Artificial Intelligence and Its Application in Biological Sciences

Artificial Intelligence (AI) is significantly advancing the biological sciences by improving how data is acquired, analyzed, and interpreted. It is modernizing traditional methods of collecting and processing biological data.

The global market for AI in drug discovery is expected to grow from around $1.5 billion to nearly $13 billion by 2032. AI-driven solutions in clinical research are projected to surpass $7 billion by the decade’s end. AI is enabling more accurate detection, advanced prediction models, and enhanced decision-making.

Now, vast datasets can be processed at remarkable speed, and AI can uncover patterns and insights that were once out of reach. The application of AI in biology spans genomics, drug discovery, personalized medicine, and more, reshaping research across multiple biological fields.

In this article, you’ll learn more about AI contributions in biology, its applications, challenges, future opportunities, and more.

AI in Genomics and Protein Sciences

Source: NIH – Neural Network AlphaFold 2’s architecture

Artificial Intelligence (AI) is making significant strides in genomic and protein sciences, offering powerful tools to tackle complex biological problems. In clinical genomics, AI applications are addressing tasks that are often too intricate for traditional methods or human intelligence alone. AI is transforming bioinformatics by enabling rapid analysis and interpretation of large-scale genomic data.

Let’s explore some of the advanced AI tools contributing to genome and protein science.

1. Variant Calling

AI has transformed the clinical interpretation of genomes, where identifying individual genetic variants is crucial. Standard tools are prone to errors influenced by factors like sequencing technology and sample preparation.

AI algorithms, particularly deep learning models like DeepVariant, learn from reference genomes to offer superior accuracy, improving variant identification. This is particularly impactful in emerging sequencing technologies, such as nanopore sequencing, which have traditionally struggled with base calling.

2. Genome Annotation and Variant Classification

After variant calling, AI excels in annotating genomes and classifying variants based on their potential impact. Techniques like PrimateAI, which uses deep learning on DNA and protein sequence data, show improved accuracy in classifying genetic variants.

These tools reduce the reliance on manual interpretation by learning directly from sequence data and biological features. These methods learn directly from data, such as protein domains and conserved sequences, making them powerful for identifying pathogenic mutations that traditional methods often overlook.

3. Phenotype-to-Genotype Mapping

One of the most promising applications of AI in genomics is its ability to link phenotypic data to genetic variants. AI algorithms are increasingly capable of extracting diagnostic concepts from diverse medical records, images, and genetic data, enabling a more accurate mapping between physical traits and genetic causes.

For example, AI systems like DeepGestalt, which analyze facial images, have outperformed human clinicians in diagnosing genetic syndromes. This capability extends to more complex clinical scenarios, such as using tumor histology to predict genomic aberrations, bridging gaps where genomic data alone may be insufficient.

4. Genotype-to-Phenotype Prediction

The ultimate goal of genomic research is to predict and diagnose diseases based on genetic information. While traditional statistical methods have been used for polygenic risk prediction, AI can integrate diverse data sources, including genetic, environmental, and behavioral factors, to refine disease risk models.

By combining genetic data with clinical records and digital health information, AI is poised to enhance disease prediction models, enabling earlier intervention and more personalized healthcare.

5. AlphaFold 3: A Revolution in Protein Structure Prediction

In the realm of protein science, AlphaFold 3, developed by DeepMind, has emerged as a groundbreaking AI-driven solution for predicting protein structures. This model has transformed our understanding of how proteins fold, providing unprecedented accuracy in determining their three-dimensional shapes from genetic sequences.

With AlphaFold 3, predicting protein interactions and functions has become more reliable, enhancing drug discovery and our understanding of genetic diseases. This leap in machine learning in biology has far-reaching implications, offering new pathways to unravel complex biological systems.

6. PEDIA: Enhancing Genetic Diagnosis with AI

PEDIA is an AI-driven platform that uses facial recognition technology to aid in interpreting clinical genomic data. This innovative tool integrates phenotypic features from portraits with genomic information, increasing diagnostic accuracy for various monogenic disorders.

By analyzing facial images and genomic data simultaneously, PEDIA enhances diagnostic yield and streamlines the identification of rare genetic disorders, making it a powerful asset in the field of clinical genomics.

