Why Applying Machine Learning to Biology is Hard – But Worth It

Why Applying Machine Learning to Biology is Hard – But Worth It

In the field of biology, applying machine learning techniques can be a challenging task. The complexity of biological data and the need for specialized knowledge make it difficult to seamlessly integrate machine learning into the study of biology. However, despite these challenges, the marriage of these two fields is worth pursuing as it opens up new possibilities for research and discovery.

One of the main challenges in applying machine learning to biology is the sheer volume of data. For each individual, there are billions and billions of data points to consider. This presents the classical big-p little-n problem, where the number of features (p) is much larger than the number of samples (n). To overcome this challenge, it is necessary to carefully optimize the machine learning process at every step, from study design to data analysis.

Another challenge is the need to adapt existing machine learning methods to biomolecular data. While there are existing tools and methods for statistical learning and deep learning, they may not be directly applicable to the unique characteristics of biological data. However, by featurizing the deep information present in biomolecular data, it is possible to leverage existing tools and techniques for analysis.

Furthermore, it is important to control for confounders and ensure consistency when working with data from multiple sites. When dealing with billions of data points per person, the risk of overfitting becomes significant. Training the models in a consistent manner and accounting for confounding variables are crucial steps in ensuring the reliability of the results.

To successfully bridge the gap between technology and biology, it is crucial to hire three types of people. The first type is the technologist, someone with a deep understanding of machine learning and data analysis. The second type is the biologist, someone who can bring domain-specific knowledge and expertise to the table. Lastly, the bridgers, individuals who have fluently worked in both technology and biology, can effectively communicate and bridge the gap between the two fields.

The collaboration between these three groups of people is essential for the success of a techbio company. Each group brings a unique perspective and skill set, contributing to a more holistic approach to problem-solving. By combining the imagination and intuition of biologists with the technical expertise of technologists, it becomes possible to tackle complex problems in biology using machine learning techniques.

The field of multiomics has emerged as a powerful approach to studying biology. By integrating data from multiple 'omics' technologies, such as genomics, transcriptomics, proteomics, and metabolomics, researchers can gain a comprehensive understanding of biological processes. This concerted approach allows for a deeper exploration of life and opens up new avenues for research and discovery.

In conclusion, while applying machine learning to biology comes with its challenges, the potential rewards make it a worthwhile endeavor. By optimizing the machine learning process, adapting existing methods to biomolecular data, and fostering collaboration between technologists and biologists, it becomes possible to unlock new insights and advancements in the field of biology.

Actionable advice:

1. Prioritize study design and data collection: Carefully plan and design studies to ensure the collection of high-quality data that is suitable for machine learning analysis. Consider the balance between the number of features and the number of samples to avoid the big-p little-n problem.

2. Featurize biomolecular data: Adapt existing machine learning methods by featurizing the deep information present in biomolecular data. This allows for the utilization of existing tools and techniques, such as statistical learning and deep learning.

3. Foster collaboration and interdisciplinary teamwork: Build a team that consists of technologists, biologists, and bridgers who can effectively communicate and collaborate. Encourage the exchange of knowledge and ideas to foster innovation and growth.

By following these actionable advice, researchers and companies can overcome the challenges of applying machine learning to biology and unlock the potential for groundbreaking discoveries and advancements in the field.