The Role of Machine Learning in Bioinformatics and Biology

Machine learning has multiple applications in diverse fields, ranging from natural language processing to healthcare. Bioinformatics and biology-related disciplines have not been left behind in the revolution. Before machine learning emerged, these disciplines faced the problem of extracting valuable insights from large biological datasets. But as of today, ML techniques such as deep learning can learn the features of complex datasets and present them in a manner that is easy to understand.

Machine learning is widely used in drug discovery—according to a 2023 Statista study, 75% of innovative biotech companies reduce research cycles by up to 50%. In this article, we are going to take a closer look at machine learning in bioinformatics and biology. But before that, let’s begin by having a quick look at what machine learning in biology and bioinformatics really means.

Key Insights

• Machine learning (ML) enables data-driven modeling of high-dimensional biological datasets (genomics, proteomics, imaging, multi-omics), shifting bioinformatics from descriptive analysis to predictive and generative inference (e.g., AlphaFold, DeepVariant, foundation models).
• Bioinformatics focuses on building computational tools and data infrastructure, while computational biology emphasizes mechanistic modeling and hypothesis testing using processed biological data.
• Key applications include variant calling (NGS), CRISPR target optimization, protein structure prediction, mass-spectrometry analysis, single-cell omics integration, biomedical text mining, and systems-level network modeling (e.g., GNNs, probabilistic graphical models).
• Foundation models (e.g., ESM, ProtBERT, Enformer) learn transferable biological representations from large-scale sequence and omics data, enabling fine-tuning for mutation effects, regulatory prediction, and protein engineering.
• Major challenges include small-sample/high-dimensional regimes, generalization across cohorts, interpretability for clinical translation, dataset bias, and regulatory validation constraints; successful deployment requires integration with experimental validation.

What is Machine Learning?

Machine learning is a subset of artificial intelligence that gives systems the ability to learn from data and perform tasks without being explicitly programmed. While this technology has been around for years, it is a recent development to apply complex mathematical calculations to big data. Below are some widely-used machine learning applications that you might already be familiar with:

• Personalized product recommendations
• Fraud detection technology
• Predictive algorithms
• Online data analytics and marketing optimization tools

Of course, the list of examples is far longer, but that’s a different story.

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Branches of Machine Learning

There are three main branches of machine learning models. These include:

• Reinforcement Learning: This type of ML technique can observe and interpret its environment through complex mathematical calculations that require robust computer infrastructure. The computers use trial and error to devise sustainable solutions for complex problems, allowing the machine to learn from its mistakes. This technology has been used to design autonomous cars.
• Supervised Learning: This technique uses labeled data sets to teach the machine how to classify data and accurately predict outcomes. It has a wide range of applications such as human resource allocation, spam filtering, fraud detection, and detection of malicious emails and links, among others.
• Unsupervised Learning: As the name suggests, unsupervised machine learning does not use labeled data sets. Instead, models work on their own to unmask hidden data patterns and gain meaningful insights from them. It’s comparable to the human brain.

Subsets of Machine Learning

• Deep Learning: Deep learning is a class of machine learning methods based on multi-layer neural networks capable of learning hierarchical representations directly from raw data. While loosely inspired by biological neural systems, modern deep learning architectures are mathematical constructs optimized for large-scale data processing rather than biologically realistic brain simulations. Deep learning has proven especially powerful in analyzing high-dimensional biological data such as genomic sequences, protein structures, medical images, and multi-omics datasets.
• Neural Networks: Just as the name suggests, neural networks are a series of neurons that aim to recognize the underlying relationship of a given set of data. They also mimic the way a human brain operates. Typically, a brain neuron receives an input that it processes to provide an output used by another neuron. The neural network uses the same principle to learn about a given data set and then predict the outcomes. It uses refined sets of data to perform analysis. The multiple layers in a neural network filter the information to give a refined output.

With the basics explained, let’s focus on bioinformatics.

