The Future of AI-based Gene Sequencing

An analysis of the latest Alphafold breakthrough  – Enformer, a neural network architecture that accurately predicts gene expression from DNA sequences – and the What’s Next? of the future of AI and Genomics.

Sections of this post include:

• DeepMind’s Development of Enformer and the Implications of AI-based Gene Sequencing
• AI-Based Gene Sequencing
• Implications of AI-Based Gene Sequencing
• History of AI in Genomic Medicine
• Latest Findings and Trends
• What Next?
  • Ethical Issues Regarding CRISPR Mediated Genome Editing
  • The Future of Genomics and Artificial Intelligence

DeepMind’s development of Enformer and the implications of AI-based gene sequencing

According to Marktechpost:  DeepMind, in collaboration with their Alphabet colleagues at Calico, [introduced] Enformer, a neural network architecture that accurately predicts gene expression from DNA sequences.

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Earlier studies on gene expression used convolutional neural networks as key building blocks. However, their accuracy and usefulness have been hampered by problems in modeling the influence of distal enhancers on gene expression. The proposed new method is based on Basenji2, a program that can predict regulatory activity from DNA sequences of up to 40,000 base pairs.

The team expressed a need for a fundamental architectural modification to capture extended sequences and understand whether the regulatory DNA elements influence expression at greater distances.

The new model is based on Transformers to leverage self-attention processes to absorb considerably more DNA background. The Transformers are built to “read” substantially expanded DNA sequences because they are suitable for looking at long text sections. The model architecture can describe the influence of critical regulatory regions called enhancers on gene expression from further away within the DNA sequence. It does this by successfully processing sequences to consider interactions at distances greater than five times (i.e., 200,000 base pairs) than earlier methods.

Although it is now feasible to analyze an organism’s DNA, understanding the genome requires complex studies. Despite extensive research, the vast majority of DNA regulation of gene expression remains a mystery.  Enformer recognizes the vocabulary of the DNA sequence in part, similar to a spell checker, and may thus indicate modifications that potentially change gene expression.

The primary purpose of this new approach is to forecast which changes to the DNA letters, commonly known as genetic variations, would affect the gene’s expression. Enformer outperforms earlier models in predicting the impact of genetic variants on gene expression, both in natural genetic variants and synthetic variants that change critical regulatory regions. This characteristic helps decipher the expanding number of disease-associated variations discovered in genome-wide association studies.

Enformer is a significant step forward in complex genomic sequence studies. The team intends to collaborate with other researchers and organizations interested in using computational models to answer the big problems in genomics.

AI-Based Gene Sequencing

AI-based gene sequencing involves the application of artificial intelligence and machine learning techniques to analyze and interpret genetic data obtained through DNA sequencing. Gene sequencing technologies have advanced rapidly, allowing scientists to decode the DNA of organisms more quickly and at a lower cost. AI is being used to process and interpret the vast amounts of data generated by these sequencing technologies.

Implications of AI-Based Gene Sequencing

1. Personalized Medicine: AI can help identify genetic variations associated with diseases, allowing for personalized medical treatments tailored to an individual’s genetic makeup. This can lead to more effective and targeted therapies.
2. Genetic Disease Identification: AI can aid in identifying genetic mutations that cause rare or complex genetic disorders. This can assist in early diagnosis and management of these conditions.
3. Drug Discovery and Development: AI can analyze genetic data to identify potential drug targets and predict the effectiveness of drugs based on an individual’s genetic profile. This can accelerate drug discovery processes.
4. Cancer Research: AI-based gene sequencing can provide insights into the genetic mutations driving cancer growth. This information can guide the development of targeted therapies.
5. Agricultural Improvement: AI can be used in plant and animal breeding to select for desirable genetic traits, improving crop yields and livestock health.
6. Ethical Considerations: The availability of extensive genetic data raises concerns about privacy, consent, and potential misuse of genetic information. Safeguarding this data and ensuring responsible use are important ethical considerations.
7. Data Security: Genetic data is sensitive and valuable. Ensuring secure storage and transmission of this data is crucial to protect individuals’ privacy and prevent data breaches.
8. Data Bias: AI algorithms can inherit biases present in training data, potentially leading to unequal representation and inaccurate predictions for certain populations. Efforts are needed to address bias and ensure equitable use of AI in gene sequencing.
9. Regulatory Challenges: The rapid pace of AI and genetic research can outpace regulatory frameworks. Establishing appropriate regulations to ensure the accuracy and reliability of AI-based genetic analyses is essential.
10. Interdisciplinary Collaboration: Effective utilization of AI in gene sequencing requires collaboration between biologists, geneticists, computer scientists, and ethicists to navigate complex challenges.

