AIMC Topic: Computational Biology

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PIPENN: protein interface prediction from sequence with an ensemble of neural nets.

Bioinformatics (Oxford, England)
MOTIVATION: The interactions between proteins and other molecules are essential to many biological and cellular processes. Experimental identification of interface residues is a time-consuming, costly and challenging task, while protein sequence data...

A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers.

Bioinformatics (Oxford, England)
MOTIVATION: Deep learning has revolutionized protein tertiary structure prediction recently. The cutting-edge deep learning methods such as AlphaFold can predict high-accuracy tertiary structures for most individual protein chains. However, the accur...

DeepUMQA: ultrafast shape recognition-based protein model quality assessment using deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Protein model quality assessment is a key component of protein structure prediction. In recent research, the voxelization feature was used to characterize the local structural information of residues, but it may be insufficient for descri...

Adapt-Kcr: a novel deep learning framework for accurate prediction of lysine crotonylation sites based on learning embedding features and attention architecture.

Briefings in bioinformatics
Protein lysine crotonylation (Kcr) is an important type of posttranslational modification that is associated with a wide range of biological processes. The identification of Kcr sites is critical to better understanding their functional mechanisms. H...

ASPIRER: a new computational approach for identifying non-classical secreted proteins based on deep learning.

Briefings in bioinformatics
Protein secretion has a pivotal role in many biological processes and is particularly important for intercellular communication, from the cytoplasm to the host or external environment. Gram-positive bacteria can secrete proteins through multiple secr...

An enhanced cascade-based deep forest model for drug combination prediction.

Briefings in bioinformatics
Combination therapy has shown an obvious curative effect on complex diseases, whereas the search space of drug combinations is too large to be validated experimentally even with high-throughput screens. With the increase of the number of drugs, artif...

Comprehensive Prediction of Lipocalin Proteins Using Artificial Intelligence Strategy.

Frontiers in bioscience (Landmark edition)
BACKGROUND: Lipocalin belongs to the calcyin family, and its sequence length is generally between 165 and 200 residues. They are mainly stable and multifunctional extracellular proteins. Lipocalin plays an important role in several stress responses a...

Prediction of disease-associated nsSNPs by integrating multi-scale ResNet models with deep feature fusion.

Briefings in bioinformatics
More than 6000 human diseases have been recorded to be caused by non-synonymous single nucleotide polymorphisms (nsSNPs). Rapid and accurate prediction of pathogenic nsSNPs can improve our understanding of the principle and design of new drugs, which...

Predicting miRNA-disease associations using an ensemble learning framework with resampling method.

Briefings in bioinformatics
MOTIVATION: Accumulating evidences have indicated that microRNA (miRNA) plays a crucial role in the pathogenesis and progression of various complex diseases. Inferring disease-associated miRNAs is significant to explore the etiology, diagnosis and tr...