AIMC Topic: Computational Biology

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Predicting protein-protein interactions from protein sequences by a stacked sparse autoencoder deep neural network.

Molecular bioSystems
Protein-protein interactions (PPIs) play an important role in most of the biological processes. How to correctly and efficiently detect protein interaction is a problem that is worth studying. Although high-throughput technologies provide the possibi...

AptRank: an adaptive PageRank model for protein function prediction on   bi-relational graphs.

Bioinformatics (Oxford, England)
MOTIVATION: Diffusion-based network models are widely used for protein function prediction using protein network data and have been shown to outperform neighborhood-based and module-based methods. Recent studies have shown that integrating the hierar...

Automated cell type discovery and classification through knowledge transfer.

Bioinformatics (Oxford, England)
MOTIVATION: Recent advances in mass cytometry allow simultaneous measurements of up to 50 markers at single-cell resolution. However, the high dimensionality of mass cytometry data introduces computational challenges for automated data analysis and h...

ISPRED4: interaction sites PREDiction in protein structures with a refining grammar model.

Bioinformatics (Oxford, England)
MOTIVATION: The identification of protein-protein interaction (PPI) sites is an important step towards the characterization of protein functional integration in the cell complexity. Experimental methods are costly and time-consuming and computational...

Finding useful data across multiple biomedical data repositories using DataMed.

Nature genetics
The value of broadening searches for data across multiple repositories has been identified by the biomedical research community. As part of the US National Institutes of Health (NIH) Big Data to Knowledge initiative, we work with an international com...

ProQ3D: improved model quality assessments using deep learning.

Bioinformatics (Oxford, England)
SUMMARY: Protein quality assessment is a long-standing problem in bioinformatics. For more than a decade we have developed state-of-art predictors by carefully selecting and optimising inputs to a machine learning method. The correlation has increase...

Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Loops are often vital for protein function, however, their irregular structures make them difficult to model accurately. Current loop modelling algorithms can mostly be divided into two categories: knowledge-based, where databases of frag...