A Biologist’s Guide to Artificial Intelligence: Building the foundations of Artificial Intelligence and Machine Learning for Achieving Advancements in Life Sciences

A Biologist’s Guide to Artificial Intelligence: Building the Foundations of Artificial Intelligence and Machine Learning for Achieving Advancements in Life Sciences provides an overview of the basics of Artificial Intelligence for life science biologists. In 14 chapters/sections, readers will find an introduction to Artificial Intelligence from a biologist’s perspective, including coverage of AI in precision medicine, disease detection, and drug development. The book also gives insights into the AI techniques used in biology and the applications of AI in food, and in environmental, evolutionary, agricultural, and bioinformatic sciences. Final chapters cover ethical issues surrounding AI and the impact of AI on the future. This book covers an interdisciplinary area and is therefore is an important subject matter resource and reference for researchers in biology and students pursuing their degrees in all areas of Life Sciences. It is also a useful title for the industry sector and computer scientists who would gain a better understanding of the needs and requirements of biological sciences and thus better tune the algorithms.

• Helps biologists succeed in understanding the concepts of Artificial Intelligence and machine learning
• Equips with new data mining strategies an easy interface into the world of Artificial Intelligence
• Enables researchers to enhance their own sphere of researching Artificial Intelligence

• Language: English
• Publisher: Academic Press
• Release date: Feb 29, 2024
• ISBN: 9780443240003

Book preview

Chapter 1: Exploring artificial intelligence through a biologist's lens

Shabia Shabir ¹ , and Ambreen Hamadani ²       ¹Islamic University of Science and Technology (IUST), Awantipora, Jammu and Kashmir, India      ²National Institute of Technology, Srinagar, Jammu and Kashmir, India

Abstract

The integration of artificial intelligence technology with biological science has the potential to make significant contributions and reshape experimental, exploratory, and analytical research in various domains of the biological field. This chapter aims to provide an overview of artificial intelligence and the different algorithms and techniques associated with it. Additionally, we will explore its advancements and the impact it has had on other fields, including healthcare, medical diagnostics, and computer vision. Lastly, we will discuss the emerging research areas and the challenges that need to be addressed for a better understanding of this field.

Keywords

AI; AI methods in biology; Artificial intelligence; Biologist; Biology and AI; Computational intelligence; Computer vision; Machine learning; Robotics; Statistical applications

Introduction

Artificial intelligence (AI) is a simulation technique that can create intelligent agents, usually with the learning capability of using perception in the form of data or sensors. Machine learning, being an area within AI, focuses on the design and development of algorithms to induce the learning capability in AI agents. Statistics is the technique that is extensively being used for analytical surveys, especially in Data Science. However, the main difference between statistical concepts and AI is that the former works by assuming a situation (hypothesis) and then testing its validity through some tests. In contrast, AI can create/predict a situation based on the data being provided (Krenn et al., 2022). Data mining is a prominent area of AI that focuses on extracting valuable insights, hidden patterns, or unknown knowledge from large datasets, databases, or data warehouses. It draws inspiration from various fields such as machine learning, AI, and statistics to develop techniques and algorithms for effective data analysis. By employing data mining methods, researchers and analysts can uncover meaningful relationships, trends, and patterns within complex and vast datasets that may not be apparent through traditional methods of analysis. These extracted insights can then be utilized for decision-making, predictive modeling, anomaly detection, and other valuable applications in diverse industries and domains (López, 2005). While machine learning, AI, and statistics share common goals, they employ different approaches to achieve them. Each field brings its unique methodologies and techniques to table. AI finds application in a wide range of areas, including robotics, strategic planning and scheduling, manufacturing, and maintenance. In robotics, AI techniques are used to develop intelligent systems that can perceive, reason, and act in the physical world. Strategic planning and scheduling benefit from AI algorithms to optimize decision-making processes and resource allocation. Manufacturing utilizes AI for automation, quality control, and process optimization. Maintenance processes can be enhanced through predictive analytics and machine learning algorithms to detect potential failures and schedule maintenance activities proactively. Collaboration and knowledge sharing among researchers from different fields are crucial. By exchanging their insights and experiences, experts from various domains can contribute to the development of new technologies and approaches. This interdisciplinary collaboration can lead to enhanced understanding and the extraction of hidden knowledge within their respective fields, ultimately fostering innovation and advancement in AI applications. Fig. 1.1 gives a better understanding of the relationship between statistics and AI.

