Of the seventeen SDGs, the Produvia team identified five goals that can be solved with genomics and artificial intelligence technologies.

AI Goal #1: No Poverty

Goal #1: No Poverty

Can we really end poverty? Can we grow the middle class? These are really hard questions to answer. Satellite imagery was combined with machine learning to predict poverty [7]. Poverty has been linked to disease, chronic illness, childhood obesity, elevated blood lead levels, academic achievements, and DNA methylation [8-12]. How can machine learning help with these genomic causations or correlations? If we can predict disease or DNA methylation across genes, we can take preventative action in the fight against poverty.

AI Goal #2: Zero Hunger

Goal #2: Zero Hunger

How can humanity end hunger? Can we achieve a stable food supply? Can we end hidden hunger, also known as micronutrient deficiency? Certain hormones that regulate hunger and satiety [13]. Hunger can be detected in crying infants using deep learning [14]. Analyzing how people eat or their consumption patterns can reveal hidden hunger or gaps in micronutrient deficiency. Can people improve nutrition and promote sustainable agriculture? To answer these questions, consider that plant breeding and other agricultural technologies are greatly improved using machine learning. Increasing crop yield production will close the gap between crop output and hunger. Genetically improving cultivars and improving agronomic practices is one way to increase crop productivity [15]. If we make agricultural more productive, we can reduce world hunger.

AI Goal #3: Good Health and Well-Being

Goal #3: Good Health and Well-Being

Can we live a healthier life? Can we promote the well-being of all humanity? Better detection of AIDS, tuberculosis, malaria and neglected tropical diseases are now possible thanks to deep learning. Imagine being able to create personalized genomic profiles of each person on earth. This will allow us to predict the outbreak of diseases knowing where the susceptibility lies. Humanity has the potential to edit human reproduction. With gene editing, we can create the next generation of humans, which are immune to the latest diseases and typical health conditions. Combing gene editing with machine learning will allow humanity to achieve customized genetic and genomic profiles of individuals. If we can better understand how the aging process affects health and longevity, we can create healthier societies. Today, we can use deep learning to detect changes in biomarkers (i.e., physiological variables, composite indices) using data from longitudinal studies.

AI Goal #4: Life Below Water

Goal #4: Life Below Water

Can we conserve ocean life? Can humanity use the oceans, seas and marine resources sustainably? Genomics and machine learning can solve many problems to ensure the continuation of life below water. For example, we can classify ocean acidity to reduce declining fish stock. We can apply conservation genomics with deep learning technologies, to predict the biodiversity of living organisms. Can we improve our aquaculture? Over the past few decades, advancements in agricultural biotechnology have changed the way research is analyzed. Today, genomic data using is analyzed using a variety of computational tools including machine learning or deep learning.

AI Goal #5: Life on Land

Goal #15: Life on Land

Can we protect our ecosystem? Can we restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification? Lastly, can humanity halt and reverse land degradation and halt biodiversity loss? Understanding complex ecosystems and how genes are affected by the environment is possible thanks to machine learning technologies. Deep learning can meet genome-scale metabolic modeling [16]. Machine learning technologies have demonstrated the ability to analyze large, complex biological data. Furthermore, the massive and rapid advancements in both biological data generation and machine learning methodologies are promising for further understanding of genomics and biological data. It’s now possible to classify microbial roles in ecosystems using deep learning [17]. Genomic tools, such as population genomics, meta-omics, and genome editing, can also restore ecosystems and biodiversity. Meta-omics can improve the assessment and monitoring of restoration outcomes. Gene editing can generate novel genotypes for restoring challenging environments. Using machine learning to analyze population genomics, meta-omics, and genome editing data will aid companies in developing solutions to improve life on earth.

AI Research in Genomics

Artificial intelligence research is driving technological breakthroughs all industry verticals, genomics included. Reading academic papers takes time and the technical language is not easy to understand. At Produvia, on the other hand, we keep up-to-date with the latest academic research papers so you don’t have to. Below, we highlight 20 AI and machine learning use cases for genomics [18-26]:

Genomics

Genomics is an interdisciplinary field of biology focusing on the structure, function, evolution, mapping, and editing of genomes. Here are five AI and machine learning applications for genomics:

Extract genomic and epigenomic variants of clinical utility Identify genes Predict genomic associations Predict protein functions Predict sequence the specificity of DNA and RNA-binding proteins

Regulatory Genomics

Regulatory genomics is the study of genomic regions or features and how they regulate genes. At Produvia, we list five AI and machine learning applications for regulatory genomics:

Classify gene expression Predict gene expression from genotype Predict promoters and enhancers Predict splicing Predict transcription factors and RNA-binding proteins

Functional Genomics

The field of molecular biology that attempts to describe gene functions and interactions is functional genomics. Here are five AI applications for functional genomics:

Classify mutations and functional activities Classify subcellular localization Predict promoters and enhancers Predict splicing Predict transcription factors and RNA-binding proteins

Structural Genomics

Structural genomics is the field of genomics that involves the characterization of genome structures. At Produvia, we list five AI and machine learning applications for structural genomics:

Classify protein tertiary structures Classify structures of proteins Predict contact maps Predict physical properties Predict protein secondary structures

AI Ideas for Genomics

You’re interested in artificial intelligence and machine learning, but don’t know where to start. At Produvia, we brainstormed several ideas for the application of artificial intelligence technologies in genomics. Here are thirty-five AI ideas for genomics:

Annotate genes based on structure and chromosomes Classify cancer from gene expression profiles Classify genes Classify genomic profiles Classify mutation types Design targetted therapies Detect deoxyribonucleic acid regions that are predictive of gene expression Determine relationships between genotypes and phenotype Discover drugs for genomic medicine Distinguish between cancer and adenoma Estimate prevalence for chromatin marks Extract transcriptome patterns Identify biomarkers for a disease Identify enhancers Identify pairwise variable associations between genomic data types Identify positioned nucleosomes Identify potentially valuable disease biomarkers Identify promoters Identify subtype of breast cancer tumor Identify transcription factor binding sites Identify transcription start sites, splice sites, exons Interpret regulatory control in single cells Model regulatory elements Partition and label the genome with chromatin state annotation Predict chromatin marks from deoxyribonucleic acid sequences Predict disease phenotype or prognosis Predict gene function Predict genetic interactions Predict protein backbones from protein sequences Predict regulatory functions and relationships Predict sequence the specificity of enhancer and cis-regulatory regions Predict the specificities of deoxyribonucleic acid-binding and ribonucleic acid-binding proteins Predict the splicing activity of individual exons Predict variant deleteriousness Quantify effects of single nucleotide variants on chromatin accessibility

Challenges and Opportunities in Genomics

The use of artificial intelligence technologies to solve genomics problems poses many challenges. These industry challenges also present opportunities for AI technology providers, such as Produvia, to solve market problems and create AI solutions. Below, we list three genomics opportunities:

Generating ground-truth labels or genomics datasets can be expensive “Right to an explanation” laws must be addressed Longitudinal studies are required

How can AI companies overcome these challenges? At Produvia, we believe that industry collaboration will overcome Challenge #1, algorithmic transparency will overcome Challenge #2, and long-term research projects will overcome Challenge #3.