AIMC Topic: Computational Biology

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Towards reliable named entity recognition in the biomedical domain.

Bioinformatics (Oxford, England)
MOTIVATION: Automatic biomedical named entity recognition (BioNER) is a key task in biomedical information extraction. For some time, state-of-the-art BioNER has been dominated by machine learning methods, particularly conditional random fields (CRFs...

DeepMito: accurate prediction of protein sub-mitochondrial localization using convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: The correct localization of proteins in cell compartments is a key issue for their function. Particularly, mitochondrial proteins are physiologically active in different compartments and their aberrant localization contributes to the path...

CoCoScore: context-aware co-occurrence scoring for text mining applications using distant supervision.

Bioinformatics (Oxford, England)
MOTIVATION: Information extraction by mining the scientific literature is key to uncovering relations between biomedical entities. Most existing approaches based on natural language processing extract relations from single sentence-level co-mentions,...

Protein contact prediction using metagenome sequence data and residual neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Almost all protein residue contact prediction methods rely on the availability of deep multiple sequence alignments (MSAs). However, many proteins from the poorly populated families do not have sufficient number of homologs in the convent...

Scaling tree-based automated machine learning to biomedical big data with a feature set selector.

Bioinformatics (Oxford, England)
MOTIVATION: Automated machine learning (AutoML) systems are helpful data science assistants designed to scan data for novel features, select appropriate supervised learning models and optimize their parameters. For this purpose, Tree-based Pipeline O...

deepDR: a network-based deep learning approach to in silico drug repositioning.

Bioinformatics (Oxford, England)
MOTIVATION: Traditional drug discovery and development are often time-consuming and high risk. Repurposing/repositioning of approved drugs offers a relatively low-cost and high-efficiency approach toward rapid development of efficacious treatments. T...

Prediction of S-nitrosylation sites by integrating support vector machines and random forest.

Molecular omics
Cysteine S-nitrosylation is a type of reversible post-translational modification of proteins, which controls diverse biological processes. It is associated with redox-based cellular signaling to protect against oxidative stress. The identification of...

PVsiRNAPred: Prediction of plant exclusive virus-derived small interfering RNAs by deep convolutional neural network.

Journal of bioinformatics and computational biology
Plant exclusive virus-derived small interfering RNAs (vsiRNAs) regulate various biological processes, especially important in antiviral immunity. The identification of plant vsiRNAs is important for understanding the biogenesis and function mechanism...

Fuzzy cognitive map based approach for determining the risk of ischemic stroke.

IET systems biology
Stroke is the third major cause of mortality in the world. The diagnosis of stroke is a very complex issue considering controllable and uncontrollable factors. These factors include age, sex, blood pressure, diabetes, obesity, heart disease, smoking,...

Editorial: Are computers going to take over: implications of machine learning and computational psychiatry for trainees and practising clinicians.

Journal of child psychology and psychiatry, and allied disciplines
Are computers going to take over? In some ways they already have, and they are bound to take over more. As clinicians and researchers, what do we need to do in order to handle, trust, and evaluate intelligent machines?