Protein-protein interaction site prediction by model ensembling with hybrid feature and self-attention.

Background: Protein-protein interactions (PPIs) are crucial in various biological functions and cellular processes. Thus, many computational approaches have been proposed to predict PPI sites. Although significant progress has been made, these methods still have limitations in encoding the characteristics of each amino acid in sequences.

Identification of gene in and validation of its function in .

Unlabelled: Root systems anchor plants to the substrate in addition to transporting water and nutrients, playing a fundamental role in plant survival. The gene mediates gravity signal transduction and participates in root and shoot development and auxin flow in many plants. In this study, a regulator, LsLAZY1, was identified from based on previous transcriptome data.

A survey on protein-DNA-binding sites in computational biology.

Transcription factors are important cellular components of the process of gene expression control. Transcription factor binding sites are locations where transcription factors specifically recognize DNA sequences, targeting gene-specific regions and recruiting transcription factors or chromatin regulators to fine-tune spatiotemporal gene regulation. As the common proteins, transcription factors play a meaningful role in life-related activities.

Multiple Protein Subcellular Locations Prediction Based on Deep Convolutional Neural Networks with Self-Attention Mechanism.

As an important research field in bioinformatics, protein subcellular location prediction is critical to reveal the protein functions and provide insightful information for disease diagnosis and drug development. Predicting protein subcellular locations remains a challenging task due to the difficulty of finding representative features and robust classifiers. Many feature fusion methods have been widely applied to tackle the above issues.

Self-evoluting framework of deep convolutional neural network for multilocus protein subcellular localization.

In the present paper, deep convolutional neural network (DCNN) is applied to multilocus protein subcellular localization as it is more suitable for multi-class classification. There are two main problems with this application. First, the appropriate features for correlation between multiple sites are hard to find.