scholarly journals Automating parameter selection to avoid implausible biological pathway models

2019 ◽  
Author(s):  
Chris S. Magnano ◽  
Anthony Gitter
Keyword(s):  
Level Structure ◽  
Ground Truth ◽  
Parameter Tuning ◽  
Parameter Selection ◽  
Biological Pathways ◽  
Reconstruction Algorithms ◽  
Direct Evaluation ◽  
Link Type ◽  
Pathway Reconstruction ◽  
Omic Data

AbstractA common way to integrate and analyze large amounts of biological “omic” data is through pathway reconstruction: using condition-specific omic data to create a subnetwork of a generic background network that represents some process or cellular state. A challenge in pathway reconstruction is that adjusting pathway reconstruction algorithms’ parameters produces pathways with drastically different topological properties and biological interpretations. Due to the exploratory nature of pathway reconstruction, there is no ground truth for direct evaluation, so parameter tuning methods typically used in statistics and machine learning are inapplicable. We developed the pathway parameter advising algorithm to tune pathway reconstruction algorithms to minimize biologically implausible predictions. We leverage background knowledge in pathway databases to select pathways whose high-level structure resembles that of manually curated biological pathways. At the core of this method is a graphlet decomposition metric, which measures topological similarity to curated biological pathways. In order to evaluate pathway parameter advising, we compare its performance in avoiding implausible networks and reconstructing pathways from the NetPath database with other parameter selection methods across four pathway reconstruction algorithms. We also demonstrate how pathway parameter advising can guide construction of an influenza host factor network. Pathway parameter advising is method-agnostic; it is applicable to any pathway reconstruction algorithm with tunable parameters. Our pathway parameter advising software is available on GitHub at https://github.com/gitter-lab/pathway-parameter-advising and PyPI at https://pypi.org/project/pathwayParameterAdvising/.

scholarly journals Automating parameter selection to avoid implausible biological pathway models

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Chris S. Magnano ◽  
Anthony Gitter
Keyword(s):  
Level Structure ◽  
Ground Truth ◽  
Reconstruction Algorithm ◽  
Parameter Tuning ◽  
Parameter Selection ◽  
Biological Pathways ◽  
Reconstruction Algorithms ◽  
Direct Evaluation ◽  
Pathway Reconstruction ◽  
Omic Data

AbstractA common way to integrate and analyze large amounts of biological “omic” data is through pathway reconstruction: using condition-specific omic data to create a subnetwork of a generic background network that represents some process or cellular state. A challenge in pathway reconstruction is that adjusting pathway reconstruction algorithms’ parameters produces pathways with drastically different topological properties and biological interpretations. Due to the exploratory nature of pathway reconstruction, there is no ground truth for direct evaluation, so parameter tuning methods typically used in statistics and machine learning are inapplicable. We developed the pathway parameter advising algorithm to tune pathway reconstruction algorithms to minimize biologically implausible predictions. We leverage background knowledge in pathway databases to select pathways whose high-level structure resembles that of manually curated biological pathways. At the core of this method is a graphlet decomposition metric, which measures topological similarity to curated biological pathways. In order to evaluate pathway parameter advising, we compare its performance in avoiding implausible networks and reconstructing pathways from the NetPath database with other parameter selection methods across four pathway reconstruction algorithms. We also demonstrate how pathway parameter advising can guide reconstruction of an influenza host factor network. Pathway parameter advising is method agnostic; it is applicable to any pathway reconstruction algorithm with tunable parameters.

scholarly journals Augmenting Signaling Pathway Reconstructions

2020 ◽  
Author(s):  
Tobias Rubel ◽  
Anna Ritz
Keyword(s):  
Signaling Pathways ◽  
Signaling Pathway ◽  
Protein Interactions ◽  
Cellular Response ◽  
Ground Truth ◽  
Protein Interaction Data ◽  
Reconstruction Method ◽  
Reconstruction Algorithms ◽  
Molecular Systems ◽  
Pathway Reconstruction