History of AI in Genomic Medicine

The integration of artificial intelligence (AI) into genomic medicine has evolved over the past few decades. Here’s a brief history:

– 1990s-2000s: The Human Genome Project marked the beginning of large-scale genome sequencing efforts. Initial AI applications focused on bioinformatics, which involved developing algorithms for sequence analysis and identifying genes.

– 2010s: Advances in sequencing technologies led to a surge in genomic data. Machine learning techniques gained prominence for predicting the functional impact of genetic variations, identifying disease-related genes, and assisting in personalized medicine efforts.

– 2020s: The use of deep learning and neural networks became more widespread in genomic medicine. AI-driven approaches extended to drug discovery, clinical trial optimization, and the development of predictive models for disease risk.

Latest Findings and Trends

Here are some of the latest findings and trends in AI for genomic medicine:

1. Enhanced Genomic Variant Interpretation: AI models are improving the interpretation of genetic variants by considering functional genomics data, evolutionary conservation, and protein structure predictions. This aids in distinguishing pathogenic mutations from benign ones.
2. Polygenic Risk Scores (PRS): AI is being used to calculate PRS, which aggregate the effects of multiple genetic variants to predict disease risk. PRS help in assessing an individual’s susceptibility to complex diseases like heart disease, diabetes, and cancer.
3. Drug Discovery and Repurposing: AI algorithms analyze genomic data to identify potential drug targets and repurpose existing drugs for new indications. Deep learning models are used to predict the binding affinity of drugs to target proteins.
4. Single-Cell Genomics: AI techniques are applied to analyze single-cell RNA sequencing data, allowing the study of cellular heterogeneity and identifying rare cell types. This has implications for understanding diseases at a cellular level.
5. Imaging and Omics Integration: AI is being used to integrate various types of data, including genomic, imaging, and clinical data, to develop comprehensive disease models. This aids in understanding disease progression and heterogeneity.
6. Radiogenomics: AI combines radiological images with genomic data to identify correlations between imaging features and genetic variations. This has applications in predicting treatment response and patient outcomes.
7. Rare Disease Diagnosis: AI helps in diagnosing rare genetic diseases by analyzing patient data and comparing it with reference databases. This accelerates the identification of causative variants.
8. Cancer Genomics: AI models predict cancer subtypes, treatment responses, and tumor evolution patterns based on genomic data. This informs personalized treatment strategies.
9. AI-Assisted Clinical Trials: Machine learning optimizes clinical trial design, patient recruitment, and data analysis. It identifies patient subgroups that are more likely to benefit from experimental treatments.
10. Ethical Considerations: AI in genomic medicine raises ethical issues related to data privacy, consent, and potential biases in algorithms. Ensuring responsible AI deployment is a crucial trend.
11. International Collaborations: AI and genomics research are increasingly collaborative efforts involving researchers, clinicians, and data scientists from around the world. Data sharing and cross-disciplinary cooperation drive advancements.
12. Regulatory Challenges: The use of AI in genomics poses challenges for regulatory bodies in terms of ensuring the accuracy and clinical validity of AI-driven diagnostic tools and therapies.

What Next?