Due to the increase in volume/size and complexity of biological data, there became a necessity to introduce AI techniques in the field to create various prospective and predictive models. This would enhance the diagnostic system in the medical field and help in error-free analysis (Na, 2020). Various techniques in AI have been introduced that are well suited to the type of medical data available such as gene expression/sequence, protein/molecular structure, or images. Even the surveys collected from patients could be helpful in creating various analytical models for future perception.

Before we go through the various AI techniques used extensively in biological science, we must have a good knowledge of the various concepts often used in AI. Some of them are listed below:

1. Dataset: The dataset is formed after the data are collected from heterogeneous sources through the process of extract, transform, and load (ETL) or ELT. This shall process the data in a standardized form suitable for analysis (Hassoun et al., 2021). This is usually in the form of rows and columns, wherein the columns/attributes represent the features or attributes of a particular row or selection. Each row consists of inputs (X1, X2, X3, …., Xn) and output as (Y).

2. Feature selection: Various features are considered to be irrelevant concerning the type of analysis we need to perform. Although the whole dataset may be meaningful data, however, we need to select some of the features that are most relevant and can strongly predict the output of the AI model (Setua et al., 2017). This is done through the process of feature selection using various techniques like filter or wrapper methods (Li et al., 2019).

3. Training and testing dataset: The dataset is divided into two sub-datasets, that is, training dataset is used when we want to train the model, wherein the labeled output is provided with an input set. This labeled output indicates the class to which the record belongs, whereas the testing dataset is without the output that is used to assess the performance of the model, that is, how well a particular learned model predicts (Géron, 2019). The model learns from training by adjusting its internal parameters or weights based on the patterns and relationships it discovers. The size and diversity of the dataset determine the model's ability to generalize and make accurate predictions on unseen data.

Figure 1.1  Artificial intelligence (AI) in relation to statistics and other fields.

4. Machine learning: It refers to the process by which an AI system acquires knowledge or improves its performance through experience or data, thus enabling the system to make predictions, recognize patterns, solve problems, and perform various tasks. Learning in AI is of two types - supervised and unsupervised learning (Ang et al., 2016).

5. Supervised learning: In this learning, a labeled dataset is being provided with defined output as a training set and the test dataset, which is without the output is being used to determine the performance of the model. Some of the models that work on this principle include classification models like KNN, trees, random forest, logistic regression, NaiveBayes, SVM, etc.

6. Unsupervised learning: In this learning, an unlabeled dataset with no defined output is provided to make the model learn about the various similarities in the data. Some of the models that work on this principle include various clustering models like K-means, PCA, etc, and association analysis such as Apriori, FP-growth, and Hidden Markow models.

7. Cost function: In machine learning, there is a measure to assess the discrepancy between the model's predictions and the actual output, known as the error or loss function. This function quantifies the extent to which the model deviates from accurately capturing the relationship between the input and output data. While accuracy metrics indicate how well the model is performing overall, they do not provide specific insights on how to enhance its performance. To address this, a corrective or optimization function is utilized to determine when the model is most accurate or when the error is minimized. This function aids in the computation of adjustments that can be made to improve the model's predictions. By iteratively optimizing the model based on the error function, machine learning algorithms can refine their performance and enhance their accuracy (Lim and Kang, 2011). An example illustrating the concept of a cost function can be seen in a scenario where a robot is assigned the task of lifting and stacking boxes, potentially encountering obstacles in the process. In this case, the cost function is utilized to determine the minimum value of the root mean square error (RMSE). The RMSE is calculated by taking the square root of the average of the squared differences between the predicted values and the actual values. By employing the cost function and minimizing the RMSE, the robot can optimize its movements and actions to minimize the discrepancy between its predicted outcomes and the actual outcomes. This allows the robot to improve its accuracy and efficiency in completing the given task, navigating around obstacles, and achieving the desired stacking of boxes.