AbstractSignaling pathways drive cellular response, and understanding such pathways is fundamental to molecular systems biology. A mounting volume of experimental protein interaction data has motivated the development of algorithms to computationally reconstruct signaling pathways. However, existing methods suffer from low recall in recovering protein interactions in ground truth pathways, limiting our confidence in any new predictions for experimental validation. We present the Pathway Reconstruction AUGmenter (PRAUG), a higher-order function for producing high-quality pathway reconstruction algorithms. PRAUG modifies any existing pathway reconstruction method, resulting in augmented algorithms that outperform their un-augmented counterparts for six different algorithms across twenty-nine diverse signaling pathways. The algorithms produced by PRAUG collectively reveal potential new proteins and interactions involved in the Wnt and Notch signaling pathways. PRAUG offers a valuable framework for signaling pathway prediction and discovery.

scholarly journals Fully-automated root image analysis (faRIA)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Narendra Narisetti ◽  
Michael Henke ◽  
Christiane Seiler ◽  
Astrid Junker ◽  
Jörn Ostermann ◽  
...  
Keyword(s):  
Near Infrared ◽  
Climatic Factors ◽  
Plant Root ◽  
Ground Truth ◽  
Parameter Tuning ◽  
Dice Coefficient ◽  
Powerful Analytical Tool ◽  
Manual Interaction ◽  
And Function ◽  
Noisy Background

AbstractHigh-throughput root phenotyping in the soil became an indispensable quantitative tool for the assessment of effects of climatic factors and molecular perturbation on plant root morphology, development and function. To efficiently analyse a large amount of structurally complex soil-root images advanced methods for automated image segmentation are required. Due to often unavoidable overlap between the intensity of fore- and background regions simple thresholding methods are, generally, not suitable for the segmentation of root regions. Higher-level cognitive models such as convolutional neural networks (CNN) provide capabilities for segmenting roots from heterogeneous and noisy background structures, however, they require a representative set of manually segmented (ground truth) images. Here, we present a GUI-based tool for fully automated quantitative analysis of root images using a pre-trained CNN model, which relies on an extension of the U-Net architecture. The developed CNN framework was designed to efficiently segment root structures of different size, shape and optical contrast using low budget hardware systems. The CNN model was trained on a set of 6465 masks derived from 182 manually segmented near-infrared (NIR) maize root images. Our experimental results show that the proposed approach achieves a Dice coefficient of 0.87 and outperforms existing tools (e.g., SegRoot) with Dice coefficient of 0.67 by application not only to NIR but also to other imaging modalities and plant species such as barley and arabidopsis soil-root images from LED-rhizotron and UV imaging systems, respectively. In summary, the developed software framework enables users to efficiently analyse soil-root images in an automated manner (i.e. without manual interaction with data and/or parameter tuning) providing quantitative plant scientists with a powerful analytical tool.

scholarly journals Quantifying accuracy and heterogeneity in single-molecule super-resolution microscopy

2019 ◽  
Author(s):  
Hesam Mazidi ◽  
Tianben Ding ◽  
Arye Nehorai ◽  
Matthew D. Lew
Keyword(s):  
Single Molecule ◽  
Computational Models ◽  
Amyloid Fibrils ◽  
Imaging System ◽  
Simulated Data ◽  
Super Resolution ◽  
Ground Truth ◽  
Localization Algorithm ◽  
Reconstruction Algorithms ◽  
Imaging Data

The resolution and accuracy of single-molecule localization micro-scopes (SMLMs) are routinely benchmarked using simulated data, calibration “rulers,” or comparisons to secondary imaging modalities. However, these methods cannot quantify the nanoscale accuracy of an arbitrary SMLM dataset. Here, we show that by computing localization stability under a well-chosen perturbation with accurate knowledge of the imaging system, we can robustly measure the confidence of individual localizations without ground-truth knowledge of the sample. We demonstrate that our method, termed Wasserstein-induced flux (WIF), measures the accuracy of various reconstruction algorithms directly on experimental 2D and 3D data of microtubules and amyloid fibrils. We further show that WIF confidences can be used to evaluate the mismatch between computational models and imaging data, enhance the accuracy and resolution of recon-structed structures, and discover hidden molecular heterogeneities. As a computational methodology, WIF is broadly applicable to any SMLM dataset, imaging system, and localization algorithm.

