth Century Reanalysis Version 3 (20CRv3) and Its Relationship with Continental Extreme Temperatures

In this study, we detect and track extratropical cyclones using 6-hourly mean sea level pressure data taken from the Twentieth Century Reanalysis version 3 (20CRv3) over the period 1951–2015 and compare them with those in the Interim and fifth generation of ECMWF reanalyses over the period 1979–2018. Three indices were employed to characterize cyclone activity, including cyclone count, cyclone intensity, and a cyclone activity index (CAI) that combines the count and intensity. The results show that the cyclone indices in the three datasets have comparable annual climatologies and seasonal evolution over the northern extratropical land and ocean in recent decades. Based on the cyclone indices over the period 1951–2010 in 80 ensemble members of 20CRv3, cyclone count and intensity are negatively correlated in winter and tend to be positively and weakly correlated in summer. The interannual CAI variability is dominated by the cyclone count variability. Regional mean cyclone activity can be well represented using the ensemble average cyclone index. We then examined the linkage of the cyclone activity in 20CRv3 and observed cold and warm extremes over Eurasia and North America over the period 1951–2010. In winter, the principal components of interannual cold and warm extreme anomalies are more correlated with the regional mean cyclone count index over Eurasia, while they are more correlated with the cyclone intensity index over North America. The temperature anomalies associated with the regional and ensemble mean cyclone count index explain about 10% (20%) of interannual cold (warm) extreme variances averaged over Eurasia. The temperature anomalies associated with the mean cyclone intensity explain about 10% of interannual cold and warm extreme variances over North America. Large-scale atmospheric circulation anomalies in association with cyclone activity and the induced temperature advection drive temperature anomalies over Eurasia and North America. In summer, circulation and thermal advection anomalies associated with cyclone activity are weak over the two continents. Hence, that season’s relationship between cyclone activity and extreme temperature variability is weak.

20CRv3↗
DOE OSTI2021

iRF v2.0

A predictive, stable, and interpretable machine learning tool: the iterative random forest algorithm (iRF). iRF discovers high-order interactions among variables with the same order of computational cost as random forests (RF). We have demonstrated the utility of iRF in several applications in the biological and environmental sciences. It is a general purpose machine learning framework for building "explainable" predictive engines.

Brown, JamesB.↗
DOE OSTI2020

Veridical data science

Building and expanding on principles of statistics, machine learning, and scientific inquiry, we propose the predictability, computability, and stability (PCS) framework for veridical data science. Our framework, composed of both a workflow and documentation, aims to provide responsible, reliable, reproducible, and transparent results across the data science life cycle. The PCS workflow uses predictability as a reality check and considers the importance of computation in data collection/storage and algorithm design. It augments predictability and computability with an overarching stability principle. Stability expands on statistical uncertainty considerations to assess how human judgment calls impact data results through data and model/algorithm perturbations. As part of the PCS workflow, we develop PCS inference procedures, namely PCS perturbation intervals and PCS hypothesis testing, to investigate the stability of data results relative to problem formulation, data cleaning, modeling decisions, and interpretations. We illustrate PCS inference through neuroscience and genomics projects of our own and others. Moreover, we demonstrate its favorable performance over existing methods in terms of receiver operating characteristic (ROC) curves in high-dimensional, sparse linear model simulations, including a wide range of misspecified models. Finally, we propose PCS documentation based on R Markdown or Jupyter Notebook, with publicly available, reproducible codes and narratives to back up human choices made throughout an analysis. The PCS workflow and documentation are demonstrated in a genomics case study available on Zenodo.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Definitions, methods, and applications in interpretable machine learning

Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for prediction, the ability to interpret what a model has learned is receiving an increasing amount of attention. However, this increased focus has led to considerable confusion about the notion of interpretability. In particular, it is unclear how the wide array of proposed interpretation methods are related and what common concepts can be used to evaluate them. We aim to address these concerns by defining interpretability in the context of machine learning and introducing the predictive, descriptive, relevant (PDR) framework for discussing interpretations. The PDR framework provides 3 overarching desiderata for evaluation: predictive accuracy, descriptive accuracy, and relevancy, with relevancy judged relative to a human audience. Moreover, to help manage the deluge of interpretation methods, we introduce a categorization of existing techniques into model-based and post hoc categories, with subgroups including sparsity, modularity, and simulatability. To demonstrate how practitioners can use the PDR framework to evaluate and understand interpretations, we provide numerous real-world examples. These examples highlight the often underappreciated role played by human audiences in discussions of interpretability. Finally, based on our framework, we discuss limitations of existing methods and directions for future work. We hope that this work will provide a common vocabulary that will make it easier for both practitioners and researchers to discuss and choose from the full range of interpretation methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Regulation of Thylakoid Lipid Biosynthesis in Plants (Final Technical Report)

The conversion of sunlight into chemical energy by photosynthesis drives life on earth as it provides food and feed, as well as industrial feed stocks for specialized chemicals and biofuels. Photosynthesis is a function of the photosynthetic membrane that forms the thylakoids inside plant chloroplasts. Thylakoid membranes harbor the protein complexes necessary for photosynthetic electron transport and ATP synthesis. These complexes are embedded into a specialized glycerolipid matrix. Two decades of research on the biochemistry of the glycerolipids of the photosynthetic membrane, their transport and remodeling in response to environmental cues and the underlying regulatory processes has provided novel fundamental insights into how photosynthetic membranes are assembled and protected in plant chloroplasts. An overview of the key findings is provided.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Metalearners for estimating heterogeneous treatment effects using machine learning

There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We describe a number of metaalgorithms that can take advantage of any supervised learning or regression method in machine learning and statistics to estimate the conditional average treatment effect (CATE) function. Metaalgorithms build on base algorithms—such as random forests (RFs), Bayesian additive regression trees (BARTs), or neural networks—to estimate the CATE, a function that the base algorithms are not designed to estimate directly. We introduce a metaalgorithm, the X-learner, that is provably efficient when the number of units in one treatment group is much larger than in the other and can exploit structural properties of the CATE function. For example, if the CATE function is linear and the response functions in treatment and control are Lipschitz-continuous, the X-learner can still achieve the parametric rate under regularity conditions. We then introduce versions of the X-learner that use RF and BART as base learners. In extensive simulation studies, the X-learner performs favorably, although none of the metalearners is uniformly the best. In two persuasion field experiments from political science, we demonstrate how our X-learner can be used to target treatment regimes and to shed light on underlying mechanisms. A software package is provided that implements our methods.

42 ENGINEERING↗
DOE OSTI2018

miR-193a inhibits osteogenic differentiation of bone marrow-derived stroma cell via targeting HMGB1

Highlights: • miR-193a inhibits osteogenic differentiation of hBMSC. • miR-193a attenuates activation of MAPK and Wnt signaling pathway. • miR-193a suppresses osteogenic differentiation partially via HMGB1. • HMGB1 greatly rescues miR-193a-inhibited activation of MAPK and Wnt signaling pathway. miR-193a has been shown to be involved in a variety of biological processes, including cell proliferation, differentiation, and apoptosis. However, little is known about how miR-193a regulates osteogenic differentiation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Iterative random forests to discover predictive and stable high-order interactions

Significance We developed a predictive, stable, and interpretable tool: the iterative random forest algorithm (iRF). iRF discovers high-order interactions among biomolecules with the same order of computational cost as random forests. We demonstrate the efficacy of iRF by finding known and promising interactions among biomolecules, of up to fifth and sixth order, in two data examples in transcriptional regulation and alternative splicing.