8. Overfitting and underfitting: Overfitting occurs when an AI model attempts to learn from data that may contain noise and tries to fit each data point precisely on a curve. This phenomenon arises when the model lacks flexibility, leading to poor predictions on new data points. Consequently, the model may reject every new data point during prediction. Overfitting is often associated with models that have high variance, and it can be triggered by factors such as unclean data or an insufficient training dataset. On the other hand, underfitting refers to a situation where the model fails to learn the relationships between variables in the data or makes accurate predictions or classifications on new data points. Since the model does not fully grasp the underlying patterns, it accepts every new data point during prediction, regardless of its accuracy. Underfitting typically occurs when there is unclean data or when the model has high bias, implying that it oversimplifies the relationships between variables. In summary, overfitting and underfitting represent two extremes in model performance. Overfitting occurs when the model learns the training data too well, while underfitting arises when the model fails to capture the underlying patterns in the data effectively. Both situations can be problematic and can be mitigated by using appropriate techniques such as regularization, cross-validation, or increasing the complexity of the model.

Machine learning algorithms—the foundations of AI

The first challenging task when applying AI is to select the appropriate model based on which certain predictions and patterns would be identified. The task is challenging because the better the model, the better would be the decision. Various steps are involved in the selection of the model:

1. Defining a task: This involves specifying a problem and objective that needs to be resolved by the AI model. This would give a clear insight into what the purpose of an AI system should be. The problem statement is being provided at this stage.

2. Obtaining dataset: Once the problem is known, sufficient and relevant data should be made available for implementation to avoid overfitting and underfitting issues. The dataset may include the relevant features or attributes that need to be focused on for analytical purposes. In the case of classification, labeled target output should be made available.

3. Designing test and training set: Once the dataset is available, we need to create subsets of the dataset to determine the records that need to be utilized for training purposes and the rest for testing purposes. The more the size and diversity of the training set, the better the ability of the model to provide accurate predictions on unseen data. Further, the test set estimates how well the model is likely to perform on unseen or real-world data (Bishop and Nasrabadi, 2006).

4. Selection of the model: This involves choosing the most suitable algorithm or architecture that can effectively solve the problem at hand. The selection depends on various factors, including the nature of the data, the complexity of the task, the data available, the desired performance metrics, etc. Certain criteria can help out in the process of selection (Greener et al., 2022): (a) If the data are sufficient to be provided to the model with a fixed number of labeled features, then we can predict the class or value by either going with traditional classification algorithms like multilayer perception, SVM, random forest, etc., with labeled data or else go with clustering algorithm in case of unlabeled data. (b) If the data are more than sufficient with a large number of unlabeled features and there is a connection between various entities in the data, then a graph convolutional network (GCN) can be used (Ye et al., 2023). (c) If, in the large dataset, no connections are being identified, and the data are spatial or image, then the choice could be 2D/3D convolutional network or else if data are sequential instead of spatial, then the choice could be recurrent neural network (RNN) or 1D convolutional network (Esteva et al., 2017).

5. Training and testing: Once the model is selected, we need to train it using a training dataset, and it can tune the model through various iterations by adjusting its internal parameters or weights based on the patterns and relationships it discovers. The test set would determine the accuracy of the model in the case of the labeled training dataset.

Integrating AI with biological science

With the increase in size, complexity, and type of biological data, it became a necessity to introduce some computational methodology to improve the diagnostic and prediction system. AI has helped and affected various fields of biology like genome analytics where genomic data are analyzed using various AI algorithms, and further can be used for protein structure and genes. It may help in identifying the genetic variants associated with diseases and help in drug development. Apart from that medical diagnostics is another field, wherein AI analyze medical tests in the form of images (MRI/X-rays) for disease detection and classification. AI helps in the protection of the ecosystem by analyzing environmental behavior and changes. It helps in resource management and conservation. Various AI algorithms listed below have effectively helped in various fields of biological science.