scholarly journals Artifact Correction in Short-Term HRV during Strenuous Physical Exercise

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6372
Author(s):  
Aleksandra Królak ◽  
Tomasz Wiktorski ◽  
Magnus Friestad Bjørkavoll-Bergseth ◽  
Stein Ørn
Keyword(s):  
Median Filter ◽  
Real Life ◽  
Ground Truth ◽  
Parameter Tuning ◽  
Artifact Correction ◽  
Minimal Impact ◽  
Early Results ◽  
Direct Measurements ◽  
Hrv Analysis ◽  
Monitoring Devices

Heart rate variability (HRV) analysis can be a useful tool to detect underlying heart or even general health problems. Currently, such analysis is usually performed in controlled or semi-controlled conditions. Since many of the typical HRV measures are sensitive to data quality, manual artifact correction is common in literature, both as an exclusive method or in addition to various filters. With proliferation of Personal Monitoring Devices with continuous HRV analysis an opportunity opens for HRV analysis in a new setting. However, current artifact correction approaches have several limitations that hamper the analysis of real-life HRV data. To address this issue we propose an algorithm for automated artifact correction that has a minimal impact on HRV measures, but can handle more artifacts than existing solutions. We verify this algorithm based on two datasets. One collected during a recreational bicycle race and another one in a laboratory, both using a PMD in form of a GPS watch. Data include direct measurement of electrical myocardial signals using chest straps and direct measurements of power using a crank sensor (in case of race dataset), both paired with the watch. Early results suggest that the algorithm can correct more artifacts than existing solutions without a need for manual support or parameter tuning. At the same time, the error introduced to HRV measures for peak correction and shorter gaps is similar to the best existing solution (Kubios-inspired threshold-based cubic interpolation) and better than commonly used median filter. For longer gaps, cubic interpolation can in some cases result in lower error in HRV measures, but the shape of the curve it generates matches ground truth worse than our algorithm. It might suggest that further development of the proposed algorithm may also improve these results.

scholarly journals starmapVR: immersive visualisation of single cell spatial omic data

2020 ◽  
Author(s):  
Andrian Yang ◽  
Yu Yao ◽  
Xiunan Fang ◽  
Jianfu Li ◽  
Yongyan Xia ◽  
...  
Keyword(s):  
Single Cell ◽  
Low Cost ◽  
Single Cells ◽  
Three Dimensional ◽  
Supplementary Information ◽  
Phenotypic Properties ◽  
Histological Image ◽  
Link Type ◽  
Molecular Expression ◽  
Omic Data

AbstractMotivationAdvances in high throughput single-cell and spatial omic technologies have enabled the profiling of molecular expression and phenotypic properties of hundreds of thousands of individual cells in the context of their two dimensional (2D) or three dimensional (3D) spatial endogenous arrangement. However, current visualisation techniques do not allow for effective display and exploration of the single cell data in their spatial context. With the widespread availability of low-cost virtual reality (VR) gadgets, such as Google Cardboard, we propose that an immersive visualisation strategy is useful.ResultsWe present starmapVR, a light-weight, cross-platform, web-based tool for visualising single-cell and spatial omic data. starmapVR supports a number of interaction methods, such as keyboard, mouse, wireless controller and voice control. The tool visualises single cells in a 3D space and each cell can be represented by a star plot (for molecular expression, phenotypic properties) or image (for single cell imaging). For spatial transcriptomic data, the 2D single cell expression data can be visualised alongside the histological image in a 2.5D format. The application of starmapVR is demonstrated through a series of case studies. Its scalability has been carefully evaluated across different platforms.Availability and implementationstarmapVR is freely accessible at https://holab-hku.github.io/starmapVR, with the corresponding source code available at https://github.com/holab-hku/starmapVR under the open source MIT license.Supplementary InformationSupplementary data are available at Bioinformatics online.

