Multilevel approach to male fertility by machine learning highlights a hidden link between haematological and spermatogenetic cells.
Daniele SantiGiorgia SpaggiariAndrea CasonatiLivio CasariniRoberto GrassiBarbara VecchiLaura RoliMaria Cristina De SantisGiovanna OrlandoEnrica GravottaEnrica BaraldiMonica SettiTommaso TrentiManuela SimoniPublished in: Andrology (2020)
This is the first machine learning application to male fertility, detecting potential mathematical algorithms able to describe patients' semen characteristics changes. In this setting, a possible hidden link between testicular and haematopoietic tissues was suggested, according to their similar proliferative properties.
Keyphrases
- machine learning
- end stage renal disease
- artificial intelligence
- induced apoptosis
- chronic kidney disease
- ejection fraction
- newly diagnosed
- big data
- deep learning
- gene expression
- peritoneal dialysis
- childhood cancer
- oxidative stress
- climate change
- endoplasmic reticulum stress
- risk assessment
- cell proliferation
- signaling pathway
- human health