These advanced AI tools, DeepGestalt, PEDIA, and AlphaFold 3, are shaping the future of clinical genomics and contributing to the broader landscape of biological research. But beyond just genomics, there are several medical applications of AI. Emerging tools like G2PDiffusion are also enhancing phenotype-genotype mapping through generative modeling.

Medical Applications of AI

Source: ChatGPT

Artificial Intelligence in the medical field is enhancing diagnosis, treatment planning, and patient management.e. Over the past twelve years, AI has shown immense potential in improving healthcare efficiency and outcomes. By leveraging machine learning in biology, AI simulates human intelligence, allowing machines to analyze medical records, predict diseases, and make decisions with high accuracy.

Key applications of AI in healthcare include:

1. Treatment Optimization: AI customizes treatment plans based on patient data, improving outcomes for complex surgeries and procedures. The integration of AI-driven bioinformatics allows for more precise recommendations.

For example, AI in treatment optimization is seen in the use of Watson for Oncology (WFO), developed by IBM and formerly used in Indian cancer centers like Manipal, was one of the earliest AI-powered clinical decision support tools. It examined the concordance between the treatment recommendations made by WFO and those from a multidisciplinary tumor board for breast cancer patients.

2. Predictive Healthcare: AI predicts future health risks, including the likelihood of diseases such as cancer or heart conditions, by analyzing patient data and medical history. For example, researchers at Mount Sinai Health System developed machine learning models to predict critical events and mortality in COVID-19 patients.

Published in the Journal of Medical Internet Research, the study analyzed over 4,000 adult patients using electronic health records from March to May 2020. By examining factors such as past medical history, comorbidities, vital signs, and lab results at admission, the models were able to predict outcomes like intubation and mortality within clinically relevant time frames (3, 5, 7, and 10 days).

3. AI-Assisted Surgery: AI enhances surgical precision by providing real-time insights, reducing risks, and improving recovery times. For example, the da Vinci Surgical System is a real-world example of AI-assisted surgery that has transformed minimally invasive procedures.

The system features robotic arms equipped with surgical instruments that precisely mimic the surgeon’s hand movements, offering a greater range of motion compared to traditional laparoscopic tools. Surgeons control the robotic arms from a console in the operating room, which provides a 3D view of the surgical site and allows for precise manipulation of instruments.

4. AI in Imaging: AI aids in interpreting X-rays, CT scans, MRIs, and other imaging techniques, ensuring accurate diagnoses, pattern detection, and quicker decision-making. For example, AI in mammography (a type of imaging technique) is addressing the issue of breast cancer.

Through a collaboration between researchers and clinicians, an AI system was developed to help radiologists more accurately, quickly, and consistently detect breast cancer. This AI system, powered by Google’s artificial intelligence technology, is trained on thousands of de-identified mammograms to recognize patterns and features in breast tissue that are indicative of cancer.

5. Patient Monitoring and Virtual Assistance: AI helps track patient health, suggests treatments, and even provides virtual consultations in underserved areas by analyzing large datasets of health information.

For example, Babylon Health, a UK-based start-up, is using artificial intelligence (AI) to make healthcare more accessible and affordable worldwide. One of Babylon’s key offerings is the “Ask Babylon” feature, which uses AI to analyze symptoms, compare them to a database of medical conditions, and suggest potential diagnoses. While tools like ‘Ask Babylon’ offer helpful suggestions, they’re designed to support, not replace, licensed medical advice, and their accuracy has faced occasional scrutiny.

6. Healthcare Management: AI optimizes hospital operations, including patient scheduling, billing, and digital record management, improving the efficiency of healthcare delivery.

For example, Qventus, a California-based AI company, is transforming hospital operations by automating scheduling, testing workflows, and patient flow.t. AI Operational Assistants developed by Qventus act as vital partners to healthcare professionals, automating tasks like scheduling, pre-admission testing, and managing patient flow. The technology integrates with Electronic Health Records (EHRs) and uses generative AI, machine learning, and behavioral science to optimize hospital operations.

With its ability to learn and adapt, AI is not only enhancing healthcare delivery but also helping to bridge service gaps. However, the impact of AI extends beyond healthcare; it’s also making significant strides in agriculture and industrial biotechnology. Let’s explore how AI is also transforming these industries.

