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Bioinformatics is an area that has changed how experiments and research in biological sciences are designed and developed. Biotechnology is an area that has not been developed beyond the scope of bioinformatics, impacted areas such as drug discovery and development, crop improvement, bioremediation, studies of environmental diversity, molecular pathology, and other areas within biotechnology directly. Due to the development of the high throughput sequencing technologies or Next-generation sequencing (NGS), a large amount of data is being generated, which needs to be processed and analyzed to generate new knowledge and discoveries. To analyze the amount of data, the fields of machine learning and deep learning from computer science were integrated into bioinformatics. Machine learning applies techniques that allow computers to learn, while deep learning generates models of artificial neural networks that try to mimic the functioning of the human brain allowing them to learn from the data and improve their learning through experiences. These areas are essential to identify, analyze, interpret, and obtain knowledge of a large amount of biological data (Big biological data). In this study, we present a review of these two areas: machine learning and deep learning, impact-oriented, and its applications in the area of biotechnology.

Aprendizaje de máquina y aprendizaje profundo en biotecnología: aplicaciones, impactos y desafíos