The application of bioinformatics has revolutionized the practice of medicine in the past 20 years. From early studies that uncovered subtypes of cancer to broad efforts spearheaded by the Cancer Genome Atlas initiative, the use of bioinformatics strategies to analyse high-dimensional data has provided unprecedented insights into the molecular basis of disease. In addition to the identification of disease subtypes — which enables risk stratification — informatics analysis has facilitated the identification of novel risk factors and drivers of disease, biomarkers of progression and treatment response, as well as possibilities for drug repurposing or repositioning; moreover, bioinformatics has guided research towards precision and personalized medicine. Implementation of specific computational approaches such as artificial intelligence, machine learning and molecular subtyping has yet to become widespread in urology clinical practice for reasons of cost, disruption of clinical workflow and need for prospective validation of informatics approaches in independent patient cohorts. Solving these challenges might accelerate routine integration of bioinformatics into clinical settings.
Retrospective classification of tumours using novel bioinformatics approaches has provided unprecedented insights into the molecular basis of urological cancers.
Molecular classifiers provide a useful adjunct to standard-of-care for the management of urological cancers, but prospective prediction of treatment response using molecular classifiers is not yet applied routinely.
Machine learning (ML) and artificial intelligence (AI) algorithms might circumvent the challenge of inter-observer variability in histopathology and could be incorporated into routine clinical practice.
Benign urology and functional urological disorders require improved patient phenotyping to fully realize the power of bioinformatics, as observed in oncology.
Incorporation of ML and AI approaches into routine clinical practice will require adherence to best practices including transparency in reporting results, and external validation in independent samples or patient cohorts before implementation.
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The authors declare no competing interests.
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- Area under the curve
(AUC). The AUC of a receiver operating characteristic curve is used to measure the accuracy of a model. An AUC of 0.5 represents a model that does not perform any better than random chance.
- Artificial neural networks
Computational models inspired by the structure and functioning of biological neural networks, used in machine learning to process complex data and make predictions or classifications.
Biases are systematic errors or prejudices that can exist in artificial intelligence (AI) systems, data, algorithms or decision-making processes. Biases can arise owing to various factors, such as biased data collection, biased algorithm design or biased human decisions that influence the training process. For example, bias can be introduced if a training dataset used to develop an AI model for bladder cancer classification majorly consists of patient material from a specific demographic group or sex, or if certain genomic markers are prioritized.
- Cohen’s κ coefficient
A statistical parameter used to measure reliability between raters that can have values from −1 to +1, in which 0 is the extent of agreement expected by random chance.
- Data leakage
Data leakage occurs when information from the test set leaks into the training set, or vice versa. This phenomenon can happen when the data are pre-processed or cleaned before splitting, or when the test set is used to inform feature selection or model tuning. Data leakage can result in overly optimistic performance estimates, and the model might perform poorly on new, unseen data.
- Deep learning
A subfield of machine learning that uses artificial neural networks with multiple layers, enabling the artificial intelligence system to learn hierarchical representations and extract intricate patterns from large datasets.
A technique used in machine learning to prevent overfitting, in which certain information is temporarily ignored during training to ensure that the model does not overly rely on specific features, improving the ability of the model to generalize and make accurate predictions. By incorporating dropout, the ability of the model to generalize is improved and placing excessive importance on individual genes is avoided, ensuring a robust and reliable analysis.
- Early stopping
A strategy used during machine learning training to avoid overfitting, in which the training process is stopped before completion based on a specific measure (such as validation performance) to prevent the model from becoming too specialized to the training data, ensuring good ability to generalize to new, unseen data.
- Imbalanced classes
Classes are imbalanced when the proportion of observations in one class is much higher or lower than in the other. This phenomenon can lead to biased performance estimates, as the model might be accurate on the dominant class but perform poorly on the minority class. This problem can be addressed by using techniques such as stratified sampling, oversampling or undersampling.
- Learning rubbish (learning garbage)
This refers to the process of training an artificial intelligence system using low-quality or inaccurate data. For example, if in a study, the training dataset contains gene expression profiles from unrelated cancer types or includes samples with unreliable annotations, the AI system might learn from this rubbish data and produce misleading associations.
Microarrays are nucleic acid sequences corresponding to defined genes or transcripts arrayed on a solid phase support for hybridization with cDNA prepared from samples under investigation. Microarrays enable measurement of transcript abundance on a genome-wide scale.
- Natural language processing
A branch of artificial intelligence focused on the interaction between computers and human language, enabling machines to understand, interpret and generate human language text or speech.
- Negative predictive value
The proportion of individuals with a negative test result who do not have the disease.
- Non-representative sampling
Sampling is non-representative when the training or test set is not representative of the population from which the test was sampled. This phenomenon can happen when the data are collected from a biased or limited source, or when hidden confounding factors influence the outcome variable. Non-representative samples can lead to poor generalization and low predictive accuracy.
- Optimal fitting
Optimal fitting occurs when a model is sufficiently complex to capture underlying patterns in the data and generalizes well to new data. This fitting requires a balance between model complexity and the amount and quality of data available for training the model.
Overfitting occurs when a model is too complex and fits the training data too well, resulting in poor performance on new data. This phenomenon can happen when a model is trained on a limited set of data and learns the noise in the data instead of the underlying patterns. For example, in a situation in which a decision tree model is used to predict the outcome of a therapy based on the expression of a large number of genes in a tumour (features), but many of these genes are irrelevant or noisy, the model might overfit the data.
- Positive predictive value
The proportion of individuals with a positive test result who actually have the disease.
- Semi-supervised learning
A machine learning technique that uses a combination of labelled and unlabelled data to train an artificial intelligence system, leveraging the available labelled data and the patterns inferred from the unlabelled data.
- Small sample size
The sample size is small when too few observations are available in the training or test set to build or evaluate a robust model. In this case, the model either memorizes the training data or fails to capture the underlying patterns, leading to overfitting or underfitting. Small sample size can be addressed by increasing the size of the dataset, using data augmentation techniques or using models with increased robustness.
- Supervised learning
A machine learning technique in which the artificial intelligence system is trained using labelled data, where the input and corresponding output pairs are provided to guide the learning process.
- Test or testing set
A subset of a dataset used to evaluate the performance of a machine learning model. The purpose of the test dataset is to measure how well the model performs on new, unseen data. The test dataset is used to estimate the accuracy of the model’s predictions on new data.
- Training set
A subset of a dataset used to train a machine learning model. The purpose of the training dataset is to build the model by learning the relationships between the input variables (features) and the output variable (target variable). The model uses the training dataset to determine how to make predictions.
Underfitting occurs when a model is too simple and fails to capture the complexity of the data, resulting in poor performance on both the training data and new data. For example, if a linear regression model is used to predict the outcome of a therapy based on the size and number of tumours but the relationship between these variables is more complex, the model might underfit the data.
- Unsupervised learning
A machine learning technique in which the artificial intelligence system discovers patterns or structures in data without being explicitly guided or labelled.
Validation is the process of assessing the accuracy of a model on data that have not yet been seen by the model. Cross-validation is a technique in which a dataset is divided into multiple training and test sets, and the model is trained and tested on each set, to evaluate the performance of the model on the dataset. Common problems with separating the training and test sets include feature selection performed on the entire set, which can lead to overfitting and poor performance on new data.
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Hashemi Gheinani, A., Kim, J., You, S. et al. Bioinformatics in urology — molecular characterization of pathophysiology and response to treatment. Nat Rev Urol (2023). https://doi.org/10.1038/s41585-023-00805-3