MOTIVATION: Cell-type identification is one of the most important tasks in single-cell RNA Sequencing (scRNA-Seq) analysis. Recent research has revealed contrastive learning's great potential in handling multiple cell-type identification tasks. RESULTS: In this work, we proposed a novel augmentation-free scRNA-Seq contrastive learning (AF-RCL) algorithm, which simplifies the conventional data augmentation operation and adopts a new contrastive learning loss function. A large-scale empirical evaluation suggests that AF-RCL not only outperformed other contrastive learning-based cell-type identification methods but also obtained state-of-the-art predictive performance compared with other well-known cell-type identification methods. Further analysis also shows AF-RCL's advantages in learning high-quality discriminative feature representations based on scRNA-Seq expression profiles. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://doi.org/10.6084/m9.figshare.28830311.v1 and at https://github.com/ibrahimsaggaf/AFRCL. The pre-trained AF-RCL encoders can be downloaded from https://doi.org/10.5281/zenodo.15109736, and the scRNA-Seq datasets used in this work can be downloaded from https://doi.org/10.5281/zenodo.8087611.

Understanding the roles of ageing-related genes is crucial for deciphering the mystery of ageing. In this work, we propose a novel hierarchical dependency-constrained tree augmented naïve Bayes algorithms, i.e. Hie–TAN–Lite, to classify model organisms’ genes into pro-longevity or anti-longevity genes. The method exploits the pre-defined generalisation-specialisation relationships between Gene Ontology terms as a type of constraint to learn the tree representation of dependencies among Gene Ontology terms, and also eliminates the hierarchical redundancy when learning the classifier. The experimental results showed that Hie–TAN–Lite successfully obtained better predictive performance than several other hierarchical dependency-constrained classification algorithms.

Gene Ontology terms are widely used as a type of informative feature for gene and protein-related predictive tasks. However, due to the pre-defined hierarchical dependencies between GO terms, the derived datasets are usually highly sparse leading to poor predictive performance. In this work, we propose four new types of averaged one-dependence estimators classification algorithms, which simultaneously exploit positive feature values and the pre-defined GO terms hierarchical dependencies as two types of constraints to regularize the one-dependence estimators learning process. The experimental results confirm that the newly proposed methods obtained significantly better predictive performance than the benchmark methods when coping with high-dimensional datasets exploiting Gene Ontology terms as features.

The Hierarchical Redundancy Eliminated Tree Augmented Naïve Bayes (HRE-TAN) classifier is a semi-naïve Bayesian model that learns a type of hierarchical redundancy-free tree-like feature representation to estimate data distributions. In this work, we propose two new types of positive feature values prioritized hierarchical redundancy eliminated tree augmented naïve Bayes classifiers that focus on features bearing positive instance values. The two newly proposed methods are applied to 28 real-world bioinformatics datasets showing better predictive performance than the conventional HRE-TAN classifier.

Accurate gene or protein function prediction is a key challenge in the post-genome era. Most current methods perform well on molecular function prediction, but struggle to provide useful annotations relating to biological process functions due to the limited power of sequence-based features in that functional domain. In this work, we systematically evaluate the predictive power of temporal transcription expression profiles for protein function prediction in Drosophila melanogaster. Our results show significantly better performance on predicting protein function when transcription expression profile-based features are integrated with sequence-derived features, compared with the sequence-derived features alone. We also observe that the combination of expression-based and sequence-based features leads to further improvement of accuracy on predicting all three domains of gene function. Based on the optimal feature combinations, we then propose a novel multi-classifier-based function prediction method for Drosophila melanogaster proteins, FFPred-fly+. Interpreting our machine learning models also allows us to identify some of the underlying links between biological processes and developmental stages of Drosophila melanogaster.

Ageing is a highly complex biological process that is still poorly understood. With the growing amount of ageing-related data available on the web, in particular concerning the genetics of ageing, it is timely to apply data mining methods to that data, in order to try to discover novel patterns that may assist ageing research. In this work, we introduce new hierarchical feature selection methods for the classification task of data mining and apply them to ageing-related data from four model organisms: Caenorhabditis elegans (worm), Saccharomyces cerevisiae (yeast), Drosophila melanogaster (fly), and Mus musculus (mouse). The main novel aspect of the proposed feature selection methods is that they exploit hierarchical relationships in the set of features (Gene Ontology terms) in order to improve the predictive accuracy of the Naïve Bayes and 1-Nearest Neighbour (1-NN) classifiers, which are used to classify model organisms' genes into pro-longevity or anti-longevity genes. The results show that our hierarchical feature selection methods, when used together with Naïve Bayes and 1-NN classifiers, obtain higher predictive accuracy than the standard (without feature selection) Naïve Bayes and 1-NN classifiers, respectively. We also discuss the biological relevance of a number of Gene Ontology terms very frequently selected by our algorithms in our datasets.

The genetic mechanisms of ageing are mysterious and sophisticated issues that attract biologists' attention. With the help of data mining techniques, some findings relevant to the ageing problem can be revealed. This paper studies the performance of Bayesian network augmented naive Bayes classifier, naive Bayes classifier and proposed feature selection methods for naive Bayes on predicting a C. elegans gene's effect on the organism's longevity. The results show that due to the hierarchical structure of predictor attribute values (Gene Ontology terms), the Bayesian network augmented naive Bayes classifier performs better than the naive Bayes classifier, and the proposed feature selection methods for naive Bayes can effectively optimize the predictive performance of naive Bayes.