What is bioinformatics?

Shortly put, bioinformatics is the application of computational and analysis techniques to capture and interpret biological data. It’s an interdisciplinary field between computer science, mathematics, statistics, biology, and genetics. Bioinformatics is mainly used to identify genes and nucleotides to better understand genetic diseases. It’s closely related to computational biology. Most people use the two terms interchangeably. But in the real sense, they are two distinct fields.

Let’s have a closer look at the two.

What’s the difference between Bioinformatics and Computational Biology?

Computational biology and bioinformatics are interdisciplinary approaches to life sciences. They draw from empirical disciplines such as information science, computer science, physics, and mathematics.

Both fields have emerged from the rapid growth of biotechnology around the globe and are often used in research centers, laboratories, and colleges.

While the two disciplines may sound similar, they are distinct in the kinds of needs they address.

Below are some of the main differences between them for reference and clarification.

Real-Life Case Study: Protein Structure Prediction with Computational Biology

One of the most transformative breakthroughs in computational biology has been the development of AlphaFold2 by DeepMind. This deep learning system predicts three-dimensional protein structures directly from amino acid sequences with unprecedented accuracy.

The release of predicted structures for millions of proteins has significantly accelerated:

• drug discovery,

• enzyme engineering,

• functional annotation of previously uncharacterized proteins.

More recently, AlphaFold-Multimer and similar models have enabled prediction of protein complexes, further extending the scope of structural bioinformatics.

This marks a shift from data analysis to structure-level biological inference powered by machine learning.

What Are Machine Learning Applications in Genomics?

Genomics is an essential domain of bioinformatics that focuses on studying genome mapping, evolution, and editing. A genome is a complete set of genetic material found in an organism. There are three main subsets of genomics;

• Regulatory genomics: It focuses on the study of how to regulate genomic expression. Machine learning applications in this branch of genomics include producing RNA-binding proteins and transcription factors and predicting and classifying gene expression.
• Structural genomics: It seeks to characterize genome structures by utilizing computational and experimental techniques. Machine learning in bioinformatics helps in this section to classify protein structures, i.e., primary, secondary, and tertiary structures.
• Functional genomics: In this section, researchers seek to describe gene functions and interactions. Machine learning in biology can help classify mutations and protein subcellular localization.

Machine learning methods combined with natural language processing have allowed researchers to analyze large amounts of genomics-related biological data. This way, they can easily solve problems such as relation extraction and named entity recognition.

Currently, the industry has a wide range of products and services in the commercial sector, thanks to machine learning. According to research, the industry is projected to reach a smashing 54.4 billion USD by 2025. Some of the applications of the ML in genomics include:

What Are Machine Learning Applications in Genome Sequencing?

Next-generation sequencing (NGS) technologies dramatically reduced both the cost and time required for whole-genome sequencing. While the Human Genome Project took over a decade due to technological and infrastructural limitations of the time, modern high-throughput sequencing platforms can sequence a human genome within days at a fraction of the original cost.

Machine learning enhances NGS workflows by improving:

• variant calling accuracy,
• error correction,
• structural variant detection,
• quality control and noise reduction.

Tools such as DeepVariant apply deep neural networks to improve variant detection accuracy beyond traditional statistical models.

What Are Machine Learning Applications in Gene Editing?

Gene editing is the process of manipulating the genetic composition of an organism by inserting, deleting, or replacing a DNA sequence. It uses a technology called CRISPR, which is a faster and cheaper method of conducting the process.

However, researchers still need to do the legwork of selecting the right DNA sequence, which can be a long process susceptible to errors. Machine learning has come to the rescue by making it easier to identify the correct target audience, significantly reducing the cost and time required to perform gene editing.

What Are Machine Learning Applications in Proteomics?

Proteomics is the study of protein components, their interactions with each other, and their roles in an organism. Mass-spectrometry-enabled proteomics has made it possible to analyze thousands of human proteins.

