Cancer driver genes (CDGs) are crucial in cancer development and are key targets for treatment. This study proposes a novel approach utilizing Graph Neural Networks (GNNs), particularly Graph Convolutional Networks (GCNs), to predict CDGs by integrating protein-protein interaction (PPI) networks and multi-omics data. We optimized GCN hyperparameters across six different PPI networks from literature, which improved predictive accuracy. A consensus model was created using genes identified across all networks. We used ensemble-based methods, including majority voting and score aggregation, to identify high-probability CDGs. Comparisons showed that ensemble models outperformed individual models. Our approach identified several candidate genes, including some previously unclassified, as potential CDGs in various cancers. Notably, 33 genes were consistently predicted as CDGs across all networks, highlighting their potential importance in cancer development. This study highlights the effectiveness of GNNs for CDG prediction and the value of integrating multi-omics data and diverse PPI networks for advancing cancer genomics research and targeted therapy development.

OBJECTIVE: This study aims to comprehensively review the use of graph neural networks (GNNs) for clinical risk prediction based on electronic health records (EHRs). The primary goal is to provide an overview of the state-of-the-art of this subject, highlighting ongoing research efforts and identifying existing challenges in developing effective GNNs for improved prediction of clinical risks. METHODS: A search was conducted in the Scopus, PubMed, ACM Digital Library, and Embase databases to identify relevant English-language papers that used GNNs for clinical risk prediction based on EHR data. The study includes original research papers published between January 2009 and May 2023. RESULTS: Following the initial screening process, 50 articles were included in the data collection. A significant increase in publications from 2020 was observed, with most selected papers focusing on diagnosis prediction (n = 36). The study revealed that the graph attention network (GAT) (n = 19) was the most prevalent architecture, and MIMIC-III (n = 23) was the most common data resource. CONCLUSION: GNNs are relevant tools for predicting clinical risk by accounting for the relational aspects among medical events and entities and managing large volumes of EHR data. Future studies in this area may address challenges such as EHR data heterogeneity, multimodality, and model interpretability, aiming to develop more holistic GNN models that can produce more accurate predictions, be effectively implemented in clinical settings, and ultimately improve patient care.

Identifying the genes and mutations that drive the emergence of tumors is a critical step to improving our understanding of cancer and identifying new directions for disease diagnosis and treatment. Despite the large volume of genomics data, the precise detection of driver mutations and their carrying genes, known as cancer driver genes, from the millions of possible somatic mutations remains a challenge. Computational methods play an increasingly important role in discovering genomic patterns associated with cancer drivers and developing predictive models to identify these elements. Machine learning (ML), including deep learning, has been the engine behind many of these efforts and provides excellent opportunities for tackling remaining gaps in the field. Thus, this survey aims to perform a comprehensive analysis of ML-based computational approaches to identify cancer driver mutations and genes, providing an integrated, panoramic view of the broad data and algorithmic landscape within this scientific problem. We discuss how the interactions among data types and ML algorithms have been explored in previous solutions and outline current analytical limitations that deserve further attention from the scientific community. We hope that by helping readers become more familiar with significant developments in the field brought by ML, we may inspire new researchers to address open problems and advance our knowledge towards cancer driver discovery.

Identifying essential genes and proteins is a critical step towards a better understanding of human biology and pathology. Computational approaches helped to mitigate experimental constraints by exploring machine learning (ML) methods and the correlation of essentiality with biological information, especially protein-protein interaction (PPI) networks, to predict essential genes. Nonetheless, their performance is still limited, as network-based centralities are not exclusive proxies of essentiality, and traditional ML methods are unable to learn from non-euclidean domains such as graphs. Given these limitations, we proposed EPGAT, an approach for Essentiality Prediction based on Graph Attention Networks (GATs), which are attention-based Graph Neural Networks (GNNs), operating on graph-structured data. Our model directly learns gene essentiality patterns from PPI networks, integrating additional evidence from multiomics data encoded as node attributes. We benchmarked EPGAT for four organisms, including humans, accurately predicting gene essentiality with ROC AUC score ranging from 0.78 to 0.97. Our model significantly outperformed network-based and shallow ML-based methods and achieved a very competitive performance against the state-of-the-art node2vec embedding method. Notably, EPGAT was the most robust approach in scenarios with limited and imbalanced training data. Thus, the proposed approach offers a powerful and effective way to identify essential genes and proteins.

Data stream classification poses many challenges for the data mining community when the environment is non-stationary, among which adaptation to the concept drifts, i.e., changes in the underlying concepts, is a major one. Two main ways to develop adaptive approaches are ensemble methods and incremental algorithms. Ensemble methods play an important role due to its modularity, which provides a natural way of adapting to change. Incremental algorithms are faster and have better anti-noise capacity than ensemble algorithms, but have more restrictions on concept drifting data streams. Thus, it is a challenge to combine the flexibility and adaptation of an ensemble classifier in the presence of concept drift, with the simplicity of use found in a single classifier with incremental learning. With this motivation, this work proposes an incremental, online, and probabilistic algorithm for classification as an effort of tackling concept drifting in data streams. The algorithm is called Incremental Gaussian Mixture Network for Non-Stationary Environments (IGMN-NSE) and is an adaptation of the IGMN algorithm. The two main contributions of IGMN-NSE in relation to the IGMN are predictive power improvement for classification tasks and adaptation to achieve a good performance in non-stationary environments. Extensive experiments with both synthetic and real-world data demonstrate that the IGMN-NSE can track the changing environments very closely, regardless of the type of concept drift.

The discovery of gene regulatory networks is a major goal in the field of bioinformatics due to their relevance, for instance, in the development of new drugs and medical treatments. The idea underneath this task is to recover gene interactions in a global and simple way, identifying the most significant connections and thereby generating a model to depict the mechanisms and dynamics of gene expression and regulation. In the present paper we tackle this challenge by applying a genetic algorithm to Boolean-based networks whose structures are inferred through the optimization of a Tsallis entropy function, which has been already successfully used in the inference of gene networks with other search schemes. Additionally, wisdom of crowds is applied to create a consensus network from the information contained within the last generation of the genetic algorithm. Results show that the proposed method is a promising approach and that the combination of criterion function based on Tsallis entropy with an heuristic search such as genetic algorithms yields networks up to 50% more accurate when compared to other Boolean-based approaches.

The discovery of the structure of genetic regulatory networks is of great interest for biologists and geneticists due to its pivotal role in organisms' metabolism. In the present paper we aim to investigate the inference power of genetic regulatory networks modeled as random boolean networks without the use of any prior biological information. The solutions space is explored by means of genetic algorithms, whose main goal is to find a consistent network given the target data obtained from biological experiments. We show that this approach succeeds in reconstructing a model with satisfactory level of accuracy, representing an useful tool to guide biologist towards the most probable interactions between the target genes.