scholarly journals Multi-Model and Network Inference Based on Ensemble Estimates: Avoiding the Madness of Crowds

2019 ◽  
Author(s):  
Michael P.H. Stumpf
Keyword(s):  
Systems Biology ◽  
Model Selection ◽  
Recent Progress ◽  
Network Inference ◽  
Applied Mathematics ◽  
System Model ◽  
Great Success ◽  
Computational Statistics ◽  
Inference Methods ◽  
Positive Results

AbstractRecent progress in theoretical systems biology, applied mathematics and computational statistics allows us to compare quantitatively the performance of different candidate models at describing a particular biological system. Model selection has been applied with great success to problems where a small number — typically less than 10 — of models are compared, but recently studies have started to consider thousands and even millions of candidate models. Often, however, we are left with sets of models that are compatible with the data, and then we can use ensembles of models to make predictions. These ensembles can have very desirable characteristics, but as I show here are not guaranteed to improve on individual estimators or predictors. I will show in the cases of model selection and network inference when we can trust ensembles, and when we should be cautious. The analyses suggests that the careful construction of an ensemble – choosing good predictors – is of paramount importance, more than had perhaps been realised before: merely adding different methods does not suffice. The success of ensemble network inference methods is also shown to rest on their ability to suppress false-positive results. A Jupyter notebook which allows carrying out an assessment of ensemble estimators is provided.

scholarly journals Multi-model and network inference based on ensemble estimates: avoiding the madness of crowds

2020 ◽  
Vol 17 (171) ◽  
pp. 20200419
Author(s):  
Michael P. H. Stumpf
Keyword(s):  
Systems Biology ◽  
Model Selection ◽  
Biological System ◽  
Recent Progress ◽  
Network Inference ◽  
Applied Mathematics ◽  
Great Success ◽  
Computational Statistics ◽  
Inference Methods ◽  
Positive Results

Recent progress in theoretical systems biology, applied mathematics and computational statistics allows us to compare the performance of different candidate models at describing a particular biological system quantitatively. Model selection has been applied with great success to problems where a small number—typically less than 10—of models are compared, but recent studies have started to consider thousands and even millions of candidate models. Often, however, we are left with sets of models that are compatible with the data, and then we can use ensembles of models to make predictions. These ensembles can have very desirable characteristics, but as I show here are not guaranteed to improve on individual estimators or predictors. I will show in the cases of model selection and network inference when we can trust ensembles, and when we should be cautious. The analyses suggest that the careful construction of an ensemble—choosing good predictors—is of paramount importance, more than had perhaps been realized before: merely adding different methods does not suffice. The success of ensemble network inference methods is also shown to rest on their ability to suppress false-positive results. A Jupyter notebook which allows carrying out an assessment of ensemble estimators is provided.

Assessing the fit of the multi-species network coalescent to multi-locus data

2020 ◽  
Author(s):  
Ruoyi Cai ◽  
Cécile Ané
Keyword(s):  
Model Selection ◽  
Goodness Of Fit ◽  
Network Inference ◽  
Phylogenetic Network ◽  
Phylogenetic Networks ◽  
Supplementary Information ◽  
Goodness Of Fit Test ◽  
Full Likelihood ◽  
Genome Wide ◽  
Inference Methods

Abstract Motivation With growing genome-wide molecular datasets from next-generation sequencing, phylogenetic networks can be estimated using a variety of approaches. These phylogenetic networks include events like hybridization, gene flow or horizontal gene transfer explicitly. However, the most accurate network inference methods are computationally heavy. Methods that scale to larger datasets do not calculate a full likelihood, such that traditional likelihood-based tools for model selection are not applicable to decide how many past hybridization events best fit the data. We propose here a goodness-of-fit test to quantify the fit between data observed from genome-wide multi-locus data, and patterns expected under the multi-species coalescent model on a candidate phylogenetic network. Results We identified weaknesses in the previously proposed TICR test, and proposed corrections. The performance of our new test was validated by simulations on real-world phylogenetic networks. Our test provides one of the first rigorous tools for model selection, to select the adequate network complexity for the data at hand. The test can also work for identifying poorly inferred areas on a network. Availability and implementation Software for the goodness-of-fit test is available as a Julia package at https://github.com/cecileane/QuartetNetworkGoodnessFit.jl. Supplementary information Supplementary data are available at Bioinformatics online.

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.

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.

scholarly journals BRANE Cut: Biologically-Related A priori Network Enhancement with Graph cuts for Gene Regulatory Network Inference

2015 ◽  
Author(s):  
Aurélie Pirayre ◽  
Camille Couprie ◽  
Frédérique Bidard ◽  
Laurent Duval ◽  
Jean-Christophe Pesquet
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Gene Networks ◽  
Network Inference ◽  
State Of The Art ◽  
A Priori ◽  
Graph Cuts ◽  
Gene Regulatory Network Inference ◽  
Gene Regulatory ◽  
Inference Methods

