Background: Gliomas are the most common malignant brain tumors in adults, characterized by a poor prognosis. Although the current World Health Organization (WHO) classification provides clear guidelines for classifying oligodendroglioma, astrocytoma, and glioblastoma patients, significant heterogeneity persists within each class, limiting the effectiveness of current treatment strategies. With the increasing availability of large-scale multi-omics datasets resulting from advancements in sequencing technologies and online repositories that provide them, such as The Cancer Genome Atlas (TCGA), it is now possible to investigate these tumors at multiple molecular levels. Methods: In this work, we apply integrative multi-omics analysis to explore the interplay between genomic (mutations), epigenomic (DNA methylation), and transcriptomic (mRNA and miRNA) layers. Our approach relies on Multi-Omics Factor Analysis (MOFA), a Bayesian latent factor analysis model designed to capture sources of variation across different omics types. Results: Our results highlight distinct molecular profiles across the three glioma types and identify potential relationships between methylation and genetic expression. In particular, we uncover novel candidate biomarkers associated with survival as well as a transcriptional profile associated with neural system development. Conclusions: These findings may contribute to more personalized therapeutic strategies, potentially improving treatment effectiveness and survival outcomes in this disease.

Gliomas represent the most prevalent type of brain tumor, with their most aggressive variant, glioblastoma multiforme, associated with high mortality rates. Due to their elevated molecular heterogeneity, accurate classification of gliomas has presented significant challenges. Therefore, considerable effort has been dedicated to identifying relevant biomarkers that improve early diagnosis and unveil new areas for treatment. Advances in high-throughput sequencing technology have enabled public resources such as The Cancer Genome Atlas (TCGA) to provide large-scale data from various cancers, allowing researchers to perform more comprehensive analysis of this disease. In this study, we introduce MOHVAE-B, a comprehensive framework designed for the integration of multi-omics data and biomarker discovery using data from TCGA. MOHVAE-B employs a supervised hierarchical variational autoencoder integrated with SHAP-based interpretability to effectively integrate high-dimensional multi-omics data and extract the most influential features driving the model’s predictions. Subsequently, Bayesian Networks (BNs) are constructed to model conditional dependencies between the selected features, providing insights into their possible relations. Applied to the TCGA glioma cohorts, MOHVAE-B achieved a near-perfect AUC of 0.9993 and successfully identified high-impact features related to glioma classification. For glioblastoma multiforme, this included six novel candidates: LINC02172, NACA2, LINC01114, HNRNPA1P48, PPIAL4G, and LINC01558. For low-grade gliomas, the model highlighted AMER2 as a promising marker. Across both cohorts, PMP2 stood out as a particularly strong candidate for a potential role in glioma pathogenesis. The constructed BNs provided an additional layer of validation, reinforcing NACA2 as a candidate of interest in glioma biology.