Logistic regression

Logistic regression is a statistical model used for binary classification tasks, where the goal is to determine the probability that an input belongs to one of two classes. It is a popular and widely used algorithm in machine learning and statistics. A logistic regression model is based on a logistic function, also known as a sigmoid function, which maps any real-valued number to a value between 0 and 1. In logistic regression, the input features are combined linearly using weights, and the results are passed through a sigmoid function to obtain a predicted probability. In mathematics, a logistic regression model can be represented as:

P(y = 1|x) = sigmoid(w^Tx + b). where: P(y = 1|x) is the probability that the output variable y equals 1 given the input characteristic x. w is the weight vector associated with the input features, and b is the negative term.

In training, logistic regression models are fitted to the training data, which are labeled and are having fixed number of features (Kleinbaum et al., 2002). Logistic regression has many advantages, including simplicity, interpretability, and efficiency, and has a good benchmark. It can handle both numerical and categorical input features and can be extended to handle multi-class classification problems. However, logistic regression assumes a linear relationship between input characteristics and the log-oddness of the outcome. If the relationship is not linear, that is, complex feature relationship, feature engineering, or more complex models may be required. This model also overfits if the number of features is large.

An application of AI can be observed in the field of protein-variant effect prediction, where researchers in biology have introduced a method called DeMaSk. This method aims to predict the impact of missense mutations in proteins by leveraging deep mutation scanning (DMS) databases and sequence homologs. DeMaSk exhibits state-of-the-art performance in accurately predicting the effect of amino acid substitutions and can be readily applied to protein sequences of any type. This approach holds promise for enhancing our understanding of the functional consequences of mutations in proteins and can have implications in various biological and biomedical research domains (Munro and Singh, 2020). Apart from that, its application is found in chemical/biochemical reaction kinetics where the researchers have presented solutions for detecting chemical kinetic and analogous models, which involve the systems where the concentrations of intermediates are not complex in terms of analysis (Haario and Taavitsainen, 1998).

Support vector machine

Support vector machines (SVMs) are versatile machine learning algorithms employed for classification and regression tasks. They excel in scenarios with intricate decision boundaries and high-dimensional data. SVMs are particularly suitable for labeled data and a fixed number of features. The fundamental concept of SVMs involves identifying an optimal hyperplane that effectively separates data points belonging to different classes. In binary classification, the objective is to construct a hyperplane that maximizes the margin between the two classes. The margin represents the perpendicular distance between the hyperplane and the nearest data points of each class, known as support vectors. By finding the hyperplane with the largest margin, SVMs can robustly classify new data points based on their position about this decision boundary. SVMs have demonstrated effectiveness in various domains and offer a valuable tool for tackling complex classification problems and handling high-dimensional datasets. The various steps involved in this algorithm include data preparation, feature mapping, hyperplane optimization, margin maximization, regularization, and tuning. After the SVM model is trained, it can predict the class of the test input data or data points by checking which side of the hyperplane they lie on. SVM performs linear and nonlinear classification and regression; however, it is difficult to scale up the model for large datasets.

Application of this model has been found in protein function prediction, wherein Gene Ontology terms have been assigned to human protein chains for better performance (Cozzetto et al., 2016) and transmembrane-protein topology prediction, which integrates both signal peptide and re-entrant (Nugent and Jones, 2009).

Random forest is a machine learning algorithm that belongs to the family of ensemble methods. It performs predictions by connecting multiple decision trees and is widely used in classification and post-hoc applications. Random forest allows you to measure the importance of features based on their contribution to the accuracy of the sample. It can handle high-dimensional data and has been successfully used in fields such as finance, health care, image recognition, and text analysis. The chance of overfitting is reduced, and better generalization for unseen data is provided. It is known for its ability to handle complex data sets and deliver accurate predictions. However, the number of trees, depth per tree, and other hyperparameters must be carefully tuned to optimize the performance for the particular problem.