Background: Inferring gene networks from high-throughput data constitutes an important step in the discovery of relevant regulatory relationships in organism cells. Despite the large number of available Gene Regulatory Network inference methods, the problem remains challenging: the underdetermination in the space of possible solutions requires additional constraints that incorporate a priori information on gene interactions. Methods: Weighting all possible pairwise gene relationships by a probability of edge presence, we formulate the regulatory network inference as a discrete variational problem on graphs. We enforce biologically plausible coupling between groups and types of genes by minimizing an edge labeling functional coding for a priori structures. The optimization is carried out with Graph cuts, an approach popular in image processing and computer vision. We compare the inferred regulatory networks to results achieved by the mutual-information-based Context Likelihood of Relatedness (CLR) method and by the state-of-the-art GENIE3, winner of the DREAM4 multifactorial challenge. Results: Our BRANE Cut approach infers more accurately the five DREAM4 in silico networks (with improvements from 6% to 11%). On a real Escherichia coli compendium, an improvement of 11.8% compared to CLR and 3% compared to GENIE3 is obtained in terms of Area Under Precision-Recall curve. Up to 48 additional verified interactions are obtained over GENIE3 for a given precision. On this dataset involving 4345 genes, our method achieves a performance similar to that of GENIE3, while being more than seven times faster. The BRANE Cut code is available at: http://www-syscom.univ-mlv.fr/~pirayre/Codes-GRN-BRANE-cut.html Conclusions: BRANE Cut is a weighted graph thresholding method. Using biologically sound penalties and data-driven parameters, it improves three state-of-the-art GRN inference methods. It is applicable as a generic network inference post-processing, due its computational efficiency.

scholarly journals Improved High Dimensional Discrete Bayesian Network Inference using Triplet Region Construction

2020 ◽  
Vol 69 ◽  
pp. 231-295
Author(s):  
Peng Lin ◽  
Martin Neil ◽  
Norman Fenton
Keyword(s):  
Network Inference ◽  
General Purpose ◽  
Space Complexity ◽  
High Dimensional ◽  
Choice Problem ◽  
Worst Case ◽  
Exact Inference ◽  
Tree Width ◽  
Inference Methods ◽  
Bayesian Network Inference

Performing efficient inference on high dimensional discrete Bayesian Networks (BNs) is challenging. When using exact inference methods the space complexity can grow exponentially with the tree-width, thus making computation intractable. This paper presents a general purpose approximate inference algorithm, based on a new region belief approximation method, called Triplet Region Construction (TRC). TRC reduces the cluster space complexity for factorized models from worst-case exponential to polynomial by performing graph factorization and producing clusters of limited size. Unlike previous generations of region-based algorithms, TRC is guaranteed to converge and effectively addresses the region choice problem that bedevils other region-based algorithms used for BN inference. Our experiments demonstrate that it also achieves significantly more accurate results than competing algorithms.

scholarly journals Designing and re-designing the e-learning course «Teaching with Episodes of Situated Learning»

2019 ◽  
Vol 11 (2) ◽  
pp. 95-100 ◽  
Author(s):  
Serena Triacca ◽  
Livia Petti ◽  
Pier Cesare Rivoltella
Keyword(s):  
Situated Learning ◽  
Instructional System ◽  
Evaluation Process ◽  
System Model ◽  
Catholic University ◽  
E Learning ◽  
Training Structure ◽  
High Level ◽  
Academic Year ◽  
Positive Results

Abstract In order to satisfy the several training requests regarding the method of Episodes of Situated Learning by teachers of all levels, in the academic year 2018/2019, CREMIT (Catholic University) has developed an e-learning course. This paper aims to describe the training structure, designed according to the ADDIE instructional system model, focusing on the elements such as micro-learning, e-tivities and e-tutoring. The course was delivered in two editions (November–December 2018 and May–June 2019). The evaluation process highlights some relevant aspects: the high level of participants’ satisfaction, the moderate numbers of dropouts and the completely positive results of the assessment activities. The analysis of the gathered data allowed us to re-design the e-learning course.

scholarly journals SimiC: A Single Cell Gene Regulatory Network Inference method with Similarity Constraints

2020 ◽  
Author(s):  
Jianhao Peng ◽  
Ullas V. Chembazhi ◽  
Sushant Bangru ◽  
Ian M. Traniello ◽  
Auinash Kalsotra ◽  
...  
Keyword(s):  
Single Cell ◽  
Network Inference ◽  
Regional Analysis ◽  
Supplementary Information ◽  
Inference Method ◽  
Gene Regulatory Network Inference ◽  
Inference Problem ◽  
Cell State ◽  
Gene Regulatory ◽  
Inference Methods

AbstractMotivationWith the use of single-cell RNA sequencing (scRNA-Seq) technologies, it is now possible to acquire gene expression data for each individual cell in samples containing up to millions of cells. These cells can be further grouped into different states along an inferred cell differentiation path, which are potentially characterized by similar, but distinct enough, gene regulatory networks (GRNs). Hence, it would be desirable for scRNA-Seq GRN inference methods to capture the GRN dynamics across cell states. However, current GRN inference methods produce a unique GRN per input dataset (or independent GRNs per cell state), failing to capture these regulatory dynamics.ResultsWe propose a novel single-cell GRN inference method, named SimiC, that jointly infers the GRNs corresponding to each state. SimiC models the GRN inference problem as a LASSO optimization problem with an added similarity constraint, on the GRNs associated to contiguous cell states, that captures the inter-cell-state homogeneity. We show on a mouse hepatocyte single-cell data generated after partial hepatectomy that, contrary to previous GRN methods for scRNA-Seq data, SimiC is able to capture the transcription factor (TF) dynamics across liver regeneration, as well as the cell-level behavior for the regulatory program of each TF across cell states. In addition, on a honey bee scRNA-Seq experiment, SimiC is able to capture the increased heterogeneity of cells on whole-brain tissue with respect to a regional analysis tissue, and the TFs associated specifically to each sequenced tissue.AvailabilitySimiC is written in Python and includes an R API. It can be downloaded from https://github.com/jianhao2016/[email protected], [email protected] informationSupplementary data are available at the code repository.

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