AI in Agricultural and Industrial Biotechnology

Source: NIH – AI-driven Agricultural Technology Strategy

Artificial Intelligence (AI) is reshaping both agriculture and industrial biotechnology by addressing critical challenges and driving innovation across these sectors. In agriculture, AI is helping to meet the growing demand for food by improving crop yields, optimizing resource use, and enhancing sustainability. Meanwhile, in industrial biotechnology, AI is streamlining biotechnological processes, making production more efficient, cost-effective, and sustainable.

AI in Agricultural Biotechnology

AI in agriculture is addressing the increasing demand for food, driven by global population growth. The agricultural sector faces several challenges, including pest control, inefficient pesticide use, drought, and a lack of adequate irrigation systems. Let’s explore some of the major contributions below.

1. AI for Crop Monitoring and Harvesting

AI-powered drones and robots are transforming real-time crop monitoring and harvesting. AI-powered drones and robots can assess crop health, detect early signs of disease, and even automate harvesting with greater precision and speed.. Machine learning (ML) models help predict weather patterns and other environmental factors, providing farmers with accurate forecasts for better decision-making.

2. Precision Agriculture

AI-based technologies allow for precise mapping and monitoring of fertilizer, pesticide, and herbicide needs, reducing waste and improving efficiency. For example, AI helps assess soil profiles and manage irrigation, enabling targeted interventions that conserve water and optimize crop growth.

3. Disease Detection and Prevention

AI-powered biosensors and drones are increasingly used to detect plant diseases early, even before symptoms appear. AI algorithms, such as convolutional neural networks (CNNs), analyze images from drones and identify diseases like bacterial leaf spots with accuracy rates of nearly 100%. Early detection helps reduce crop loss and improve overall yield.

Building on these advancements in agriculture, AI is also making a significant impact in industrial biotechnology. Let’s explore how AI is transforming this sector.

AI in Industrial Biotechnology

As AI continues to contribute to agriculture, its potential in industrial biotechnology is equally transformative. AI is being integrated into various biotechnological processes to optimize production, reduce costs, and improve product quality. From enzyme production to biofuel optimization, AI is driving efficiency and sustainability in industrial biotechnology.

Some of the major applications of AI in industrial biotechnology are:

1. Optimizing Bioprocesses

AI-based models are increasingly used to optimize fermentation processes and other biotechnological applications. For example, AI tools such as artificial neural networks (ANNs) and genetic algorithms (GAs) are used to optimize the production of enzymes like glucansucrase and cellulase, resulting in significant yield increases.

2. AI in Biofuel Production

In the bioenergy sector, AI models predict biomass properties and optimize biofuel production. By analyzing data from various sources, AI can help improve the efficiency of biofuel production through methods like torrefaction and pyrolysis. AI-based models, such as artificial neural networks (ANNs) and machine learning in biology, are also used to optimize methanolysis processes for biofuel production.

3. AI in Chemical and Pharmaceutical Production

AI is also enhancing the production of chemicals and pharmaceuticals by optimizing biotechnological processes. In the brewing industry, AI models assess beer quality and aroma profiles, ensuring consistent product quality. Similarly, AI helps improve the synthesis of biochemicals, textiles, and biodegradable polymers, reducing resource use and maximizing output.

AI is reshaping core bioprocesses by improving efficiency and cutting costs. This impact is particularly evident in fields such as single-cell and multi-omic analysis, as outlined below.

Single-Cell and Multi-Omic Research

Source: NIH – Multi-Omics Data Analysis and Integration

Besides the well-known omics layers: transcriptomics, genomics, proteomics, and metabolomics, other datasets are becoming central to decoding complex diseases like cancer. These include immunomics, microbiome data, multiplex family history data, and multilayer biomarker signatures. Here’s how AI fits into this picture.

1. Immunomics Data

Immunomics, also known as immune profiling, captures information on various immune cell types under different physiological conditions. It combines genomics, proteomics, and clinical data to map the immunome at both single-cell and population levels. T cells and B cells behave like intelligent systems; they learn to recognize antigens over time.

AI models can decode T-cell receptor (TCR) sequences to identify which antigens they recognize. Microsoft and others are building datasets for antigen-specific binding, targeting diseases including ovarian and pancreatic cancer. Machine learning is becoming increasingly essential in immune-oncology, from analyzing histopathology images to predicting patient outcomes and survival. AI tools like EpiToolKit predict peptide and MHC binding.

