AIMC Topic: Neural Networks, Computer

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Identifying nonadditive contributions to the hydrophobicity of chemically heterogeneous surfaces via dual-loop active learning.

The Journal of chemical physics
Hydrophobic interactions drive numerous biological and synthetic processes. The materials used in these processes often possess chemically heterogeneous surfaces that are characterized by diverse chemical groups positioned in close proximity at the n...

Implicit Contact Dynamics Modeling With Explicit Inertia Matrix Representation for Real-Time, Model-Based Control in Physical Environment.

Neural computation
Model-based control has great potential for use in real robots due to its high sampling efficiency. Nevertheless, dealing with physical contacts and generating accurate motions are inevitable for practical robot control tasks, such as precise manipul...

Nonconvex Sparse Regularization for Deep Neural Networks and Its Optimality.

Neural computation
Recent theoretical studies proved that deep neural network (DNN) estimators obtained by minimizing empirical risk with a certain sparsity constraint can attain optimal convergence rates for regression and classification problems. However, the sparsit...

PBDiff: Neural network based program-wide diffing method for binaries.

Mathematical biosciences and engineering : MBE
Program-wide binary code diffing is widely used in the binary analysis field, such as vulnerability detection. Mature tools, including BinDiff and TurboDiff, make program-wide diffing using rigorous comparison basis that varies across versions, optim...

CoCoPRED: coiled-coil protein structural feature prediction from amino acid sequence using deep neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Coiled-coil is composed of two or more helices that are wound around each other. It widely exists in proteins and has been discovered to play a variety of critical roles in biology processes. Generally, there are three types of structural...

EDCNN: identification of genome-wide RNA-binding proteins using evolutionary deep convolutional neural network.

Bioinformatics (Oxford, England)
MOTIVATION: RNA-binding proteins (RBPs) are a group of proteins associated with RNA regulation and metabolism, and play an essential role in mediating the maturation, transport, localization and translation of RNA. Recently, Genome-wide RNA-binding e...

DULoc: quantitatively unmixing protein subcellular location patterns in immunofluorescence images based on deep learning features.

Bioinformatics (Oxford, England)
MOTIVATION: Knowledge of subcellular locations of proteins is of great significance for understanding their functions. The multi-label proteins that simultaneously reside in or move between more than one subcellular structure usually involve with com...

DeepTrio: a ternary prediction system for protein-protein interaction using mask multiple parallel convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-protein interaction (PPI), as a relative property, is determined by two binding proteins, which brings a great challenge to design an expert model with an unbiased learning architecture and a superior generalization performance. A...

HyperAttentionDTI: improving drug-protein interaction prediction by sequence-based deep learning with attention mechanism.

Bioinformatics (Oxford, England)
MOTIVATION: Identifying drug-target interactions (DTIs) is a crucial step in drug repurposing and drug discovery. Accurately identifying DTIs in silico can significantly shorten development time and reduce costs. Recently, many sequence-based methods...

Multi-instance learning of graph neural networks for aqueous pKa prediction.

Bioinformatics (Oxford, England)
MOTIVATION: The acid dissociation constant (pKa) is a critical parameter to reflect the ionization ability of chemical compounds and is widely applied in a variety of industries. However, the experimental determination of pKa is intricate and time-co...