This study investigated the added value of using maximum-intensity projection (MIP) images for fully automatic segmentation of lesions using deep learning (DL) in [18F]FDG and [68Ga]Ga-prostate-specific membrane antigen (PSMA) PET/CT scans. Methods: We used 489 staging [18F]FDG PET/CT scans from patients diagnosed with melanoma, lymphoma, or lung cancer (391 scans for training and 98 for internal testing). As an external test set, 117 staging [18F]FDG PET/CT scans from lymphoma patients (another center, 2 scanners) were used. For [68Ga]Ga-PSMA, 355 whole-body [68Ga]Ga-PSMA PET/CT scans from patients with prostate cancer were used (285 scans for training and 70 scans for testing). All scans had corresponding expert-based segmentation (ground truth). Three approaches per radiopharmaceutical were used for fully automatic segmentation: 3-dimensional U-Net applied directly on PET images (standard-DL-based), 3-dimensional U-Net applied on multiangle MIP images (MIP-DL-based), and a combined approach (standard-DL+MIP-DL-based). The performance was evaluated in comparison with ground truth segmentation through lesion detection scores, voxelwise segmentation overlap metrics, and quantification of clinically relevant imaging features. Results: For [18F]FDG PET scans, the MIP-DL-based method showed a lower lesion false-discovery rate than did the standard-DL-based approach, although not significant in internal and external test sets. Sensitivity in lesion detection did not vary significantly, and a reduction in voxelwise metrics was observed (median Dice coefficient of 0.65 vs. 0.80 in the internal test set). Significantly increased performance was obtained with the combined approach in both test sets. In the internal test set, the median false-discovery rate was 0% (12% using the standard-DL), and a considerable increase in the agreement of lesion features was observed (intraclass correlation coefficient range, 0.42-0.94 for standard-DL-based and 0.80-0.94 for the combined approach). Similar results were observed in the external set. Regarding [68Ga]Ga-PSMA scans, there was no significant increase in the performance of MIP-DL-based and combined approaches compared with standard-DL, which was already outstanding in lesion detectability. Conclusion: Fully automatic segmentation of lesions in whole-body or total-body [18F]FDG PET/CT scans may benefit from the addition of the MIP-DL-based segmentation compared with the standard-DL-based method. It reduces the number of false-positive lesions and improves the patients' tumor burden quantification. In [68Ga]Ga-PSMA PET/CT scans, no benefits were observed compared with standard-DL-based segmentation.

The understanding of glioma disease has significantly advanced through the application of genetic and molecular profiling techniques on brain tumour tissue. Molecular biomarkers have gained a crucial role in glioma diagnosis, driving groundbreaking changes in the disease classification as standardised by the 2016 and 2021 World Health Organisation (WHO) Classification of Tumours of the Central Nervous System. Recent insights from large-scale multi-omics databases, such as The Cancer Genome Atlas (TCGA), have enriched our comprehension of this cancer type. However, given the evolution of glioma classification, retrospective databases may contain outdated annotations, suboptimal for research. To address this issue, we propose two methods for updating the tumor classification of TCGA glioma samples according to the 2016 and 2021 WHO guidelines, through the integration of open-access curated molecular profiling data. Respectively, our Method-2016 and Method-2021 allowed for the diagnostic update of 98% and 87% of cases. The proposed reclassification pipelines, provided in R scripts, enable straightforward reproduction or customisation upon new WHO guideline releases.

Motivation: With the advent of Language Models using Transformers, popularized by ChatGPT, there is a renewed interest in exploring encoding procedures that numerically represent symbolic sequences at multiple scales and embedding dimensions. The challenge that encoding addresses is the need for mechanisms that uniquely retain contextual information about the succession of individual symbols, which can then be modeled by nonlinear formulations such as neural networks. Context: Universal Sequence Maps(USM) are iterated functions that bijectively encode symbolic sequences onto embedded numerical spaces. USM is composed of two Chaos Game Representations (CGR), iterated forwardly and backwardly, that can be projected into the frequency domain (FCGR). The corresponding USM coordinates can be used to compute a Chebyshev distance metric as well as k-mer frequencies, without having to recompute the embedded numeric coordinates, and, paradoxically, allowing for non-integers values of k. Results: This report advances the bijective fractal encoding by Universal Sequence Maps (USM) by resolving seeding biases affecting the iterated process. The resolution had two results, the first expected, the second an intriguing outcome: 1) full reconciliation of numeric positioning with sequence identity; and 2) uncovering the nature of USM as an efficient numeric process converging towards a steady state sequence embedding solution. We illustrate these results for genomic sequences because of the convenience of a planar representation defined by an alphabet with only 4 tokens (the 4 nucleotides). Nevertheless, the application to alphabet of arbitrary cardinality was found to be straightforward.

