Recently, molecular fingerprints extracted from three-dimensional (3D) structures using advanced mathematics, such as algebraic topology, differential geometry, and graph theory have been paired with efficient machine learning, especially deep learning algorithms to outperform other methods in drug discovery applications and competitions. This raises the question of whether classical 2D fingerprints are still valuable in computer-aided drug discovery. This work considers 23 datasets associated with four typical problems, namely protein–ligand binding, toxicity, solubility and partition coefficient to assess the performance of eight 2D fingerprints. Advanced machine learning algorithms including random forest, gradient boosted decision tree, single-task deep neural network and multitask deep neural network are employed to construct efficient 2D-fingerprint based models. Additionally, appropriate consensus models are built to further enhance the performance of 2D-fingerprint-based methods. It is demonstrated that 2D-fingerprint-based models perform as well as the state-of-the-art 3D structure-based models for the predictions of toxicity, solubility, partition coefficient and protein–ligand binding affinity based on only ligand information. However, 3D structure-based models outperform 2D fingerprint-based methods in complex-based protein–ligand binding affinity predictions.
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Consensus over evolutionary graphs
We establish average consensus on graphs with
dynamic topologies prescribed by evolutionary games among
strategic agents. Each agent possesses a private reward function
and dynamically decides whether to create new links and/or
whether to delete existing ones in a selfish and decentralized
fashion, as indicated by a certain randomized mechanism. This
model incurs a time-varying and state-dependent graph topology
for which traditional consensus analysis is not applicable.
We prove asymptotic average consensus almost surely and in
mean square for any initial condition and graph topology. In
addition, we establish exponential convergence in expectation.
Our results are validated via simulation studies on random
networks.
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- Award ID(s):
- 1717207
- NSF-PAR ID:
- 10098522
- Date Published:
- Journal Name:
- Consensus over evolutionary graphs
- Page Range / eLocation ID:
- 2218 to 2223
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Graphs are the dominant formalism for modeling multi-agent systems. The algebraic connectivity of a graph is particularly important because it provides the convergence rates of consensus algorithms that underlie many multi-agent control and optimization techniques. However, sharing the value of algebraic connectivity can inadvertently reveal sensitive information about the topology of a graph, such as connections in social networks. Therefore, in this work we present a method to release a graph’s algebraic connectivity under a graph-theoretic form of differential privacy, called edge differential privacy. Edge differential privacy obfuscates differences among graphs’ edge sets and thus conceals the absence or presence of sensitive connections therein. We provide privacy with bounded Laplace noise, which improves accuracy relative to conventional unbounded noise. The private algebraic connectivity values are analytically shown to provide accurate estimates of consensus convergence rates, as well as accurate bounds on the diameter of a graph and the mean distance between its nodes. Simulation results confirm the utility of private algebraic connectivity in these contexts.more » « less
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In distributed machine learning, where agents collaboratively learn from diverse private data sets, there is a fundamental tension between consensus and optimality . In this paper, we build on recent algorithmic progresses in distributed deep learning to explore various consensus-optimality trade-offs over a fixed communication topology. First, we propose the incremental consensus -based distributed stochastic gradient descent (i-CDSGD) algorithm, which involves multiple consensus steps (where each agent communicates information with its neighbors) within each SGD iteration. Second, we propose the generalized consensus -based distributed SGD (g-CDSGD) algorithm that enables us to navigate the full spectrum from complete consensus (all agents agree) to complete disagreement (each agent converges to individual model parameters). We analytically establish convergence of the proposed algorithms for strongly convex and nonconvex objective functions; we also analyze the momentum variants of the algorithms for the strongly convex case. We support our algorithms via numerical experiments, and demonstrate significant improvements over existing methods for collaborative deep learning.more » « less
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Abstract Background Characterizing the topology of gene regulatory networks (GRNs) is a fundamental problem in systems biology. The advent of single cell technologies has made it possible to construct GRNs at finer resolutions than bulk and microarray datasets. However, cellular heterogeneity and sparsity of the single cell datasets render void the application of regular Gaussian assumptions for constructing GRNs. Additionally, most GRN reconstruction approaches estimate a single network for the entire data. This could cause potential loss of information when single cell datasets are generated from multiple treatment conditions/disease states.
Results To better characterize single cell GRNs under different but related conditions, we propose the joint estimation of multiple networks using multiple signed graph learning (scMSGL). The proposed method is based on recently developed graph signal processing (GSP) based graph learning, where GRNs and gene expressions are modeled as signed graphs and graph signals, respectively. scMSGL learns multiple GRNs by optimizing the total variation of gene expressions with respect to GRNs while ensuring that the learned GRNs are similar to each other through regularization with respect to a learned signed consensus graph. We further kernelize scMSGL with the kernel selected to suit the structure of single cell data.
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Premise Large genomic data sets offer the promise of resolving historically recalcitrant species relationships. However, different methodologies can yield conflicting results, especially when clades have experienced ancient, rapid diversification. Here, we analyzed the ancient radiation of Ericales and explored sources of uncertainty related to species tree inference, conflicting gene tree signal, and the inferred placement of gene and genome duplications.
Methods We used a hierarchical clustering approach, with tree‐based homology and orthology detection, to generate six filtered phylogenomic matrices consisting of data from 97 transcriptomes and genomes. Support for species relationships was inferred from multiple lines of evidence including shared gene duplications, gene tree conflict, gene‐wise edge‐based analyses, concatenation, and coalescent‐based methods, and is summarized in a consensus framework.
Results Our consensus approach supported a topology largely concordant with previous studies, but suggests that the data are not capable of resolving several ancient relationships because of lack of informative characters, sensitivity to methodology, and extensive gene tree conflict correlated with paleopolyploidy. We found evidence of a whole‐genome duplication before the radiation of all or most ericalean families, and demonstrate that tree topology and heterogeneous evolutionary rates affect the inferred placement of genome duplications.
Conclusions We provide several hypotheses regarding the history of Ericales, and confidently resolve most nodes, but demonstrate that a series of ancient divergences are unresolvable with these data. Whether paleopolyploidy is a major source of the observed phylogenetic conflict warrants further investigation.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.23919/ECC.2018.8550392