Application of random forest is found in the development of protein–ligand interaction functions (Wang and Zhang, 2017) and in the prediction of disease-associated genome mutations, wherein nonsynonymous single nucleotide polymorphisms (nsSNPs) prevalent in genomes are closely related to inherited diseases (Bao et al., 2005).

Gradient boosting

This model is used for regression and classification tasks. This model is built by ensembling various weak machine-learning models like decision trees. Various implementations of gradient boosting are available, with XGBoost, LightGBM, and CatBoost being popular libraries. These services streamline the curriculum and provide additional resources to increase productivity and productivity. It should be noted that gradient boosting can require careful tuning of hyperparameters and can be computationally intensive compared to some other algorithms. However, due to its high interpretability, strong predictive capabilities, and less sensitivity to feature scaling, it is widely used in various industries such as finance, healthcare, web search, and recommendation systems.

The application of this model lies in gene expression profiling, wherein gene expression values were predicted based on XGBoost (Li et al., 2019).

Clustering

Clusters are used in AI and machine learning to group similar data points based on their characteristics or patterns. This is an unsupervised learning method, which means it does not need any labeled data for training. The purpose of clustering is to identify underlying structures or patterns in a data set, where data points in one cluster are more similar than those in another cluster. Clustering can provide insight into the underlying distribution of data ho, identify natural clusters, or help explore and understand data. Certain cluster validation metrics can assess the performance of the clustering process and is suitable for low dimensional data. However, it is difficult to scale up for large datasets, and there may be some contradictory outputs due to the inclusion of noisy datasets.

Application of clustering can be found in differential gene expression analysis, wherein similarly expressed genes are identified across the patient under study (Altman and Krzywinski, 2017). Apart from this, its application is found in protein structure prediction, wherein a strategy named SPICKER has been presented to identify near-native folds. This is done by clustering protein structures generated during computer simulations (Zhang and Skolnick, 2004).

Genetic algorithm

The genetic algorithm is an inspiration from Charles Darwin's principle of natural evolution. It is an optimization algorithm and is often used in AI and machine learning to solve complex optimization problems. The basic idea behind genetic algorithms is to mimic the evolutionary process of organisms to find the best solution to the problem. The algorithm contains a population of preferred solutions, usually represented as sequences of values, chromosomes, or individuals. These individuals undergo genetic operations such as selection, crossover, and mutation that can produce new offspring with improved fitness. Genetic algorithms have been successfully applied to various problems, including parameter optimization, feature selection, scheduling, vehicle routing, and many more, but their performance can be affected by factors such as choice of genetic operators, population number, and termination criteria affect. Often, these processes need to be optimized to achieve the best results. Application of genetic algorithm is found in eliminating multiple sequence alignment process by integrating it with the data mining approach and overcoming the complexity of motif discovery (Baloglu and Kaya, 2006).

Fuzzy logic

Fuzzy logic is a mathematical framework for reasoning and decision-making under uncertainty and ambiguity. It provides a way to deal with ambiguity or ambiguity, resulting in flexible and nuanced decision-making processes. In traditional binary theory, statements are either true or false. In real-world situations, however, there are usually multiple members of a truth or group. Fuzzy logic extends binary logic by introducing the concept of fuzzy sets that generate the number of members for elements based on their number of members (Khan and Quadri, 2017). Fuzzy thinking is not a substitute for classical thinking but rather a complementary tool that deals with situations of uncertainty and ambiguity. It provides a framework for making decisions based on incomplete or ambiguous information, allowing for greater flexibility and robustness in solving complex real-world problems. Application of Fuzzy logic is found in bioinformatics and biomedical engineering (Bordon et al., 2015; Xu, 2008).

Neural network/multilayer perceptron

A multilayer perceptron (MLP) is a popular architecture within the field of artificial neural networks (ANNs). It comprises interconnected artificial neurons or perceptrons and serves as a fundamental framework in the domain of deep learning. MLPs are widely employed for various tasks, including classification, regression, and pattern recognition. Their ability to model complex relationships and handle large datasets makes them a valuable tool in many applications within the field of machine learning.