However, computational and experimental challenges have restricted its progress, requiring informatics solutions such as machine learning to analyze and interpret massive biological data sets. Mass spectrometry is an analytical tool used to characterize biological samples and is used in omics studies due to its high-throughput activities.

Mass spectrometry does not measure proteins directly in their conventional form. Instead, it splits them up into smaller chunks of amino acid sequences of about 30 building blocks. It then compares them with the database and assigns the amino acids to specific proteins. The results are not entirely accurate because some proteins are not recognized correctly.

Machine learning methods can be applied to identify a wide range of proteins from a given sample. They can be used on:

• The mass spectral peaks: Samples are analyzed without finding out which proteins and peptides are present. Instead, peaks with high signal intensities are summed up as possible biomarkers.
• Proteins recognized by sequence database searching: The sample analyzed is scanned for peptide masses that are then used to identify the proteins they relate to.

These techniques have been helpful in the diagnosis of different types of diseases and have an obvious advantage over traditional methods such as enzyme-linked immunosorbent assays (ELISAs), protein arrays, affinity separation, 2D gel electrophoresis, among others.

Recent advancements in the use of Machine learning in proteomics include the development of a software called Prosit[4]. It was used successfully by a team of researchers in the Technical University of Munich (TUM) to quickly recognize protein patterns without errors.

What Are Machine Learning Applications in Microarrays?

Microarrays are laboratory tools used to detect multiple gene expressions at a go. With the increasing popularity of genetic studies in animals, plants, and microbes, this technology is helpful in studying genome organization, gene expression, and chromatin structures.

A microarray is made up of different probes (DNA, RNA, tissues, proteins, and peptides) that correspond to gene segments placed in a specific arrangement, mostly on a silicon microchip or glass slide. The theory behind this technology is that complementary sequences will bind to each other under the right conditions, while non-contemporary ones will not bind. The level of hybridization between contemporary probes is indicated by fluorescence.

The level of complexity in microarray data sets is increasing at a rapid pace. Large-scale experiments require thousands of probes to be monitored simultaneously. Machine learning has made it easier to spot significant interactions involved in complex experiments. It has been used widely in microarray analysis, with gene classification and clustering being some of the most cited examples.

Neural Designer, for example, has made it possible for researchers to discover intricate relationships and identify complex patterns in microarray data by the use of machine learning methods. Public databases such as Array Express record all the information about a microarray experiment, making it readily available for reuse by the research community.

Some of the applications of machine learning methods on microarrays include:

• Gene Analysis: Analyzes changes in gene patterns to determine if they are normal or if a certain disease causes them.
• Differentiate gene stages: Ascertains the circumstances that make genes mutate from normal to disease state.
• Predict future gene stages: It develops models that can predict future gene changes using historical biological data.
• Prevents diseases: It helps in the discovery of relationships between genes and diseases and uses predictive modeling for early diagnosis and preventive medicine.

What is Single-Cell and Spatial Omics?

While microarrays played a foundational role in gene expression analysis, modern biology increasingly relies on single-cell sequencing technologies (scRNA-seq, scATAC-seq) and spatial transcriptomics.

These technologies generate extremely high-dimensional data requiring advanced machine learning techniques for:

• dimensionality reduction,

• batch effect correction,

• cell-type classification,

• trajectory inference,

• multi-modal data integration.

Deep generative models and variational autoencoders are frequently used to analyze and integrate single-cell datasets.

What Are Machine Learning Applications in Text Mining?

Recent advances in large language models (LLMs) have significantly improved biomedical text mining. Transformer-based architectures can now extract complex relationships between genes, proteins, diseases, and drugs from millions of scientific publications.

Text mining is also referred to as text analytics. It’s a machine learning-powered technology that uses natural language processing to examine large volumes of documents and discover new information that helps answer research questions.

