Imageomics Set to Revolutionize Understanding of Life

Introduction

Imageomics, a new field of science, has made stunning progress in the past year and is on the verge of major discoveries about life on Earth, according to one of the founders of the discipline.

Main Digest

Imageomics: Images as the Source of Information About Life

Tanya Berger-Wolf, faculty director of the Translational Data Analytics Institute at The Ohio State University, outlined the state of imageomics in a presentation on Feb. 17, 2024, at the annual meeting of the American Association for the Advancement of Science.

"Imageomics is coming of age and is ready for its first major discoveries," Berger-Wolf said in an interview before the meeting.

Imageomics is a new interdisciplinary scientific field focused on using machine learning tools to understand the biology of organisms, particularly biological traits, from images.

Those images can come from camera traps, satellites, drones - even the vacation photos that tourists take of animals like zebras and whales, said Berger-Wolf, who is director of the Imageomics Institute at Ohio State, funded by the National Science Foundation.

These images contain a wealth of information that scientists couldn't properly analyze and use before the development of artificial intelligence and machine learning.

The field is new - the Imageomics Institute was just founded in 2021 - but big things are happening, Berger-Wolf told AAAS.

One major area of study that is coming to fruition involves how phenotypes - the observable traits of animals that can be seen in images - are related to their genome, the DNA sequence that produces these traits.

"We are on the cusp of understanding the direct connections of observable phenotype to genotype," she said.

"We couldn't do this without imageomics. It is pushing forward both artificial intelligence and biological science."

Berger-Wolf cited new research on butterflies as one example of the advances that imageomics is making. She and colleagues are studying mimics - butterfly species whose appearance is similar to a different species. One reason for mimicry is to look like a species that predators, such as birds, avoid because their taste is not appealing.

In these cases, birds - as well as humans - can't tell the species apart by looking at them, even though the butterflies themselves know the difference. However, machine learning can analyze images and learn the very subtle differences in color or other traits that differentiate the types of butterflies.

"We can't tell them apart because these butterflies didn't evolve these traits for our benefit. They evolved to signal to their own species and to their predators," she said.

"The signal is there - we just can't see it. Machine learning can allow us to learn what those differences are."

But more than that, we can use the imageomics approach to change the images of the butterflies to see how extensive the mimics' differences must be to fool birds. Researchers are planning to print realistic images of the butterflies with subtle differences to see which ones real birds respond to.

This is doing something new with AI that hasn't been done before.

"We're not using AI to just recapitulate what we know. We are using AI to generate new scientific hypotheses that are actually testable. It is exciting," Berger-Wolf said.

Researchers are going even further with the imageomics approach to connect these subtle differences in how the butterflies look to the actual genes that lead to those differences.

"There's a lot we are going to be learning in the next few years that will push imageomics forward into new areas that we can only imagine now," she said.

One key goal is to use this new knowledge generated by imageomics to find ways to protect threatened species and the habitats where they live.

"There's a lot of good that will come from imageomics in the coming years," Berger-Wolf said.

Presentations

Berger-Wolf's AAAS presentation, titled "Imageomics: Images as the Source of Information About Life" is part of the session "Imageomics: Powering Machine Learning for Understanding Biological Traits."

Imageomics: Images as the Source of Information About Life

Images are the most abundant source for documenting life. Yet the traits of organisms, critical for understanding fundamental biology and evolution, cannot be readily extracted from them. Knowledge-guided machine learning and computer vision can turn massive collections of images into a high-resolution information database about living organism, enabling scientific discovery, conservation, and policy decisions.

Imageomics: Powering Machine Learning for Understanding Biological Traits

The analysis of integrated products of genes and environment (traits) is critical for predicting effects of environmental change or genetic manipulation and understand the process of evolution. Biologists have characterized traits from observation and measurements of organisms. Today, images are the most abundant and readily available source of information about the world for documenting biodiversity and for extracting organismal traits.

Imageomics is an approach that removes a key bottleneck to progress in understanding how genes and the environment affect organismal phenotypes while also addressing key challenges in artificial intelligence (AI) - explainability, inductive bias, novelty, and open world recognition - by enabling computable traits from images. Deep structured knowledge of species traits and evolution, in the form of ontologies, phylogenies, and other knowledge bases, uses massive biological image stores to ground machine learning (ML) to generate biologically meaningful explanations.

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This peer reviewed publication was selected for publishing by the editors of Disabled World due to its significant relevance to the disability community. Originally authored by Ohio State University, and published on 2024/02/17 (Edit Update: 2024/02/18), the content may have been edited for style, clarity, or brevity. For further details or clarifications, Ohio State University can be contacted at osu.edu. NOTE: Disabled World does not provide any warranties or endorsements related to this article.

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