AIMC Topic: Neural Networks, Computer

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Perspectives on Computation in Plants.

Artificial life
Plants thrive in virtually all natural and human-adapted environments and are becoming popular models for developing robotics systems because of their strategies of morphological and behavioral adaptation. Such adaptation and high plasticity offer ne...

Design of synthetic promoters for cyanobacteria with generative deep-learning model.

Nucleic acids research
Deep generative models, which can approximate complex data distribution from large datasets, are widely used in biological dataset analysis. In particular, they can identify and unravel hidden traits encoded within a complicated nucleotide sequence, ...

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction.

Briefings in bioinformatics
Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect...

DeepAlgPro: an interpretable deep neural network model for predicting allergenic proteins.

Briefings in bioinformatics
Allergies have become an emerging public health problem worldwide. The most effective way to prevent allergies is to find the causative allergen at the source and avoid re-exposure. However, most of the current computational methods used to identify ...

MPCLCDA: predicting circRNA-disease associations by using automatically selected meta-path and contrastive learning.

Briefings in bioinformatics
Circular RNA (circRNA) is closely associated with human diseases. Accordingly, identifying the associations between human diseases and circRNA can help in disease prevention, diagnosis and treatment. Traditional methods are time consuming and laborio...

DeepSTF: predicting transcription factor binding sites by interpretable deep neural networks combining sequence and shape.

Briefings in bioinformatics
Precise targeting of transcription factor binding sites (TFBSs) is essential to comprehending transcriptional regulatory processes and investigating cellular function. Although several deep learning algorithms have been created to predict TFBSs, the ...

TIMER is a Siamese neural network-based framework for identifying both general and species-specific bacterial promoters.

Briefings in bioinformatics
BACKGROUND: Promoters are DNA regions that initiate the transcription of specific genes near the transcription start sites. In bacteria, promoters are recognized by RNA polymerases and associated sigma factors. Effective promoter recognition is essen...

MCFF-MTDDI: multi-channel feature fusion for multi-typed drug-drug interaction prediction.

Briefings in bioinformatics
Adverse drug-drug interactions (DDIs) have become an increasingly serious problem in the medical and health system. Recently, the effective application of deep learning and biomedical knowledge graphs (KGs) have improved the DDI prediction performanc...

MMSMAPlus: a multi-view multi-scale multi-attention embedding model for protein function prediction.

Briefings in bioinformatics
Protein is the most important component in organisms and plays an indispensable role in life activities. In recent years, a large number of intelligent methods have been proposed to predict protein function. These methods obtain different types of pr...

MITNet: a fusion transformer and convolutional neural network architecture approach for T-cell epitope prediction.

Briefings in bioinformatics
Classifying epitopes is essential since they can be applied in various fields, including therapeutics, diagnostics and peptide-based vaccines. To determine the epitope or peptide against an antibody, epitope mapping with peptides is the most extensiv...