Imageomics: Latest News Updates and Information
Author: Disabled World (DW)
Updated/Revised Date: 2024/12/27
Category Topic: Imageomics (Publications Database)
Page Content: Synopsis Introduction Main Subtopics
Synopsis: Information and news regarding Imageomics, a groundbreaking field at the intersection of imaging technology and genomics.
• Imageomics is an emergent scientific field that combines machine learning with biology to extract biological information from images of various life forms.
• As Imageomics continues to evolve, it is poised to revolutionize our understanding of life in ways previously unimaginable.
Introduction
Imageomics is an emergent scientific field that combines machine learning with biology to extract biological information from images of various life forms. It aims to understand the relationship between an organism's phenotype (observable traits) and its genotype (genetic makeup) by incorporating Artificial Intelligence (AI) to generate new scientific hypotheses that are actually testable.
Main Document
Spearheaded by researchers like Tanya Berger-Wolf from The Ohio State University, imageomics utilizes machine learning algorithms to decipher the biological information contained in images. These images, sourced from a variety of means such as camera traps and satellites, can now be analyzed to reveal intricate details about an organism's phenotype and its underlying genotype. Developed barely two years ago, imageomics is already fostering groundbreaking advancements in the scientific community.
Imageomics represents a cutting-edge field at the intersection of imaging technology and omics sciences, such as genomics, proteomics, and metabolomics. It harnesses advanced imaging techniques to visualize, analyze, and interpret complex biological systems at various scales, from molecular to organismal levels. By integrating high-resolution imaging with omics data, Imageomics offers unprecedented insights into the spatial organization, dynamics, and interactions of biomolecules within cells, tissues, and organisms. This interdisciplinary approach holds immense promise for unraveling fundamental biological processes, understanding disease mechanisms, and advancing personalized medicine.
Summary: Imageomics Facts and Information
- Imageomics is an emerging interdisciplinary field bridging imaging technology and genomics.
- It combines advanced imaging techniques with genomic data analysis to visualize biological processes.
- Imageomics enables the examination of cellular structures, dynamics, and interactions at a high resolution.
- The field holds promise for understanding disease mechanisms, including cancer progression and neurological disorders.
- Imageomics facilitates the study of developmental biology, aiding in the investigation of embryonic development and tissue regeneration.
- Applications of Imageomics span various fields, from basic research to clinical diagnostics and personalized medicine.
- Techniques in Imageomics include fluorescence microscopy, live-cell imaging, and high-throughput sequencing.
- Imageomics has the potential to revolutionize drug discovery by providing insights into drug mechanisms and cellular responses.
- Collaboration between biologists, computer scientists, and engineers drives innovation in Imageomics research and technology.
- Continued advancements in Imageomics promise to deepen our understanding of life's complexities and pave the way for new discoveries in biology and medicine.
The Imageomics Institute
The Imageomics Institute is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) Institute program under Award #2118240 (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). It started in Oct 2021.
The inception and research of the Imageomics Institute builds heavily on the "Biology-Guided Neural Networks for Discovering Phenotypic Traits" (BGNN) project, also funded by the US National Science Foundation. BGNN itself built in part on the Phenoscape project (funded by NSF multiple times), which started in 2007 and was incubated at the NSF-funded National Evolutionary Synthesis Center (NESCent).
The vision of the Institute is to establish a new scientific field called imageomics that harnesses revolutions in data science and computing, as well as the rapidly expanding collections of biological image data, in order to accelerate biological understanding of phenotypic traits extracted from images of organisms. As Imageomics continues to evolve, it is poised to revolutionize our understanding of life in ways previously unimaginable.
BioCLIP and the TreeOfLife-10M Dataset
Images of the natural world, collected by a variety of cameras, from drones to individual phones, are increasingly abundant sources of biological information. TreeOfLife-10M is currently the largest and most diverse available dataset of biology images with combined images from iNaturalist, BIOSCAN-1M, and Encyclopedia of Life, to create a dataset of 10M images, spanning 450,000 plus species.
There is an explosion of computational methods and tools, particularly computer vision, for extracting biologically relevant information from images for science and conservation. The Imageomics Institute GitHub organization hosts the development and distribution of a collection of open-source ML tools used to study the biological information encoded in images and videos integrated with structured biological knowledge.
BioCLIP is a new machine learning model released to researchers and designed to learn from the dataset by using both visual cues in the images with various types of text associated with the images, such as taxonomic labels and other information. TreeOfLife-10M is currently the largest and most diverse ML ready dataset of biology images. BioCLIP is a CLIP model trained on new 10M-image dataset of biological organisms with fine-grained taxonomic labels. BioCLIP is a foundation model for the tree of life, leveraging the unique properties of biology captured by TreeOfLife-10M, namely the abundance and variety of images of plants, animals, and fungi, together with the availability of rich structured biological knowledge.