lionessR: single sample network inference in R

Abstract Background In biomedical research, network inference algorithms are typically used to infer complex association patterns between biological entities, such as between genes or proteins, using data from a population. This resulting aggregate network, in essence, averages over the networks of those individuals in the population. LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples) is a method that can be used together with a network inference algorithm to extract networks for individual samples in a population. The method’s key characteristic is that, by modeling networks for individual samples in a data set, it can capture network heterogeneity in a population. LIONESS was originally made available as a function within the PANDA (Passing Attributes between Networks for Data Assimilation) regulatory network reconstruction framework. However, the LIONESS algorithm is generalizable and can be used to model single sample networks based on a wide range of network inference algorithms. Results In this software article, we describe lionessR, an R implementation of LIONESS that can be applied to any network inference method in R that outputs a complete, weighted adjacency matrix. As an example, we provide a vignette of an application of lionessR to model single sample networks based on correlated gene expression in a bone cancer dataset. We show how the tool can be used to identify differential patterns of correlation between two groups of patients. Conclusions We developed lionessR, an open source R package to model single sample networks. We show how lionessR can be used to inform us on potential precision medicine applications in cancer. The lionessR package is a user-friendly tool to perform such analyses. The package, which includes a vignette describing the application, is freely available at: https://github.com/kuijjerlab/lionessR and at: http://bioconductor.org/packages/lionessR.

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lionessR: single-sample network reconstruction in R

10.1101/582098 ◽

2019 ◽

Author(s):

Marieke L. Kuijjer ◽

John Quackenbush ◽

Kimberly Glass

Keyword(s):

Regulatory Network ◽

Bone Cancer ◽

Linear Interpolation ◽

R Package ◽

Network Reconstruction ◽

Single Sample ◽

Reconstruction Method ◽

Reconstruction Algorithms ◽

Cancer Dataset ◽

Weighted Adjacency Matrix

SummaryWe recently developed LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples), a method that can be used together with network reconstruction algorithms to extract networks for individual samples in a population. LIONESS was originally made available as a function within the PANDA (Passing Attributes between Networks for Data Assimilation) regulatory network reconstruction framework. In this application note, we describe lionessR, an R implementation of LIONESS that can be applied to any network reconstruction method in R that outputs a complete, weighted adjacency matrix. As an example, we use lionessR to model single-sample co-expression networks on a bone cancer dataset, and show how lionessR can be used to identify differential co-expression between two groups of patients.Availability and implementationThe lionessR open source R package, which includes a vignette of the application, is freely available athttps://github.com/mararie/[email protected]

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Plugging Small RNAs into the Network

mSystems ◽

10.1128/msystems.00422-20 ◽

2020 ◽

Vol 5 (3) ◽

Author(s):

Lars Barquist

Keyword(s):

Small Rnas ◽

Posttranscriptional Regulation ◽

Network Inference ◽

Functional Characterization ◽

The State ◽

Diverse Range ◽

Regulatory Interactions ◽

Link Type ◽

Wide Range

ABSTRACT Small RNAs (sRNAs) have been discovered in every bacterium examined and have been shown to play important roles in the regulation of a diverse range of behaviors, from metabolism to infection. However, despite a wide range of available techniques for discovering and validating sRNA regulatory interactions, only a minority of these molecules have been well characterized. In part, this is due to the nature of posttranscriptional regulation: the activity of an sRNA depends on the state of the transcriptome as a whole, so characterization is best carried out under the conditions in which it is naturally active. In this issue of mSystems, Arrieta-Ortiz and colleagues (M. L. Arrieta-Ortiz, C. Hafemeister, B. Shuster, N. S. Baliga, et al., mSystems 5:e00057-20, 2020, https://doi.org/10.1128/mSystems.00057-20) present a network inference approach based on estimating sRNA activity across transcriptomic compendia. This shows promise not only for identifying new sRNA regulatory interactions but also for pinpointing the conditions in which these interactions occur, providing a new avenue toward functional characterization of sRNAs.