Ethical Issues Regarding CRISPR Mediated Genome Editing

CRISPR-Cas9 is a powerful genome editing technology that allows scientists to modify DNA with a high degree of precision. While it holds immense promise for various applications, including medical research, agriculture, and biotechnology, there are several ethical issues associated with its use. Some of the key ethical concerns include:

1. Off-Target Effects: CRISPR-Cas9 editing is not always perfectly accurate and can inadvertently introduce changes in unintended locations within the genome, known as off-target effects. This could lead to unintended consequences, such as causing new diseases or disrupting essential genes. Ensuring the accuracy and specificity of CRISPR edits is crucial to avoid these potential risks.
2. Germline Editing: One of the most debated ethical issues is the editing of human germline cells (sperm, eggs, embryos) to make heritable genetic changes. Altering germline cells could affect not only the individual being edited but also future generations. There are concerns about unforeseen genetic and health implications, as well as the potential for designer babies and the creation of genetic “haves” and “have-nots.”
3. Informed Consent: When using CRISPR for medical purposes, obtaining informed consent from individuals or patients is crucial. However, explaining the potential long-term consequences and uncertainties associated with genome editing can be challenging, especially when dealing with complex scientific information.
4. Unequal Access: As with many new technologies, there is a risk of unequal access to CRISPR-based therapies or enhancements. Ensuring that these technologies are available to all socio-economic groups is an ethical imperative to prevent exacerbating existing inequalities.
5. Environmental Impact: In agriculture, CRISPR can be used to genetically modify crops and livestock for improved yield, disease resistance, and other traits. However, the environmental impact of releasing genetically modified organisms into ecosystems raises concerns about unintended consequences and potential disruption of ecosystems.
6. Dual-Use Concerns: The same technologies that have positive applications can also be misused for harmful purposes. CRISPR has the potential for creating engineered organisms with harmful traits, posing risks such as bioterrorism and the accidental release of organisms with unintended ecological impacts.
7. Long-Term Effects: Given the relative novelty of CRISPR technology, the long-term effects of genome editing on both individuals and ecosystems are not fully understood. There is a need for ongoing monitoring and research to ensure that any unintended consequences are identified and addressed.
8. Regulation and Oversight: Establishing appropriate regulatory frameworks and oversight mechanisms for CRISPR research and applications is essential. Balancing innovation and safety requires careful consideration of ethical, legal, and societal concerns.
9. Cultural and Religious Perspectives: Different cultural, religious, and philosophical beliefs may shape opinions on genome editing. Some may view editing the human germline as interfering with nature or as morally objectionable, while others may see it as a way to alleviate suffering.
10. Transparency and Openness: Ethical concerns arise when there is a lack of transparency in the research process or when important information, such as negative results or unsuccessful experiments, is not shared. Openness in research is crucial for building trust and ensuring responsible development.

Addressing these ethical issues requires collaboration between scientists, policymakers, ethicists, and the public. Ethical considerations should guide the development, implementation, and regulation of CRISPR technology to maximize its benefits while minimizing potential risks and negative consequences.

The Future of Genomics and Artificial Intelligence

The future of genomics and artificial intelligence (AI) is expected to be transformative, with the convergence of these two fields offering unprecedented opportunities for advancements in healthcare, research, biotechnology, and beyond. Here are some key aspects of their future:

