Precision oncology seeks to leverage molecular information about cancer to improve patient
outcomes. Tissue biopsy samples are widely used to characterize tumours but are limited by …

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated
remarkable progress in image-recognition tasks. Methods ranging from convolutional neural …

Deep learning methods are a class of machine learning techniques capable of identifying
highly complex patterns in large datasets. Here, we provide a perspective and primer on …

Rapid advances in hardware-based technologies during the past decades have opened up
new possibilities for life scientists to gather multimodal data in various application domains …

In the era of precision medicine, cancer therapy can be tailored to an individual patient
based on the genomic profile of a tumour. Despite the ever-increasing abundance of cancer …

In the field of cancer genomics, the broad availability of genetic information offered by next-
generation sequencing technologies and rapid growth in biomedical publication has led to …

Deep neural networks represent, nowadays, the most effective machine learning technology
in biomedical domain. In this domain, the different areas of interest concern the Omics (study …

Rich sources of obesity‐related data arising from sensors, smartphone apps, electronic
medical health records and insurance data can bring new insights for understanding …

Neural networks are powerful tools used widely for building cancer prediction models from
microarray data. We review the most recently proposed models to highlight the roles of …

Cancer is one of the top causes of death globally. Recently, microarray gene expression
data has been used to aid in cancer's effective and early detection. The use of DNA …