Hyper-parameter Optimization Using Continuation Algorithms

Rojas-Delgado, Jairo; Jiménez, J. A.; Bello, Rafael; Lozano, José A.

doi:10.1007/978-3-031-26504-4_26

Cited by 1 publication

(1 citation statement)

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“…Promising recent approaches include score-function methods with control variates [27], variational optimization (VO) [28], and scale-space-based continuation approaches [29]. Such approaches have been applied in the context of reinforcement learning [27], graph matching [30], hyperparameter optimization [31] and differentiable deep neural network training [28], but there has been limited application of such techniques specifically to GNN architectures. In this work, we introduce an efficient VO approach, based on optimization of an smoothed objective function using a Gaussian scale-space [32].…”

Section: Introductionmentioning

confidence: 99%

A Variational Graph Partitioning Approach to Modeling Protein Liquid-liquid Phase Separation

Wang,

Warrell,

Zheng

et al. 2024

Preprint

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Protein Liquid-Liquid Phase Separation (LLPS) plays an essential role in cellular processes and is known to be associated with various diseases. However, our understanding of this enigmatic phenomena remains limited. In this work, we propose a graph-neural-network(GNN)-based interpretable machine learning approach to study the intricate nature of protein structure-function relationships associated with LLPS. For many protein properties of interest, information relevant to the property is expected to be confined to local domains. For LLPS proteins, the presence of intrinsically disordered regions (IDR)s in the molecule is arguably the most important information; an adaptive GNN model which preferentially shares information within such units and avoids mixing in information from other parts of the molecule may thus enhance the prediction of LLPS proteins. To allow for the accentuation of domain restricted information, we propose a novel graph-based model with the ability to partition each protein graph into task-dependent subgraphs. Such a model is designed not only to achieve better predictive performance but also to be highly interpretable, and thus have the ability to suggest novel biological insights. In addition to achieving state-of-the-art results on the prediction of LLPS proteins from protein structure for both regulator and scaffold proteins, we examine the properties of the graph partitions identified by our model, showing these to be consistent with the annotated IDRs believed to be largely responsible for LLPS. Moreover, our method is designed in a generic way such that it can be applied to other graph-based predictive tasks with minimal adaption.

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