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Structure Guided Molecular Docking Assisted Alignment Dependent 3DQSAR Study on Steroidal Aromatase Inhibitors (SAIs) as Anti-breast Cancer Agents

Letters in Drug Design & Discovery ◽

10.2174/1570180815666181010101024 ◽

2019 ◽

Vol 16 (7) ◽

pp. 808-817 ◽

Cited By ~ 3

Author(s):

Laxmi Banjare ◽

Sant Kumar Verma ◽

Akhlesh Kumar Jain ◽

Suresh Thareja

Keyword(s):

Breast Cancer ◽

Molecular Docking ◽

Aromatase Inhibitors ◽

Direct Synthesis ◽

3D Qsar ◽

Chemotherapeutic Agents ◽

Modeling Tools ◽

Data Set ◽

Wide Range ◽

Steroidal Aromatase Inhibitors

Background: In spite of the availability of various treatment approaches including surgery, radiotherapy, and hormonal therapy, the steroidal aromatase inhibitors (SAIs) play a significant role as chemotherapeutic agents for the treatment of estrogen-dependent breast cancer with the benefit of reduced risk of recurrence. However, due to greater toxicity and side effects associated with currently available anti-breast cancer agents, there is emergent requirement to develop target-specific AIs with safer anti-breast cancer profile. Methods: It is challenging task to design target-specific and less toxic SAIs, though the molecular modeling tools viz. molecular docking simulations and QSAR have been continuing for more than two decades for the fast and efficient designing of novel, selective, potent and safe molecules against various biological targets to fight the number of dreaded diseases/disorders. In order to design novel and selective SAIs, structure guided molecular docking assisted alignment dependent 3D-QSAR studies was performed on a data set comprises of 22 molecules bearing steroidal scaffold with wide range of aromatase inhibitory activity. Results: 3D-QSAR model developed using molecular weighted (MW) extent alignment approach showed good statistical quality and predictive ability when compared to model developed using moments of inertia (MI) alignment approach. Conclusion: The explored binding interactions and generated pharmacophoric features (steric and electrostatic) of steroidal molecules could be exploited for further design, direct synthesis and development of new potential safer SAIs, that can be effective to reduce the mortality and morbidity associated with breast cancer.

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Integrative Data Analysis from a Unifying Research Synthesis Perspective

10.1093/oso/9780190676001.003.0020 ◽

2018 ◽

Author(s):

Eun-Young Mun ◽

Anne E. Ray

Keyword(s):

Data Analysis ◽

Large Scale ◽

Research Synthesis ◽

Alcohol Intervention ◽

Data Set ◽

Integrative Data Analysis ◽

Level Data ◽

Model Complex ◽

Wide Range ◽

Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

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A Self-Spatial Adaptive Weighting Based U-Net for Image Segmentation

Electronics ◽

10.3390/electronics10030348 ◽

2021 ◽

Vol 10 (3) ◽

pp. 348

Author(s):

Choongsang Cho ◽

Young Han Lee ◽

Jongyoul Park ◽

Sangkeun Lee

Keyword(s):

Image Segmentation ◽

Medical Image ◽

Medical Image Segmentation ◽

Feature Maps ◽

Data Set ◽

Feature Map ◽

Adaptive Weighting ◽

Spatially Adaptive ◽

Wide Range ◽

Decoder Architecture

Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease diagnosis, surgical planning, and history monitoring. The state-of-the-art models in medical image segmentation are variants of encoder-decoder architecture, which is called U-Net. To effectively reflect the spatial features in feature maps in encoder-decoder architecture, we propose a spatially adaptive weighting scheme for medical image segmentation. Specifically, the spatial feature is estimated from the feature maps, and the learned weighting parameters are obtained from the computed map, since segmentation results are predicted from the feature map through a convolutional layer. Especially in the proposed networks, the convolutional block for extracting the feature map is replaced with the widely used convolutional frameworks: VGG, ResNet, and Bottleneck Resent structures. In addition, a bilinear up-sampling method replaces the up-convolutional layer to increase the resolution of the feature map. For the performance evaluation of the proposed architecture, we used three data sets covering different medical imaging modalities. Experimental results show that the network with the proposed self-spatial adaptive weighting block based on the ResNet framework gave the highest IoU and DICE scores in the three tasks compared to other methods. In particular, the segmentation network combining the proposed self-spatially adaptive block and ResNet framework recorded the highest 3.01% and 2.89% improvements in IoU and DICE scores, respectively, in the Nerve data set. Therefore, we believe that the proposed scheme can be a useful tool for image segmentation tasks based on the encoder-decoder architecture.

