AIMC Topic: Machine Learning

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Computational anti-COVID-19 drug design: progress and challenges.

Briefings in bioinformatics
Vaccines have made gratifying progress in preventing the 2019 coronavirus disease (COVID-19) pandemic. However, the emergence of variants, especially the latest delta variant, has brought considerable challenges to human health. Hence, the developmen...

Challenges and opportunities in network-based solutions for biological questions.

Briefings in bioinformatics
Network biology is useful for modeling complex biological phenomena; it has attracted attention with the advent of novel graph-based machine learning methods. However, biological applications of network methods often suffer from inadequate follow-up....

Machine learning meets omics: applications and perspectives.

Briefings in bioinformatics
The innovation of biotechnologies has allowed the accumulation of omics data at an alarming rate, thus introducing the era of 'big data'. Extracting inherent valuable knowledge from various omics data remains a daunting problem in bioinformatics. Bet...

Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies.

Briefings in bioinformatics
SARS-CoV-2 caused the first severe pandemic of the digital era. Computational approaches have been ubiquitously used in an attempt to timely and effectively cope with the resulting global health crisis. In order to extensively assess such contributio...

epitope3D: a machine learning method for conformational B-cell epitope prediction.

Briefings in bioinformatics
The ability to identify antigenic determinants of pathogens, or epitopes, is fundamental to guide rational vaccine development and immunotherapies, which are particularly relevant for rapid pandemic response. A range of computational tools has been d...

Identifying multi-functional bioactive peptide functions using multi-label deep learning.

Briefings in bioinformatics
The bioactive peptide has wide functions, such as lowering blood glucose levels and reducing inflammation. Meanwhile, computational methods such as machine learning are becoming more and more important for peptide functions prediction. Most of the pr...

An overview of machine learning methods for monotherapy drug response prediction.

Briefings in bioinformatics
For an increasing number of preclinical samples, both detailed molecular profiles and their responses to various drugs are becoming available. Efforts to understand, and predict, drug responses in a data-driven manner have led to a proliferation of m...

Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2.

Briefings in bioinformatics
Coronavirus disease 2019 (COVID-19) has impacted public health as well as societal and economic well-being. In the last two decades, various prediction algorithms and tools have been developed for predicting antiviral peptides (AVPs). The current COV...

Stratified neural networks in a time-to-event setting.

Briefings in bioinformatics
Deep neural networks are frequently employed to predict survival conditional on omics-type biomarkers, e.g., by employing the partial likelihood of Cox proportional hazards model as loss function. Due to the generally limited number of observations i...

DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations.

Briefings in bioinformatics
MOTIVATION: Drug combination therapy has become an increasingly promising method in the treatment of cancer. However, the number of possible drug combinations is so huge that it is hard to screen synergistic drug combinations through wet-lab experime...