2. Microbiome Data

The human microbiome plays a critical role in health. Dysbiosis, or imbalance, is linked to many diseases, including cancer. Integrating microbiome data with other omics layers has led to new discoveries in the development of drugs. Projects like the Integrative Human Microbiome Project (iHMP) are expanding our view of host-microbe interactions. Metabolomics often bridges this interaction, revealing small-molecule exchanges that influence immune response and tumor behavior.

AI methods, including transfer learning, are being used to study less-known microbial species by borrowing insights from well-characterized ones. Metagenomic data have been used to predict colorectal cancer with strong accuracy across cohorts. Random Forests and Bayesian networks have accurately classified cancers based on microbiome profiles. Machine learning can even predict infection risk from pre-treatment microbiome data—an important step for cancer patients undergoing chemotherapy.

3. Multilayer Signature Biomarkers

Cancer is shaped by complex molecular interactions that span multiple omics layers. By integrating genomics, transcriptomics, proteomics, metabolomics, and immunomics, researchers are beginning to map these interactions with higher precision. Recent advances allow large-scale multi-omics profiling of patient cohorts. These datasets can reveal weak but consistent patterns tied to disease progression.

Network-level analyses show how proteins, RNAs, and metabolites interact across layers. Incorporating this data boosts the power of genetic mapping. Mathematical models, built using logic trees, kinetics, or differential equations, support system-level analysis of signaling and metabolic networks. AI can spot these cross-layer connections, especially in tumors where metabolic reprogramming is a hallmark.

While AI offers many advantages, it also introduces specific challenges that can’t be overlooked. Let’s take a closer look at some key concerns.

AI Models: Challenges and Future Opportunities

Artificial Intelligence (AI) has made remarkable strides across healthcare and biotechnology. However, its integration into these sectors faces several challenges that must be addressed to realize its full potential. From data limitations to the complexity of human decision-making, AI in these fields is still in its developmental phase.

Here are the key challenges AI models face today and the opportunities shaping their future development.

AI Models and Data Challenges

Despite their power, AI models depend on large, diverse datasets, yet access and quality remain major hurdles in healthcare and biotech. To train AI models effectively, vast amounts of diverse and high-quality data are needed. As you uncover these challenges, it becomes clear that overcoming data-related obstacles is essential for improving AI’s accuracy and generalizability in practical applications.

1. Data Needs and Availability for Training AI Models

AI models, especially in healthcare and biotechnology, heavily rely on vast and high-quality datasets for training. The availability of such data remains a challenge, as much of it is fragmented or siloed across different research institutions and organizations. Multi-omics data integration is vital for AI’s ability to offer meaningful insights, but gathering and standardizing these datasets is a complex task. This lack of unified data not only limits the accuracy of AI predictions but also slows down progress in precision medicine.

Creating shared data ecosystems and promoting open-access frameworks could help bridge this gap and accelerate innovation. Additionally, ethical considerations around data privacy and consent play a significant role in data accessibility. Computational biology greatly benefits from overcoming these challenges, enabling the creation of more precise models that can predict complex biological outcomes.

2. Generalizability and Interpretability in AI Predictions

While AI models show great potential in disease prediction, their ability to generalize across diverse populations remains a concern. Models that perform well in one dataset may not yield equally accurate results in others, particularly if the datasets have demographic or geographical differences. This raises critical questions about fairness and inclusivity in AI-driven healthcare.

To ensure reliable outcomes for all populations, it’s essential to train models on diverse, representative datasets and continually validate them in varied clinical settings. This lack of transparency hinders trust in AI models, especially in clinical settings. Integrating AI in bio research can enhance these models, ensuring more accurate and interpretable results that are critical for healthcare applications.

3. Addressing Biases and Ensuring Ethical AI Practices

AI algorithms can inherit biases from training data, which can lead to unintended discrimination, especially in healthcare applications. For instance, if a dataset underrepresents certain ethnic groups, the AI model may not accurately predict health risks for these populations. Addressing these biases through diverse, representative datasets and ensuring ethical AI practices are essential. This is particularly important when applying AI in drug discovery, where biases can affect drug efficacy predictions across different patient populations.

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Future Opportunities and Developments

With continued advancements, AI has the potential to opennew paths for personalized treatment, drug discovery, and more. Here are some of the key areas where AI can make a transformative impact.

