scholarly journals Quantifying the multi-scale performance of network inference algorithms

2014 ◽  
Vol 13 (5) ◽  
Author(s):  
Chris J. Oates ◽  
Richard Amos ◽  
Simon E.F. Spencer
Keyword(s):  
Graphical Models ◽  
Network Inference ◽  
Multiple Scales ◽  
Biological Systems ◽  
Higher Order ◽  
Wisdom Of Crowds ◽  
Multi Scale ◽  
Reverse Engineer ◽  
Multiple Length Scales ◽  
Inference Algorithms

AbstractGraphical models are widely used to study complex multivariate biological systems. Network inference algorithms aim to reverse-engineer such models from noisy experimental data. It is common to assess such algorithms using techniques from classifier analysis. These metrics, based on ability to correctly infer individual edges, possess a number of appealing features including invariance to rank-preserving transformation. However, regulation in biological systems occurs on multiple scales and existing metrics do not take into account the correctness of higher-order network structure. In this paper novel performance scores are presented that share the appealing properties of existing scores, whilst capturing ability to uncover regulation on multiple scales. Theoretical results confirm that performance of a network inference algorithm depends crucially on the scale at which inferences are to be made; in particular strong local performance does not guarantee accurate reconstruction of higher-order topology. Applying these scores to a large corpus of data from the DREAM5 challenge, we undertake a data-driven assessment of estimator performance. We find that the “wisdom of crowds” network, that demonstrated superior local performance in the DREAM5 challenge, is also among the best performing methodologies for inference of regulation on multiple length scales.

scholarly journals Towards Engineering Biosystems with Emergent Collective Functions

2020 ◽  
Author(s):  
Thomas E. Gorochowski ◽  
Sabine Hauert ◽  
Jan-Ulrich Kreft ◽  
Lucia Marucci ◽  
Namid R. Stillman ◽  
...  
Keyword(s):  
Synthetic Biology ◽  
Multiple Scales ◽  
Multi Scale ◽  
Multiple Length Scales ◽  
Large Populations ◽  
Multi Agent ◽  
Underlying Mechanisms ◽  
The Individual ◽  
Biological Computation ◽  
Core Features

Many complex behaviours in biological systems emerge from large populations of interacting molecules or cells, generating functions that go beyond the capabilities of the individual parts. Such collective phenomena are of great interest to bioengineers due to their robustness and scalability. However, engineering emergent collective functions is difficult because they arise as a consequence of complex multi-level feedback, which often spans multiple length-scales. Here, we present a perspective on how some of these challenges could be overcome by using multi-agent modelling as a design framework within synthetic biology. Using case studies covering the construction of synthetic ecologies to biological computation and synthetic cellularity, we show how multi-agent modelling can capture the core features of complex multi-scale systems and provide novel insights into the underlying mechanisms which guide emergent functionalities across scales. The ability to unravel design rules underpinning these behaviours offers a means to take synthetic biology beyond single molecules or cells and towards the creation of systems with functions that can only emerge from collectives at multiple scales.

Multi-scale Features based Interpersonal Relation Recognition using Higher-order Graph Neural Network

2021 ◽  
Author(s):  
Jianjun Gao ◽  
Linbo Qing ◽  
Lindong Li ◽  
Yongqiang Cheng ◽  
Yonghong Peng
Keyword(s):  
Neural Network ◽  
Higher Order ◽  
Interpersonal Relation ◽  
Multi Scale

scholarly journals ModularBoost: an efficient network inference algorithm based on module decomposition

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.

Multi-scale simulations of biological systems using the OPEP coarse-grained model

2018 ◽  
Vol 498 (2) ◽  
pp. 296-304 ◽  
Author(s):  
Fabio Sterpone ◽  
Sébastien Doutreligne ◽  
Thanh Thuy Tran ◽  
Simone Melchionna ◽  
Marc Baaden ◽  
...  
Keyword(s):  
Biological Systems ◽  
Coarse Grained ◽  
Coarse Grained Model ◽  
Multi Scale

