AIMC Topic: Computational Biology

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Deep learning improves antimicrobial peptide recognition.

Bioinformatics (Oxford, England)
MOTIVATION: Bacterial resistance to antibiotics is a growing concern. Antimicrobial peptides (AMPs), natural components of innate immunity, are popular targets for developing new drugs. Machine learning methods are now commonly adopted by wet-laborat...

[From symptom to syndrome using modern software support].

Der Internist
Diagnosing rare diseases can be challenging for clinicians. This article gives an overview on novel approaches, which enable automated phenotype-driven analyses of differential diagnoses for rare diseases as well as genomic variation data of affected...

Assessing Deep and Shallow Learning Methods for Quantitative Prediction of Acute Chemical Toxicity.

Toxicological sciences : an official journal of the Society of Toxicology
Animal-based methods for assessing chemical toxicity are struggling to meet testing demands. In silico approaches, including machine-learning methods, are promising alternatives. Recently, deep neural networks (DNNs) were evaluated and reported to ou...

Learned protein embeddings for machine learning.

Bioinformatics (Oxford, England)
MOTIVATION: Machine-learning models trained on protein sequences and their measured functions can infer biological properties of unseen sequences without requiring an understanding of the underlying physical or biological mechanisms. Such models enab...

DeepSol: a deep learning framework for sequence-based protein solubility prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Protein solubility plays a vital role in pharmaceutical research and production yield. For a given protein, the extent of its solubility can represent the quality of its function, and is ultimately defined by its sequence. Thus, it is imp...

CoABind: a novel algorithm for Coenzyme A (CoA)- and CoA derivatives-binding residues prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Coenzyme A (CoA)-protein binding plays an important role in various cellular functions and metabolic pathways. However, no computational methods can be employed for CoA-binding residues prediction.

Cost function network-based design of protein-protein interactions: predicting changes in binding affinity.

Bioinformatics (Oxford, England)
MOTIVATION: Accurate and economic methods to predict change in protein binding free energy upon mutation are imperative to accelerate the design of proteins for a wide range of applications. Free energy is defined by enthalpic and entropic contributi...

SECLAF: a webserver and deep neural network design tool for hierarchical biological sequence classification.

Bioinformatics (Oxford, England)
SUMMARY: Artificial intelligence tools are gaining more and more ground each year in bioinformatics. Learning algorithms can be taught for specific tasks by using the existing enormous biological databases, and the resulting models can be used for th...