1. Potential of AI in Various Biological Systems like Immunology

AI has a significant role to play in the future of immunology, where the complexity of immune responses requires intricate analysis. AI models can help researchers uncover new patterns within immune system data, including the interaction between immune cells and tumors, and identify novel biomarkers for disease progression. These insights can accelerate the development of targeted immunotherapies and improve patient stratification in clinical trials.

By integrating multi-omic and clinical datasets, AI enables a more precise understanding of immune dynamics, paving the way for more effective and personalized treatment strategies. This capability holds great promise in the field of cancer research, where understanding tumor microenvironments (TMEs) and immune responses could lead to more targeted therapies. By integrating AI in bio, researchers can gain deeper insights into these processes, enabling advancements in personalized medicine and immunotherapy.

2. Prospects of Integrating AI with Human Expertise

AI and human expertise can complement each other in transformative ways. While AI excels at analyzing vast amounts of complex data quickly, human clinicians and researchers bring valuable context and judgment to the table. The integration of AI with human insight can lead to more accurate diagnoses, targeted treatment recommendations, and innovative medical advancements. Moreover, this synergy is crucial in areas where data alone isn’t enough such as rare diseases or ambiguous clinical presentations.

Human expertise helps interpret edge cases, validate AI-generated hypotheses, and ensure ethical decision-making. As AI tools become more transparent and interpretable, clinicians will be better equipped to trust and apply them confidently in everyday practice. For example, AI-driven drug discovery, where computational models can predict potential drug candidates, but human expertise is needed to validate these predictions and ensure the development of safe and effective treatments.

3. Expanding AI Literacy and Encouraging Interdisciplinary Collaboration

To fully realize the potential of AI, expanding AI literacy across different fields, particularly biology, medicine, and agriculture, is crucial. Researchers and practitioners must have the necessary skills to understand and work with AI tools. Equally important is promoting interdisciplinary collaboration, which brings together experts in biology, computer science, statistics, and clinical practice.

Such collaboration ensures that AI models are not only technically sound but also grounded in real-world biological and clinical relevance. This integrated approach drives more practical, impactful innovations across scientific domains. Interdisciplinary collaboration is essential, as it fuels the exchange of knowledge between AI experts, biologists, clinicians, and data scientists. Embracing this kind of collaboration will also enhance efforts in computational biology, leading to groundbreaking advancements in healthcare and biotechnology.

Conclusion

Artificial Intelligence is rapidly transforming the biological sciences, streamlining everything from genomic analysis to drug discovery and personalized medicine. By accelerating data interpretation and uncovering complex biological insights, AI is reshaping how we study diseases, design treatments, and understand life at the molecular level.

Still, the road ahead calls for thoughtful integration. Addressing challenges like data bias, ethical use, and accessibility will be key to ensuring AI delivers equitable benefits across global healthcare and research.

If you’re looking to bring AI into your own biological research, Biostate AI offers dependable RNA sequencing services starting at just $80. From sample prep to analysis, our platform simplifies the entire process, giving you clean, actionable data to move your study forward.

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Disclaimer: This article provides general information about AI in biological science and its applications. It is not intended as medical advice. For any medical concerns, always consult with a licensed healthcare professional.

FAQs

1. How can AI assist in interpreting failed or inconclusive experimental results in biological research?

AI can detect hidden patterns in noisy or incomplete datasets, helping researchers revisit inconclusive experiments. It can also simulate alternate hypotheses, offering new experimental directions that might explain anomalies.

2. What role can AI play in regulatory compliance for biotech products?

AI systems can monitor real-time data from experiments or production pipelines and flag deviations from regulatory thresholds. They also assist in automating documentation, easing audits and approvals in the pharma and biotech sectors.

3. Can AI models be used to simulate rare disease progression where patient data is extremely limited?

Yes, through transfer learning and generative models, AI can simulate disease behavior using small datasets, especially when supplemented by synthetic data or known biological pathways, offering insights where traditional methods fall short.

4. How is AI contributing to the development of bioinformatics tools for non-model organisms?

AI accelerates genome assembly, annotation, and comparative analysis for non-model organisms by extrapolating from known genomes. This is vital in agricultural and environmental research, where many organisms lack reference genomes.

5. In what ways can AI streamline the drug repurposing process?

By analyzing large-scale omics data alongside clinical trial results, AI can identify unexpected therapeutic effects of existing drugs. This reduces cost and time-to-market for new applications, especially in oncology and neurology.