# Bioinformatics and Medicine in the Era of Deep Learning

## TL;DR

This paper reviews how deep learning is transforming bioinformatics and medicine by enabling large-scale data analysis and knowledge discovery, highlighting recent advances and applications in the field.

## Contribution

It provides a comprehensive overview of deep learning applications in bioinformatics and biomedicine, emphasizing recent developments and future directions.

## Key findings

- Deep learning enhances data analysis in bioinformatics.
- Deep learning enables new insights in biomedicine.
- The field is rapidly evolving with technological advances.

## Abstract

Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by technological breakthroughs in data acquisition technologies. It has been argued that bioinformatics could quickly become the field of research generating the largest data repositories, beating other data-intensive areas such as high-energy physics or astroinformatics. Over the last decade, deep learning has become a disruptive advance in machine learning, giving new live to the long-standing connectionist paradigm in artificial intelligence. Deep learning methods are ideally suited to large-scale data and, therefore, they should be ideally suited to knowledge discovery in bioinformatics and biomedicine at large. In this brief paper, we review key aspects of the application of deep learning in bioinformatics and medicine, drawing from the themes covered by the contributions to an ESANN 2018 special session devoted to this topic.