Network pharmacology is an emerging area of systematic drug research that attempts to
understand drug actions and interactions with multiple targets. Network pharmacology has …

In cancer diagnostics, a considerable amount of data is acquired during routine work-up.
Recently, machine learning has been used to build classifiers that are tasked with cancer …

Bioimage analysis plays a critical role in extracting information from biological images,
enabling deeper insights into cellular structures and processes. The integration of machine …

Highlights•Semi-supervised learning method for an insect pest monitoring system was
proposed.•Accuracy of the pseudo-labelling algorithm can be as high as …

Appropriate wound management shortens the healing times and reduces the management
costs, benefiting the patient in physical terms and potentially reducing the healthcare …

This paper presented a review on the capabilities of machine learning algorithms toward
Earth observation data modelling and information extraction. The main purpose was to …

Background Precision medicine for cancer treatment relies on an accurate pathological
diagnosis. The number of known tumor classes has increased rapidly, and reliance on …

Background: A high number of patients who are hospitalized with COVID-19 develop acute
respiratory distress syndrome (ARDS). Objective: In response to the need for clinical …

Artificial intelligence (AI) is a vast multidisciplinary field which equip machines with cognitive
powers like ability to perceive reason, learn, abstract, and act. Machine learning (ML) and …

Selecting the most appropriate compounds to synthesize and test is a vital aspect of drug
discovery. Methods like clustering and diversity present weaknesses in selecting the optimal …