Osteogenesis Imperfecta: Molecular and Mesoscale Disease Mechanisms

2009 ◽  
Author(s):  
Alfonso Gautieri ◽  
Sebastien Uzel ◽  
Simone Vesentini ◽  
Alberto Redaelli ◽  
Markus J. Buehler
Keyword(s):  
Mechanical Properties ◽  
Osteogenesis Imperfecta ◽  
Multiple Scales ◽  
Genetic Disorder ◽  
Single Point ◽  
Single Point Mutation ◽  
Mesoscale Simulation ◽  
Disease Mechanisms ◽  
Multiple Length Scales ◽  
Collagenous Tissues

Osteogenesis Imperfecta (OI) is a genetic disorder in collagen characterized by mechanically weakened tendon and fragile bones that affects more than 1 in 10,000 individuals. Even though many studies have attempted to associate specific mutation types with phenotypic severity, the mechanisms by which a single point mutation influences the mechanical behavior of tissues at multiple length-scales remain unknown. Here we show by a hierarchy of full atomistic and mesoscale simulation that OI mutations severely compromise the mechanical properties of collagenous tissues at multiple scales, from single molecules to collagen fibrils.

scholarly journals DCPNet: A Densely Connected Pyramid Network for Monocular Depth Estimation

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6780
Author(s):  
Zhitong Lai ◽  
Rui Tian ◽  
Zhiguo Wu ◽  
Nannan Ding ◽  
Linjian Sun ◽  
...  
Keyword(s):  
Multiple Scales ◽  
Feature Fusion ◽  
State Of The Art ◽  
Depth Estimation ◽  
Multi Scale ◽  
Pyramid Structure ◽  
Benchmark Datasets ◽  
The Common ◽  
Monocular Depth ◽  
Multiple Stages

Pyramid architecture is a useful strategy to fuse multi-scale features in deep monocular depth estimation approaches. However, most pyramid networks fuse features only within the adjacent stages in a pyramid structure. To take full advantage of the pyramid structure, inspired by the success of DenseNet, this paper presents DCPNet, a densely connected pyramid network that fuses multi-scale features from multiple stages of the pyramid structure. DCPNet not only performs feature fusion between the adjacent stages, but also non-adjacent stages. To fuse these features, we design a simple and effective dense connection module (DCM). In addition, we offer a new consideration of the common upscale operation in our approach. We believe DCPNet offers a more efficient way to fuse features from multiple scales in a pyramid-like network. We perform extensive experiments using both outdoor and indoor benchmark datasets (i.e., the KITTI and the NYU Depth V2 datasets) and DCPNet achieves the state-of-the-art results.

scholarly journals Evaluating the reproducibility of single-cell gene regulatory network inference algorithms

2020 ◽  
Author(s):  
Yoonjee Kang ◽  
Denis Thieffry ◽  
Laura Cantini
Keyword(s):  
Single Cell ◽  
Network Inference ◽  
Simulated Data ◽  
Ground Truth ◽  
Real Data ◽  
Gene Regulatory Network Inference ◽  
Sequencing Platform ◽  
Cell Network ◽  
Inference Algorithms ◽  
Inference Methods

AbstractNetworks are powerful tools to represent and investigate biological systems. The development of algorithms inferring regulatory interactions from functional genomics data has been an active area of research. With the advent of single-cell RNA-seq data (scRNA-seq), numerous methods specifically designed to take advantage of single-cell datasets have been proposed. However, published benchmarks on single-cell network inference are mostly based on simulated data. Once applied to real data, these benchmarks take into account only a small set of genes and only compare the inferred networks with an imposed ground-truth.Here, we benchmark four single-cell network inference methods based on their reproducibility, i.e. their ability to infer similar networks when applied to two independent datasets for the same biological condition. We tested each of these methods on real data from three biological conditions: human retina, T-cells in colorectal cancer, and human hematopoiesis.GENIE3 results to be the most reproducible algorithm, independently from the single-cell sequencing platform, the cell type annotation system, the number of cells constituting the dataset, or the thresholding applied to the links of the inferred networks. In order to ensure the reproducibility and ease extensions of this benchmark study, we implemented all the analyses in scNET, a Jupyter notebook available at https://github.com/ComputationalSystemsBiology/scNET.

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