1. Personalized Medicine: The combination of genomics and AI will enable more precise and personalized medical treatments. AI can analyze an individual’s genetic data to predict disease risks, drug responses, and treatment outcomes, allowing for tailored healthcare plans.
2. Disease Prediction and Prevention: AI can identify patterns in genomics data that may indicate disease risks. Early detection and intervention could become possible, leading to more effective prevention strategies.
3. Drug Discovery and Development: AI can accelerate drug discovery by analyzing vast genomic datasets to identify potential drug candidates and predict their effects. This could lead to faster development of novel therapies and more efficient repurposing of existing drugs.
4. Genomic Interpretation: AI will continue to aid in interpreting the vast amount of genomic data, identifying relevant genetic variants, and distinguishing between benign and disease-associated mutations.
5. Rare Disease Diagnosis: AI can assist in diagnosing rare genetic diseases by analyzing patient data and identifying similar cases from databases, helping doctors make more accurate diagnoses.
6. Data Integration: AI can integrate genomic data with other omics data (such as proteomics and metabolomics) and clinical information, providing a comprehensive view of an individual’s health and disease progression.
7. Cancer Genomics: AI can analyze the genomic profiles of tumors to predict cancer subtypes, treatment responses, and evolution patterns. This guides personalized cancer treatment plans.
8. Epigenetics Analysis: AI can help analyze epigenetic modifications and their impact on gene expression, shedding light on the role of epigenetics in health and disease.
9. Ethical and Privacy Considerations: As genomics and AI advance, there will be increased focus on addressing ethical issues related to data privacy, consent, and the responsible use of sensitive genetic information.
10. Data Standardization and Sharing: The field will work towards standardizing genomic data formats and sharing them across institutions, fostering collaboration and advancing research.
11. AI-Enhanced Sequencing: AI can improve the efficiency and accuracy of DNA sequencing technologies, making sequencing more accessible and affordable.
12. Education and Training: As genomics becomes a central part of medical practice, training healthcare professionals to understand and effectively use genomics and AI tools will be crucial.
13. AI-Driven Discoveries: AI can uncover unexpected correlations and insights within large genomic datasets that human researchers might miss, leading to new scientific discoveries.
14. Global Health Impact: AI-powered genomics can have a substantial impact on global health initiatives, especially in resource-limited settings where accurate and rapid diagnostics are essential.
15. Regulatory Challenges: As AI-driven genomics applications become more widespread, regulatory agencies will need to develop frameworks for evaluating and approving these technologies while ensuring patient safety and data integrity.

Additional OODA Loop Resources

Before OpenAI’s ChatGPT, There was Google DeepMind’s Alphafold:  While the release of ChatGPT and enterprise-level deployment of Large Language Models have been impactful, all organizations should also be digging deeper into recent AI and deep learning history and ask the question:  “How is Deepmind’s Alphafold representative of the future architectures, ecosystems, platforms and value creation opportunities in our industry sector and subsectors?” 

The First FDA Approved CRISPR-based Medicine:  The next ten years will be marked by all the uncertainties and unintended consequences that underpin so many doom and gloom scenarios. It is time to start tracking the abundance and breakthroughs that will also come fast and furious in the next decade – equally as overwhelming, while also breathtaking, positive, highly technical and scientific – and transformative. Here are a couple of those recent “firsts.”

Recent Developments in and the Future of the Bioeconomy in 2024:   We have tracked vital recent developments in health security, bioengineering, synthetic biology, biotechnology, and medical technology – which are compiled here. The future of the U.S. Bioeconomy is crucial to the future of strategic competitive advantage globally – all of which was discussed in a future-forward fashion at OODAcon 2023.

Overall, the bioeconomy and medical technology platforms also show clear signs in 2024 as the innovation space best positioned to deploy best-in-class enterprise platforms and use cases of generative AI, artificial intelligence, and machine learning (which other emerging technology innovators, cybersecurity professionals, and industry sectors should have an instinct to track closely and to emulate).

The New Tech Trinity: Artificial Intelligence, BioTech, Quantum Tech: Will make monumental shifts in the world. This new Tech Trinity will redefine our economy, both threaten and fortify our national security, and revolutionize our intelligence community. None of us are ready for this. This convergence requires a deepened commitment to foresight and preparation and planning on a level that is not occurring anywhere. The New Tech Trinity.

The Revolution in Biology: This post provides an overview of key thrusts of the transformation underway in biology and offers seven topics business leaders should consider when updating business strategy to optimize opportunity because of these changes. For more see:  The Executive’s Guide To The Revolution in Biology

Quantum Computing and Quantum Sensemaking: Quantum Computing, Quantum Security and Quantum Sensing insights to drive your decision-making process. Quantum Computing and Quantum Security

Materials Science Revolution: Room-temperature ambient pressure superconductors represent a significant innovation. Sustainability gets a boost with reprocessable materials. Energy storage sees innovations in solid-state batteries and advanced supercapacitors. Smart textiles pave the way for health-monitoring and self-healing fabrics. 3D printing materials promise disruptions in various sectors. Perovskites offer versatile applications, from solar power to quantum computing. See: Materials Science