To understand the intricacies of microorganisms at the molecular level requires making sense of copious volumes of data such that it may now be humanly impossible to detect insightful data patterns without an artificial intelligence application called machine learning. Applying machine learning to address biological problems is expected to grow at an unprecedented rate, yet it is perceived by the uninitiated as a mysterious and daunting entity entrusted to the domain of mathematicians and computer scientists. The aim of this review is to identify key points required to start the journey of becoming an effective machine learning practitioner. These key points are further reinforced with an evaluation of how machine learning has been applied so far in a broad scope of real-life microbiology examples. This includes predicting drug targets or vaccine candidates, diagnosing microorganisms causing infectious diseases, classifying drug resistance against antimicrobial medicines, predicting disease outbreaks and exploring microbial interactions. Our hope is to inspire microbiologists and other related researchers to join the emerging machine learning revolution.

This study aimed to evaluate the preliminary psychometric properties of the Hospital Anxiety and Depression Scale (HADS) in a community sample of adults living with a spinal cord injury (SCI). A cross-sectional design was used with 963 people living in the community with an SCI. Participants were recruited via three spinal centres in the United Kingdom. They completed the HADS and the Life Satisfaction Questionnaire. Psychometric analyses were conducted for the whole sample, by gender and level of injury. The HADS demonstrated good internal consistency, with promising content validity. Two factors, approximating to anxiety and depression were extracted via factor analysis for the whole sample, by gender and level of injury. In line with existing literature, females scored significantly higher than males on the anxiety subscale. Item 7 (“I can sit at ease and feel relaxed”) was found to be a complex item, with a potentially different meaning within this population. This study presents preliminary findings, which support the psychometric integrity of the HADS within an outpatient population with SCI. Items that included potential somatic components revealed a more complex factor loading profile. Recommendations are made to further investigate this measure with amendments to such items, incorporating inpatients and independent measures of anxiety and depression to address validity directly.