Science & Technology - Other Topics↗
DOE OSTI2017

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

Here, the feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis , M. sacchariflorus and M. fIoridulus , were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis , M. sacchariflorus and M. fIoridulus , respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks

Spatial gene expression patterns enable the detection of local covariability and are extremely useful for identifying local gene interactions during normal development. The abundance of spatial expression data in recent years has led to the modeling and analysis of regulatory networks. The inherent complexity of such data makes it a challenge to extract biological information. We developed staNMF, a method that combines a scalable implementation of nonnegative matrix factorization (NMF) with a new stability-driven model selection criterion. When applied to a set of Drosophila early embryonic spatial gene expression images, one of the largest datasets of its kind, staNMF identified 21 principal patterns (PP). Providing a compact yet biologically interpretable representation of Drosophila expression patterns, PP are comparable to a fate map generated experimentally by laser ablation and show exceptional promise as a data-driven alternative to manual annotations. Our analysis mapped genes to cell-fate programs and assigned putative biological roles to uncharacterized genes. Finally, we used the PP to generate local transcription factor regulatory networks. Spatially local correlation networks were constructed for six PP that span along the embryonic anterior-posterior axis. Using a two-tail 5% cutoff on correlation, we reproduced 10 of the 11 links in the well-studied gap gene network. In conclusion, the performance of PP with the Drosophila data suggests that staNMF provides informative decompositions and constitutes a useful computational lens through which to extract biological insight from complex and often noisy gene expression data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Stable knockdown of Kif5b in MDCK cells leads to epithelial–mesenchymal transition

Polarization of epithelial cells requires vectorial sorting and transport of polarity proteins to apical or basolateral domains. Kif5b is the mouse homologue of the human ubiquitous Kinesin Heavy Chain (uKHC). To investigate the function of Kif5b in epithelial cells, we examined the phenotypes of Kif5b-deficient MDCK cells. Stable knockdown of Kif5b in MDCK cells resulted in reduced cell proliferation rate, profound changes in cell morphology, loss of epithelial cell marker, and gain of mesenchymal marker, as well as increased cell migration, invasion, and tumorigenesis abilities. E-cadherin and NMMIIA could interact with Kif5b in polarized MDCK cells, and their expression levels were decreased in Kif5b-deficient MDCK cells. Overexpression of E-cadherin and NMMIIA in Kif5b depleted MDCK cells could decrease mesenchymal marker expression and cell migration ability. These results indicate that stable knockdown of Kif5b in MDCK cells can lead to epithelial–mesenchymal transition, which is mediated by defective E-cadherin and NMMIIA expression. - Highlights: • Knockdown of Kif5b in MDCK cells resulted in reduced cell proliferation rate. • Kif5b deficient MDCK cells underwent epithelial–mesenchymal transition. • E-cadherin and NMMIIA could interact with Kif5b in polarized MDCK cells. • Decreased E-cadherin and NMMIIA levels mediate EMT in Kif5b deficient MDCK cells. • Overexpression of E-cadherin and NMMIIA reverse the effects of Kif5b knockdown.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Effects of celecoxib on proliferation and tenocytic differentiation of tendon-derived stem cells