The increase in biological publications has made it difficult for researchers to search through different sources and compile relevant information on a given topic. Machine learning can work through different types of human-generated reports in databases to process and analyze data, reducing labor costs and speeding up the research process without compromising quality.

ML text analysis can be used in bioinformatics for:

• Large-scale protein and molecule interaction analysis
• Translation of content into different languages
• Searching for novel drug targets (since it requires the extraction of information stored in biological journals and data sets)
• Automatic annotation of gene and protein functions
• Analysis of DNA expression arrays

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What Are Machine Learning Applications in Systems Biology?

Systems biology is the computational and mathematical analysis of the interactions and behavior of biological components such as molecules, cells, organs, and organisms. Computational modeling is a valuable tool used in this discipline. It uses mathematical modeling to capture the interactions between biological components and simulate the whole system’s behavior. However, it’s hard to build a steady mathematical model due to the complexity and lack of proper understanding of the underlying mechanisms.

But with the use of data-driven machine learning methods, it has become easier to model complex interactions in domains such as signal transduction networks, genetic networks, and metabolic pathways.

Machine learning is helpful in biological systems with sufficient biological data but not enough biological knowledge to develop theory-based models. A great example is the identification of the relationship between the phenotype and genotype of S. cerevisiae.

Even though there are many strains of characterized phonomes and genomes, there is still the unavailability of theory-based models that illustrate how the difference in genotypes dictates the strain phenotypes. Machine learning is used in this situation to establish the relationship between phenotypes and genotypes by training a supervised model with genomes as input and phenomes as output. The interpretation of the resulting model gives hints on the critical genetic composition of the organism. It helps in the identification of the most crucial factors that contribute to the model’s predictive power.

One of the most used machine learning techniques in systems biology is the probabilistic graphical model. It figures out the structure between different variables and is used to model genetic networks. Another common technique is genetic algorithms. It’s based on the natural process of evolution and has also been used to model genetic networks and regulatory structures.

Machine learning is also used to solve systems biology problems, such as the identification of transcription binding sites by the use of a technique called Markov chain optimization. A Markov Chain is a stochastic model that describes a sequence of possible events by relying on biological data obtained from previous events.

In recent years, graph neural networks (GNNs) have become a standard approach for modeling molecular structures. Additionally, generative models such as diffusion models and reinforcement learning-based systems are increasingly used to design novel drug candidates with optimized chemical and pharmacological properties.

These approaches enable de novo molecule design rather than simple screening of existing compound libraries.

How to Use Machine Learning and AI-Based Tools in Healthcare?

Machine learning and artificial intelligence are being used extensively in healthcare facilities to improve patient care and enhance the quality of life. Soon, hospitals might be able to use machine learning-based technology to obtain real-time data from multiple healthcare systems in different countries, increasing the efficiency of treatment. Some of the main applications of ML in healthcare include:

Drug Discovery and Manufacturing

Machine learning is widely used in the early stages of drug discovery processes. Some of the research and development technologies used include precision medicine and next-generation sequencing. They have proven to be helpful in finding alternative options for multifactorial disease therapy.

Medical Imaging and Diagnosis

Deep learning and machine learning have been used on a breakthrough technology known as computer vision. The technology has found acceptance in a wide range of applications. An example is Microsoft’s InnerEye project that builds innovative tools for quantitative analysis of 3D medical images.

Personalized Medicine

Predictive analytics can be used on patient data to facilitate personalized treatment. Currently, doctors are limited to a specific set of diagnoses or are forced to estimate the risk of a disease on a patient based on their health history and limited genetic information. However, this could change soon because machine learning is making great strides in medicine by leveraging patient data to help generate a wide range of treatment options.

Stroke Diagnosis

Machine learning uses pattern recognition to help diagnose, treat, and predict complications in various neurological diseases. Over the past few years, Acute Ischemic Stroke (AIS) treatment has experienced significant advancement. Machine learning algorithms are now being used to predict motor deficits in stroke patients. The most commonly used methods are Support Vector Machines (SVM), and 3D Conventional Neural Network (CNN).