scholarly journals QSM Reconstruction Challenge 2.0–Part 1: A Realistic in silico Head Phantom for MRI data simulation and evaluation of susceptibility mapping procedures

2020 ◽  
Author(s):  
José P. Marques ◽  
Jakob Meineke ◽  
Carlos Milovic ◽  
Berkin Bilgic ◽  
Kwok-Shing Chan ◽  
...  
Keyword(s):  
In Silico ◽  
Ground Truth ◽  
Susceptibility Mapping ◽  
Quantitative Mri ◽  
Reconstruction Algorithms ◽  
Imaging Data ◽  
Post Processing ◽  
Simulation Code ◽  
Wide Range ◽  
Echo Time

AbstractPurposeTo create a realistic in-silico head phantom for the second QSM Reconstruction Challenge and for future evaluations of processing algorithms for Quantitative Susceptibility Mapping (QSM).MethodsWe created a whole-head tissue property model by segmenting and post-processing high-resolution, multi-parametric MRI data acquired from a healthy volunteer. We simulated the steady-state magnetization using a Bloch simulator and mimicked a Cartesian sampling scheme through Fourier-based post-processing. We demonstrated some of the phantom’s properties, including the possibility of generating phase data that do not evolve linearly with echo time due to partial volume effects or complex distributions of frequency shifts within the voxel. Computer code for generating the phantom and performing the MR simulation was designed to facilitate flexible modifications of the model, such as the inclusion of pathologies, as well as the simulation of a wide range of acquisition protocols.ResultsThe brain-part of the phantom features realistic morphology combined with realistic spatial variations in relaxation and susceptibility values. Simulation code allows adjusting the following parameters and effects: repetition time and echo time, voxel size, background fields, and RF phase biases. Additionally, diffusion weighted imaging data of the phantom is provided allowing future investigations of tissue microstructure effects in phase and QSM algorithms.ConclusionThe presented phantom and computer programs are publicly available and may serve as a ground truth in future assessments of the faithfulness of quantitative MRI reconstruction algorithms.

scholarly journals Performance Assessment of the Network Reconstruction Approaches on Various Interactomes

2021 ◽  
Vol 8 ◽  
Author(s):  
M. Kaan Arici ◽  
Nurcan Tuncbag
Keyword(s):  
Protein Interactions ◽  
Structural Information ◽  
Network Reconstruction ◽  
Heat Diffusion ◽  
Reconstruction Algorithms ◽  
Personalized Pagerank ◽  
Cancer Driver ◽  
Pathway Reconstruction ◽  
Main Challenge ◽  
Steiner Forest

Beyond the list of molecules, there is a necessity to collectively consider multiple sets of omic data and to reconstruct the connections between the molecules. Especially, pathway reconstruction is crucial to understanding disease biology because abnormal cellular signaling may be pathological. The main challenge is how to integrate the data together in an accurate way. In this study, we aim to comparatively analyze the performance of a set of network reconstruction algorithms on multiple reference interactomes. We first explored several human protein interactomes, including PathwayCommons, OmniPath, HIPPIE, iRefWeb, STRING, and ConsensusPathDB. The comparison is based on the coverage of each interactome in terms of cancer driver proteins, structural information of protein interactions, and the bias toward well-studied proteins. We next used these interactomes to evaluate the performance of network reconstruction algorithms including all-pair shortest path, heat diffusion with flux, personalized PageRank with flux, and prize-collecting Steiner forest (PCSF) approaches. Each approach has its own merits and weaknesses. Among them, PCSF had the most balanced performance in terms of precision and recall scores when 28 pathways from NetPath were reconstructed using the listed algorithms. Additionally, the reference interactome affects the performance of the network reconstruction approaches. The coverage and disease- or tissue-specificity of each interactome may vary, which may result in differences in the reconstructed networks.

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