AIMC Topic: Computational Biology

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DeepLoc: prediction of protein subcellular localization using deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: The prediction of eukaryotic protein subcellular localization is a well-studied topic in bioinformatics due to its relevance in proteomics research. Many machine learning methods have been successfully applied in this task, but in most of...

Gating mass cytometry data by deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Mass cytometry or CyTOF is an emerging technology for high-dimensional multiparameter single cell analysis that overcomes many limitations of fluorescence-based flow cytometry. New methods for analyzing CyTOF data attempt to improve autom...

Partitioned learning of deep Boltzmann machines for SNP data.

Bioinformatics (Oxford, England)
MOTIVATION: Learning the joint distributions of measurements, and in particular identification of an appropriate low-dimensional manifold, has been found to be a powerful ingredient of deep leaning approaches. Yet, such approaches have hardly been ap...

OLS Client and OLS Dialog: Open Source Tools to Annotate Public Omics Datasets.

Proteomics
The availability of user-friendly software to annotate biological datasets and experimental details is becoming essential in data management practices, both in local storage systems and in public databases. The Ontology Lookup Service (OLS, http://ww...

Using neural networks for reducing the dimensions of single-cell RNA-Seq data.

Nucleic acids research
While only recently developed, the ability to profile expression data in single cells (scRNA-Seq) has already led to several important studies and findings. However, this technology has also raised several new computational challenges. These include ...

Differential privacy-based evaporative cooling feature selection and classification with relief-F and random forests.

Bioinformatics (Oxford, England)
MOTIVATION: Classification of individuals into disease or clinical categories from high-dimensional biological data with low prediction error is an important challenge of statistical learning in bioinformatics. Feature selection can improve classific...

Multi-label classifier based on histogram of gradients for predicting the anatomical therapeutic chemical class/classes of a given compound.

Bioinformatics (Oxford, England)
MOTIVATION: Given an unknown compound, is it possible to predict its Anatomical Therapeutic Chemical class/classes? This is a challenging yet important problem since such a prediction could be used to deduce not only a compound's possible active ingr...

Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility.

Bioinformatics (Oxford, England)
MOTIVATION: The accuracy of predicting protein local and global structural properties such as secondary structure and solvent accessible surface area has been stagnant for many years because of the challenge of accounting for non-local interactions b...