What Are Foundation Models in Biology?

Inspired by large language models in NLP, foundation models are now emerging in biology. These models are trained on massive biological datasets such as:

• genomic sequences,

• protein sequences,

• multi-omics profiles.

Examples include:

• ESM (Evolutionary Scale Modeling),

• ProtBERT,

• genomic transformer models such as Enformer.

These models learn general biological representations that can be fine-tuned for specific downstream tasks such as mutation effect prediction, protein stability modeling, or regulatory element identification.

Foundation models represent a paradigm shift from task-specific modeling to transferable biological representations.

Machine Learning Tools Used in Bioinformatics and Biology

Deepvariant

This is a deep-learning tool that is used in genome data mining. It can predict common genetic variations more accurately as compared to previous classical methods. DeepVariant is one of the first biological tools that leverage machine learning and Google’s computing to provide a scalable, cloud-based solution that satisfies the needs of the most complex genomics data sets.

Atomwise Algorithms

Atomwise is a biotech company based in San Francisco that developed the first deep learning-based algorithm that helps convert molecules to 3D pixels. This conversion helps in the study of the 3D structure of proteins and other molecules with atomic precision.

Also, it basically predicts molecules that can possibly interact with a specific protein. The algorithms are mainly used in drug discovery.

Cell Profile

In the past, biological imaging software could only measure a single parameter of a group of images. However, that has changed, thanks to machine-learning methods. Scientists can now prepare and image a countless number of samples per day by using the CellProfiler software.

Moreover, the software can quantitatively measure individual features such as the number of fluorescent cells in a microscopy field. It can also point out thousands of cell features by the use of deep learning techniques.

Machine learning through deep learning algorithms extracts meaningful information from huge datasets such as genomes or a group of images and builds a model based on the extracted features. The model is then used to perform analysis on other biological datasets.

Limitations and Challenges of Machine Learning in Biology

Despite its transformative potential, machine learning in biology faces several important challenges:

1. Small sample sizes and high dimensionality: Biological datasets often contain thousands of features but relatively few samples, increasing the risk of overfitting.
2. Reproducibility issues: Models trained on one cohort may fail to generalize to different populations due to biological variability and batch effects.
3. Interpretability: In biomedical contexts, predictive performance alone is insufficient. Understanding which biological mechanisms drive model predictions is critical for clinical translation.
4. Data bias: Training data may not represent diverse populations, leading to biased predictions in healthcare applications.
5. Regulatory and validation requirements: Clinical applications require rigorous validation and regulatory approval (e.g., FDA, EMA), making deployment more complex than in other industries.

Acknowledging these limitations is essential for responsible and sustainable use of machine learning in life sciences.

Final Thoughts

One of the most pressing issues in bioinformatics and biology as a whole is the processing of huge datasets generated by newly developed technologies into meaningful information. However, as we step into the era of artificial intelligence and big data, machine learning in bioinformatics is taking a central role in carrying out this transformation.

This article has highlighted some of the essential concepts of machine learning and its recent applications in biology and bioinformatics. We have seen that ML can be used to devise complex algorithms and models that help in the prediction of trends across different biological disciplines. Ultimately, for these models to succeed, they need quality data in terms of statistical power and sample sizes.

Machine learning is not replacing experimental biology but augmenting it. The most impactful applications arise when computational predictions are combined with biological validation.

The future of life sciences lies in hybrid, data-driven and mechanistic approaches where machine learning models support hypothesis generation, accelerate discovery, and enable personalized interventions — while remaining grounded in rigorous validation and ethical responsibility.

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This article is an updated version of the publication from September 21, 2021. It was updated on February 23, 2026, to incorporate new technologies and tools, industry breakthroughs, and challenges in machine learning usage.

References

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