Each node in an MLP receives information from the previous stage, computes that information, and passes the result to the next stage. The layers between the input and output layers are called hidden layers, and they enable the network to see a complex representation of the input data. The main building block of MLP is the perceptron, which is a mathematical model of a biological neuron. A perceptron takes the weighted inputs, sums them, applies the activation function, and produces the output. The activation function introduces nonlinearity into the network, allowing the identification of nonlinear relationships in the data. MLPs have been successfully used for a wide range of tasks, including classification, regression, and pattern recognition. However, they have some limitations, such as the need for large amounts of labeled training data and the tendency to fit too complex data. A variety of strategies can be used to reduce these issues, including regular attendance, dropouts, and early delays. MLP can fit fixed-size datasets with fewer layers like CNN, which makes it easy and faster to get trained. However, it has the disadvantage of getting overfitted easily, and a large number of parameters are to be managed. Interpretability is not that easy. Application of this model has been found in protein secondary structure detection and analysis (Buchan and Jones, 2019), disease diagnostics (Setua et al., 2017) and computational drug toxicity prediction (Mayr et al., 2016).

Convolutional neural network

In recent times, advanced neural network architectures such as convolutional neural networks (CNNs) and RNNs have gained significant popularity in the field of deep learning. However, the MLP remains a fundamental concept that aids in understanding neural networks.

CNNs, specifically, have shown remarkable effectiveness in analyzing visual data like images and videos. Inspired by the human visual system, CNNs have achieved great success in computer vision tasks. A key component of a CNN is the convolutional layer, where a small matrix called a filter or kernel is convolved with the input image. This operation calculates element-wise products and sums at each position, resulting in a feature map that captures local patterns and structures within the image. By utilizing multiple filters, the network can learn to recognize various objects at different levels of abstraction. CNNs excel at processing spatial input data arranged in a grid-like format, making them suitable for tasks with varying image sizes.

Overall, CNNs have revolutionized the field of computer vision and continue to be a pivotal tool for image analysis, object detection, and other related tasks. CNNs have revolutionized computer vision, achieving state-of-the-art performance in image classification, object recognition, logical segmentation, and image generation. They are also used in other areas beyond vision, such as in natural language processing (NLP) and speech and pattern recognition. However, it is harder to train deeper architectures, thus making predictions complex. Application of CNN is found in protein residue contact and distance prediction predicts the 3D shape of a protein from its amino acid sequence (Senior et al., 2020) and medical image recognition in case of skin cancer (Esteva et al., 2017).

Recurrent neural network

Recurrent neural networks are a type of ANN designed to use the concept of recursive connections to process sequential data. Unlike feedforward neural networks, which process data strictly sequentially, RNNs have response links that maintain information and pass from one ladder to another. The key feature of RNN is that it can capture dependencies and patterns in a sequence of data by holding a hidden state that summarizes the findings so far At each step in the sequence, RNN takes input updates and hidden state depending on the current input and previous hidden state. It works well with variable sized sequential data (for example, biological sequences or time-series data) can model complex temporal relationships. RNNs have been successfully applied in various sequential or time series tasks in many areas of biology, such as NLP, speech recognition, machine translation, sentiment analysis, signature recognition, and validation in patterns and generating sequences of arbitrary length because they can dynamically customize hidden state based on the input context. A limitation of traditional RNNs is the long training time and high computing memory. Apart from this, it faces difficulty in capturing long-term dependencies due to frequent decay, where the slope decreases sharply with long-term propagation To consume on top of this, several RNNs have been developed, such as long-term password memory (LSTM) and gated repetitive unit (GRU) networks, which incorporate gating mechanisms, thus allowing the network to selectively update and recall information over long sequences. Application of RNN is found in protein engineering (Alley et al., 2019; Choi, 2015) and DNA sequencing (Quang and Xie, 2016).

