AIMC Topic: Computational Biology

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Machine learning in computational biology to accelerate high-throughput protein expression.

Bioinformatics (Oxford, England)
MOTIVATION: The Human Protein Atlas (HPA) enables the simultaneous characterization of thousands of proteins across various tissues to pinpoint their spatial location in the human body. This has been achieved through transcriptomics and high-throughp...

T-GOWler: Discovering Generalized Process Models Within Texts.

Journal of computational biology : a journal of computational molecular cell biology
Contemporary workflow management systems are driven by explicit process models specifying the interdependencies between tasks. Creating these models is a challenging and time-consuming task. Existing approaches to mining concrete workflows into model...

Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations.

Bioinformatics (Oxford, England)
MOTIVATION: A heterogeneous network topology possessing abundant interactions between biomedical entities has yet to be utilized in similarity-based methods for predicting drug-target associations based on the array of varying features of drugs and t...

NeBcon: protein contact map prediction using neural network training coupled with naïve Bayes classifiers.

Bioinformatics (Oxford, England)
MOTIVATION: Recent CASP experiments have witnessed exciting progress on folding large-size non-humongous proteins with the assistance of co-evolution based contact predictions. The success is however anecdotal due to the requirement of the contact pr...

iMulti-HumPhos: a multi-label classifier for identifying human phosphorylated proteins using multiple kernel learning based support vector machines.

Molecular bioSystems
Protein phosphorylation plays a potential role in regulating protein conformation and functions. As a result, identifying an uncharacterized protein sequence as a phosphorylated protein is a very meaningful problem and an urgent issue for both basic ...

When loss-of-function is loss of function: assessing mutational signatures and impact of loss-of-function genetic variants.

Bioinformatics (Oxford, England)
MOTIVATION: Loss-of-function genetic variants are frequently associated with severe clinical phenotypes, yet many are present in the genomes of healthy individuals. The available methods to assess the impact of these variants rely primarily upon evol...

TITER: predicting translation initiation sites by deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Translation initiation is a key step in the regulation of gene expression. In addition to the annotated translation initiation sites (TISs), the translation process may also start at multiple alternative TISs (including both AUG and non-A...

Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding.

Bioinformatics (Oxford, England)
MOTIVATION: Experimental techniques for measuring chromatin accessibility are expensive and time consuming, appealing for the development of computational approaches to predict open chromatin regions from DNA sequences. Along this direction, existing...

Rectified factor networks for biclustering of omics data.

Bioinformatics (Oxford, England)
MOTIVATION: Biclustering has become a major tool for analyzing large datasets given as matrix of samples times features and has been successfully applied in life sciences and e-commerce for drug design and recommender systems, respectively. actor nal...