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ModularBoost: an efficient network inference algorithm based on module decomposition

BMC Bioinformatics ◽

10.1186/s12859-021-04074-y ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Xinyu Li ◽

Wei Zhang ◽

Jianming Zhang ◽

Guang Li

Keyword(s):

Network Inference ◽

Detection Methods ◽

Inference Problem ◽

Topological Constraints ◽

Inference Algorithms ◽

Module Detection ◽

Series Expression ◽

Gene Modules ◽

Inference Methods ◽

Complicated Task

Abstract Background Given expression data, gene regulatory network(GRN) inference approaches try to determine regulatory relations. However, current inference methods ignore the inherent topological characters of GRN to some extent, leading to structures that lack clear biological explanation. To increase the biophysical meanings of inferred networks, this study performed data-driven module detection before network inference. Gene modules were identified by decomposition-based methods. Results ICA-decomposition based module detection methods have been used to detect functional modules directly from transcriptomic data. Experiments about time-series expression, curated and scRNA-seq datasets suggested that the advantages of the proposed ModularBoost method over established methods, especially in the efficiency and accuracy. For scRNA-seq datasets, the ModularBoost method outperformed other candidate inference algorithms. Conclusions As a complicated task, GRN inference can be decomposed into several tasks of reduced complexity. Using identified gene modules as topological constraints, the initial inference problem can be accomplished by inferring intra-modular and inter-modular interactions respectively. Experimental outcomes suggest that the proposed ModularBoost method can improve the accuracy and efficiency of inference algorithms by introducing topological constraints.

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Prediction of Wide Range Two-Dimensional Refractivity Using an IDW Interpolation Method from High-Altitude Refractivity Data of Multiple Meteorological Observatories

Applied Sciences ◽

10.3390/app11041431 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1431

Author(s):

Sungsik Wang ◽

Tae Heung Lim ◽

Kyoungsoo Oh ◽

Chulhun Seo ◽

Hosung Choo

Keyword(s):

High Altitude ◽

Korean Peninsula ◽

Interpolation Method ◽

Atmospheric Condition ◽

Propagation Path ◽

Two Dimensional ◽

Data Set ◽

Wide Range ◽

Atmospheric Data ◽

Terrain Surface

This article proposes a method for the prediction of wide range two-dimensional refractivity for synthetic aperture radar (SAR) applications, using an inverse distance weighted (IDW) interpolation of high-altitude radio refractivity data from multiple meteorological observatories. The radio refractivity is extracted from an atmospheric data set of twenty meteorological observatories around the Korean Peninsula along a given altitude. Then, from the sparse refractive data, the two-dimensional regional radio refractivity of the entire Korean Peninsula is derived using the IDW interpolation, in consideration of the curvature of the Earth. The refractivities of the four seasons in 2019 are derived at the locations of seven meteorological observatories within the Korean Peninsula, using the refractivity data from the other nineteen observatories. The atmospheric refractivities on 15 February 2019 are then evaluated across the entire Korean Peninsula, using the atmospheric data collected from the twenty meteorological observatories. We found that the proposed IDW interpolation has the lowest average, the lowest average root-mean-square error (RMSE) of ∇M (gradient of M), and more continuous results than other methods. To compare the resulting IDW refractivity interpolation for airborne SAR applications, all the propagation path losses across Pohang and Heuksando are obtained using the standard atmospheric condition of ∇M = 118 and the observation-based interpolated atmospheric conditions on 15 February 2019. On the terrain surface ranging from 90 km to 190 km, the average path losses in the standard and derived conditions are 179.7 dB and 182.1 dB, respectively. Finally, based on the air-to-ground scenario in the SAR application, two-dimensional illuminated field intensities on the terrain surface are illustrated.

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Prodromal Dementia With Lewy Bodies: Evolution of Symptoms and Predictors of Dementia Onset

Journal of Geriatric Psychiatry and Neurology ◽

10.1177/08919887211023586 ◽

2021 ◽

pp. 089198872110235

Author(s):

Kathryn A. Wyman-Chick ◽

Lauren R. O’Keefe ◽

Daniel Weintraub ◽

Melissa J. Armstrong ◽

Michael Rosenbloom ◽

...

Keyword(s):