Graph convolutional network

GNN stands for graph neural network, which is a type of neural network designed to work with graph-structured data. Graphs have nodes (also called vertices) and edges that connect pairs of nodes. The model is specifically designed to learn and process information from the nodes and edges of a graph, enabling them to model and analyze complex relationships and dependencies in graph data. Input data are variably sized and characterized by the connection between various types of entities like spatial. The main idea behind GNN is to propagate information in a graph by recursively updating hidden representations based on each node's neighboring nodes. This process is usually done in multiple layers, where each layer collects information from neighboring nodes and adds to the current node representation. It can be used for various graph-related tasks, such as node classification, link prediction, graph classification, and recommendation processing. Attempts are made in situations where the graph structure contains valuable information to be used for prediction or analysis. Significant attention has been gained by the model in recent years due to its ability to model and reason about complex relational data. It has been successfully applied to various domains, including social network analysis, knowledge graph reasoning, drug discovery, recommendation systems, and traffic prediction, among others. GNNs possess high computing memory for large densely connected graphs and provide a powerful framework for learning and making predictions on data with inherent graph structures and most relevant associations. However, the model is hard to train deeper architectures.

Application of GNN is found in predicting drug properties for modeling polypharmacy side effects (Zitnik et al., 2018) and in interpreting molecular structures for antibiotic discovery (Gainza et al., 2020; Stokes et al., 2020) and knowledge extraction in modeling relational data (Schlichtkrull et al., 2017).

Research challenges

While the adoption of AI in research is gaining momentum, there are still challenges involved in research, development, and technology dissemination. These will be discussed in detail in the later chapters. We give a brief overview in this chapter.

AI research in biology presents both research and implementation challenges. From a research perspective, one challenge is the interpretation and explainability of AI models. Understanding the reasoning behind AI predictions or decisions in biological contexts is essential for researchers to gain insights and validate the results. Another research challenge lies in the availability and quality of biological data. Obtaining diverse and well-curated data for training AI models can be difficult due to the complexity and heterogeneity of biological systems. Additionally, integrating multiomics data and developing effective algorithms for data fusion and analysis pose research challenges.

On the implementation side, scalability and efficiency are crucial challenges. Applying AI techniques to large-scale biological datasets requires scalable algorithms and computing infrastructure to process and analyze the data efficiently. Moreover, implementing AI models in real-world biological applications often requires addressing practical issues such as data privacy and security, regulatory compliance, and interoperability with existing systems and workflows. Successful implementation also necessitates collaboration between AI researchers, biologists, and clinicians, bridging the gap between these domains and ensuring effective knowledge transfer. By addressing these research and implementation challenges, AI in biology can lead to transformative advancements in understanding biological systems, driving healthcare innovations, and facilitating personalized medicine.

Conclusion

When dealing with biological data, it is advisable to initially explore traditional machine learning algorithms before delving into deep learning methods. Traditional machine learning techniques can be highly effective and efficient, particularly in cases where the dataset is small, contains a limited number of features, or lacks structured relationships among the features. On the other hand, deep learning is most advantageous when data are abundant; each data point consists of multiple objects, and there are clear relationships among the features, such as adjacent pixels in images or biological data like DNA, RNA, protein sequences, and microscopy images.

While deep learning holds immense potential for analyzing biological data, it is crucial to consider factors such as data availability, feature count, and the structured relationships between features. Traditional machine learning approaches offer the benefits of rapid development and practical applicability, making them valuable options to consider. Moreover, the field of AI can draw inspiration from biology in the development of new algorithm architectures. Biological systems have evolved to handle and interpret complex data effectively. Studying biological systems can inspire AI researchers to design novel algorithms that can enhance the capabilities of AI systems. Overall, fostering cross-disciplinary collaboration among biologists, computer scientists, and engineers can pave the way for the next generation of AI in biology. By combining their expertise and integrating principles from biology, researchers can address challenges related to data, theory, model development, and other obstacles encountered in AI. This collaborative approach has the potential to drive advancements in both AI and biology, leading to exciting breakthroughs and discoveries at the intersection of these