In the era of antimicrobial resistance, the prevalence of multidrug-resistant microorganisms
that resist conventional antibiotic treatment has steadily increased. Thus, it is now …

Highlights•Machine learning is increasingly used to predict and understand drug resistance
in HIV.•Phylogenetics helps to track the emergence and spread of HIV drug resistance …

Artificial intelligence (AI) will pave the way to a new era in medicine. However, currently
available AI systems do not interact with a patient, eg, for anamnesis, and thus are only used …

Federated learning enables collaboration in medicine, where data is scattered across
multiple centers without the need to aggregate the data in a central cloud. While, in general …

The fast replication rate and lack of repair mechanisms of human immunodeficiency virus
(HIV) contribute to its high mutation frequency, with some mutations resulting in the evolution …

Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face
in the future. There have been various attempts to preserve the efficacy of existing …

Individuals infected with HIV are controlled by drugs known as antiretroviral therapy by
suppressing the amount of HIV in the body. Therefore, studies to predict both HIV-drug …

Identifying resistance to antiretroviral drugs is crucial for ensuring the successful treatment of
patients infected with viruses such as human immunodeficiency virus (HIV) or hepatitis C …

Background Antiretroviral drugs are a very effective therapy against HIV infection. However,
the high mutation rate of HIV permits the emergence of variants that can be resistant to the …

Predicting viral drug resistance is a significant medical concern. The importance of this
problem stimulates the continuous development of experimental and new computational …