Clinical Features ◽

Dementia With Lewy Bodies ◽

Clinical Symptoms ◽

Neuropsychiatric Symptoms ◽

Lewy Bodies ◽

Rem Sleep Behavior Disorder ◽

Data Set ◽

Wide Range ◽

Prodromal Dementia ◽

Dementia Onset

Background: Research criteria for prodromal dementia with Lewy bodies (DLB) were published in 2020, but little is known regarding prodromal DLB in clinical settings. Methods: We identified non-demented participants without neurodegenerative disease from the National Alzheimer’s Coordinating Center Uniform Data Set who converted to DLB at a subsequent visit. Prevalence of neuropsychiatric and motor symptoms were examined up to 5 years prior to DLB diagnosis. Results: The sample included 116 participants clinically diagnosed with DLB and 348 age and sex-matched (1:3) Healthy Controls. Motor slowing was present in approximately 70% of participants 3 years prior to DLB diagnosis. In the prodromal phase, 50% of DLB participants demonstrated gait disorder, 70% had rigidity, 20% endorsed visual hallucinations, and over 50% of participants endorsed REM sleep behavior disorder. Apathy, depression, and anxiety were common prodromal neuropsychiatric symptoms. The presence of 1+ core clinical features of DLB in combination with apathy, depression, or anxiety resulted in the greatest AUC (0.815; 95% CI: 0.767, 0.865) for distinguishing HC from prodromal DLB 1 year prior to diagnosis. The presence of 2+ core clinical features was also accurate in differentiating between groups (AUC = 0.806; 95% CI: 0.756, 0.855). Conclusion: A wide range of motor, neuropsychiatric and other core clinical symptoms are common in prodromal DLB. A combination of core clinical features, neuropsychiatric symptoms and cognitive impairment can accurately differentiate DLB from normal aging prior to dementia onset.

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Relationship Between Gas Emissions and Dry Matter Intake in Mature Angus Cows

Journal of Animal Science ◽

10.1093/jas/skab096.058 ◽

2021 ◽

Vol 99 (Supplement_2) ◽

pp. 32-33

Author(s):

Amanda Holder ◽

Megan A Gross ◽

Alexi Moehlenpah ◽

Paul Beck

Keyword(s):

Dry Matter ◽

Significant Positive Correlation ◽

Crossover Design ◽

Dry Matter Intake ◽

Data Set ◽

Gas Emissions ◽

Positive Correlation ◽

Wide Range ◽

Carbon Dioxide Co2 ◽

Second Period

Abstract The objective of this study was to examine the effects of diet quality on greenhouse gas emissions and dry matter intake (DMI). We used 42 mature, gestating Angus cows (600±69 kg; and BSC 5.3±1.1) with a wide range in DMI EPD (-1.36 to 2.29). Cows were randomly assigned to 2 diet sequences forage-concentrate (FC) or concentrate-forage(CF) determined by the diet they consumed in each period (forage or concentrate). The cows were adapted to the diet and the SmartFeed individual intake units for 14 d followed by 45 d of intake data collection for each period. Body weight was recorded on consecutive weigh days at the beginning and end of each period and then once every two wk for the duration of a period. Cows were exposed to the GreenFeed Emission Monitoring (GEM) system for no less than 9 d during each period. The GEM system was used to measure emissions of carbon dioxide (CO2) and methane (CH4). Only cows with a minimum of 20 total >3-m visits to the GEM were included in the data set. Data were analyzed in a crossover design using GLIMMIX in SASv.9.4. Within the CF sequence there was a significant, positive correlation between TMR DMI and CH4 (r=0.81) and TMR DMI and CO2 (r=0.69), however, gas emissions during the second period on the hay diet were not correlated with hay intake. There was a significant, positive correlation between hay DMI and CO2 (r=0.76) and hay DMI and CH4 (r=0.74) when cows first consumed forage (FC). In comparison to the CF sequence, cows on the FC sequence showed a positive correlation between CO2 and TMR DMI during the second period. There was also a significant positive correlation between hay and TMR DMI when assessed across (r=0.43) or within sequence (FC r=0.41, CF r=0.47).

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Query Processing of RFID Data with Object Trajectory in Manufacture and Logistics

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.16-19.1043 ◽

2009 ◽

Vol 16-19 ◽

pp. 1043-1047

Author(s):

Sun Wei ◽

Li Hua Dong ◽

Yao Hua Dong

Keyword(s):

Pervasive Computing ◽

Radio Frequency Identification ◽

Object Identification ◽

Data Set ◽

Wide Range ◽

Storage Scheme ◽

Rfid Data ◽

Frequency Identification ◽

Application Data ◽

Path Queries

In the domain of manufacture and logistics, Radio Frequency Identification (RFID) holds the promise of real-time identifying, locating, tracking and monitoring physical objects without line of sight due to an enhanced efficiency, accuracy, and preciseness of object identification, and can be used for a wide range of pervasive computing applications. To achieve these goals, RFID data has to be collected, filtered, and transformed into semantic application data. However, the amount of RFID data is huge. Therefore, it requires much time to extract valuable information from RFID data for object tracing. This paper specifically explores options for modeling and utilizing RFID data set by XML-encoding for tracking queries and path oriented queries. We then propose a method which translates the queries to SQL queries. Based on the XML-encoding scheme, we devise a storage scheme to process tracking queries and path oriented queries efficiently. Finally, we realize the method by programming in a software system for manufacture and logistics laboratory. The system shows that our approach can process the tracing or path queries efficiently.

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