AIMC Topic: Neural Networks, Computer

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AIM-CICs: an automatic identification method for cell-in-cell structures based on convolutional neural network.

Journal of molecular cell biology
Edited by Luonan Chen Whereas biochemical markers are available for most types of cell death, current studies on non-autonomous cell death by entosis rely strictly on the identification of cell-in-cell structures (CICs), a unique morphological readou...

Physics-informed neural network for phase imaging based on transport of intensity equation.

Optics express
Non-interferometric quantitative phase imaging based on Transport of Intensity Equation (TIE) has been widely used in bio-medical imaging. However, analytic TIE phase retrieval is prone to low-spatial frequency noise amplification, which is caused by...

Instantaneous photosynthetically available radiation models for ocean waters using neural networks.

Applied optics
Instantaneous photosynthetically available radiation (IPAR) at the ocean surface and its vertical profile below the surface play a critical role in models to calculate net primary productivity of marine phytoplankton. In this work, we report two IPAR...

MNMDCDA: prediction of circRNA-disease associations by learning mixed neighborhood information from multiple distances.

Briefings in bioinformatics
Emerging evidence suggests that circular RNA (circRNA) is an important regulator of a variety of pathological processes and serves as a promising biomarker for many complex human diseases. Nevertheless, there are relatively few known circRNA-disease ...

A new framework for drug-disease association prediction combing light-gated message passing neural network and gated fusion mechanism.

Briefings in bioinformatics
With the development of research on the complex aetiology of many diseases, computational drug repositioning methodology has proven to be a shortcut to costly and inefficient traditional methods. Therefore, developing more promising computational met...

MHADTI: predicting drug-target interactions via multiview heterogeneous information network embedding with hierarchical attention mechanisms.

Briefings in bioinformatics
MOTIVATION: Discovering the drug-target interactions (DTIs) is a crucial step in drug development such as the identification of drug side effects and drug repositioning. Since identifying DTIs by web-biological experiments is time-consuming and costl...

ICSDA: a multi-modal deep learning model to predict breast cancer recurrence and metastasis risk by integrating pathological, clinical and gene expression data.

Briefings in bioinformatics
Breast cancer patients often have recurrence and metastasis after surgery. Predicting the risk of recurrence and metastasis for a breast cancer patient is essential for the development of precision treatment. In this study, we proposed a novel multi-...

Predicting cell line-specific synergistic drug combinations through a relational graph convolutional network with attention mechanism.

Briefings in bioinformatics
Identifying synergistic drug combinations (SDCs) is a great challenge due to the combinatorial complexity and the fact that SDC is cell line specific. The existing computational methods either did not consider the cell line specificity of SDC, or did...

FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction.

Briefings in bioinformatics
Accurate prediction of molecular properties, such as physicochemical and bioactive properties, as well as ADME/T (absorption, distribution, metabolism, excretion and toxicity) properties, remains a fundamental challenge for molecular design, especial...

Deep convolutional neural networks for generating atomistic configurations of multi-component macromolecules from coarse-grained models.

The Journal of chemical physics
Despite the modern advances in the available computational resources, the length and time scales of the physical systems that can be studied in full atomic detail, via molecular simulations, are still limited. To overcome such limitations, coarse-gra...