Highlights: • Celecoxib has no effects on TDSCs cell proliferation in various concentrations. • Celecoxib reduced mRNAs levels of tendon associated transcription factor. • Celecoxib reduced mRNAs levels of main tendon associated collagen. • Celecoxib reduced mRNAs levels of tendon associated molecules. - Abstract: NSAIDs are often ingested to reduce the pain and improve regeneration of tendon after tendon injury. Although the effects of NSAIDs in tendon healing have been reported, the data and conclusions are not consistent. Recently, tendon-derived stem cells (TDSCs) have been isolated from tendon tissues and has been suggested involved in tendon repair. Our study aims to determine the effects of COX-2 inhibitor (celecoxib) on the proliferation and tenocytic differentiation of TDSCs. TDSCs were isolated from mice Achilles tendon and exposed to celecoxib. Cell proliferation rate was investigated at various concentrations (0.1, 1, 10 and 100 μg/ml) of celecoxib by using hemocytometer. The mRNA expression of tendon associated transcription factors, tendon associated collagens and tendon associated molecules were determined by reverse transcription-polymerase chain reaction. The protein expression of Collagen I, Collagen III, Scleraxis and Tenomodulin were determined by Western blotting. The results showed that celecoxib has no effects on TDSCs cell proliferation in various concentrations (p > 0.05). The levels of most tendon associated transcription factors, tendon associated collagens and tendon associated molecules genes expression were significantly decreased in celecoxib (10 μg/ml) treated group (p < 0.05). Collagen I, Collagen III, Scleraxis and Tenomodulin protein expression were also significantly decreased in celecoxib (10 μg/ml) treated group (p < 0.05). In conclusion, celecoxib inhibits tenocytic differentiation of tendon-derived stem cells but has no effects on cell proliferation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Layered insulator hexagonal boron nitride for surface passivation in quantum dot solar cell

Single crystalline, two dimensional (2D) layered insulator hexagonal boron nitride (h-BN), is demonstrated as an emerging material candidate for surface passivation on mesoporous TiO{sub 2}. Cadmium selenide (CdSe) quantum dot based bulk heterojunction (BHJ) solar cell employed h-BN passivated TiO{sub 2} as an electron acceptor exhibits photoconversion efficiency ∼46% more than BHJ employed unpassivated TiO{sub 2}. Dominant interfacial recombination pathways such as electron capture by TiO{sub 2} surface states and recombination with hole at valence band of CdSe are efficiently controlled by h-BN enabled surface passivation, leading to improved photovoltaic performance. Highly crystalline, confirmed by transmission electron microscopy, dangling bond-free 2D layered h-BN with self-terminated atomic planes, achieved by chemical exfoliation, enables efficient passivation on TiO{sub 2}, allowing electronic transport at TiO{sub 2}/h-BN/CdSe interface with much lower recombination rate compared to an unpassivated TiO{sub 2}/CdSe interface.

14 SOLAR ENERGY↗
DOE OSTI2013

A theoretical study of fluorographene as substrates for mono-/Bi-layer graphene

Using density functional theory, we discover fluorographene (CF) could help significantly preserve the superb electronic properties of graphene, depending on lattice stacking-order. Compared with h-BN, CF produces a much weaker screening effect for bilayer graphene when external electric field is applied, revealing a huge advantage in gap engineering. The studies suggest that fluorographene could be a promising route towards implementing highly functional substrate or gate dielectric materials for graphene-inspired device applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Spatial expression of transcription factors in Drosophila embryonic organ development

Background: Site-specific transcription factors (TFs) bind DNA regulatory elements to control expression of target genes, forming the core of gene regulatory networks. Despite decades of research, most studies focus on only a small number of TFs and the roles of many remain unknown. Results: We present a systematic characterization of spatiotemporal gene expression patterns for all known or predicted Drosophila TFs throughout embryogenesis, the first such comprehensive study for any metazoan animal. We generated RNA expression patterns for all 708 TFs by in situ hybridization, annotated the patterns using an anatomical controlled vocabulary, and analyzed TF expression in the context of organ system development. Nearly all TFs are expressed during embryogenesis and more than half are specifically expressed in the central nervous system. Compared to other genes, TFs are enriched early in the development of most organ systems, and throughout the development of the nervous system. Of the 535 TFs with spatially restricted expression, 79% are dynamically expressed in multiple organ systems while 21% show single-organ specificity. Of those expressed in multiple organ systems, 77 TFs are restricted to a single organ system either early or late in development. Expression patterns for 354 TFs are characterized for the first time in this study. Conclusions: We produced a reference TF dataset for the investigation of gene regulatory networks in embryogenesis, and gained insight into the expression dynamics of the full complement of TFs controlling the development of each organ system.

59 BASIC BIOLOGICAL SCIENCES↗