Background: Glioblastoma is a highly aggressive brain tumour with poor survival outcomes, highlighting the need for reliable prognostic models. Developing robust and interpretable prognostic signatures is critical for improving patient stratification and guiding therapy. This study explored the integration of machine learning feature selection with regularised Cox regression to construct prognostic gene signatures for glioblastoma patients. Methods: We combined the Boruta algorithm and Random Survival Forests (RSFs) with regularised Cox regression, along with network-based regularisation techniques (HubCox and OrphanCox), to develop interpretable prognostic signatures for stratifying high- and low-risk glioblastoma patients. Using mRNA-seq and survival data from The Cancer Genome Atlas (TCGA), we developed predictive models following WHO-2021 glioma guidelines. Results: Integrating Boruta or RSF with regularised Cox regression improved the performance and interpretability. Boruta increased the concordance indexes (C-indexes) by 0.030 and 0.013 for LASSO and Elastic Net, respectively, while significantly reducing the feature numbers. RSF similarly enhanced the performance and feature reduction. The genes Lysyl Oxidase Like 1 (LOXL1) and Insulin Like Growth Factor Binding Protein 6 (IGFBP6) were consistently selected and linked to glioma survival, emphasising their clinical significance. The network-based methods demonstrated superior survival probability prediction (lower Integrated Brier Score), although with lower C-index values, highlighting limitations in ranking the survival times. To evaluate the generalisability, external validation using the Chinese Glioma Genome Atlas (CGGA) confirmed that a multigene signature derived from the most consistently selected genes significantly stratified the patients by risk. Conclusions: This study underscored the utility of combining machine learning feature selection with survival analysis to enhance prognostic modelling while balancing predictive performance and interpretability.

Tumor heterogeneity is a challenge to designing effective and targeted therapies. Glioma-type identification depends on specific molecular and histological features, which are defined by the official World Health Organization (WHO) classification of the central nervous system (CNS). These guidelines are constantly updated to support the diagnosis process, which affects all the successive clinical decisions. In this context, the search for new potential diagnostic and prognostic targets, characteristic of each glioma type, is crucial to support the development of novel therapies. Based on The Cancer Genome Atlas (TCGA) glioma RNA-sequencing data set updated according to the 2016 and 2021 WHO guidelines, we proposed a 2-step variable selection approach for biomarker discovery. Our framework encompasses the graphical lasso algorithm to estimate sparse networks of genes carrying diagnostic information. These networks are then used as input for regularized Cox survival regression model, allowing the identification of a smaller subset of genes with prognostic value. In each step, the results derived from the 2016 and 2021 classes were discussed and compared. For both WHO glioma classifications, our analysis identifies potential biomarkers, characteristic of each glioma type. Yet, better results were obtained for the WHO CNS classification in 2021, thereby supporting recent efforts to include molecular data on glioma classification.

Shellfish farming and harvesting have experienced a surge in popularity in recent years. However, the presence of fecal bacteria can contaminate shellfish, posing a risk to human health. This can result in the reclassification of shellfish production areas or even prohibit harvesting, leading to significant economic losses. Therefore, it is crucial to establish effective strategies for predicting contamination of shellfish by the bacteria Escherichia coli (E. coli). In this study, various univariate and multivariate time series forecasting models were investigated to address this problem. These models include autoregressive integrated moving average (ARIMA), vector autoregressive (VAR), and long short-term memory (LSTM) networks. The data used for this study consisted of measurements of both E. coli concentrations and meteorological variables, which were obtained from the Portuguese Institute of Sea and Atmosphere (IPMA) for four shellfish production areas. Overall, the ARIMA models performed the best with the lowest root mean squared error (RMSE) compared to the other models tested. The ARIMA models were able to accurately predict the concentrations of E. coli one week in advance. Additionally, the models were able to detect the peaks of E. coli for all areas, except for one, with recall values ranging from 0.75 to 1. This work represents the initial steps in the search for candidate forecasting models to help the shellfish production sector in anticipating harvesting prohibitions and hence supporting management and regulation decisions.

Colorectal cancer (CRC) is the third most common cancer and the second most deathly worldwide. It is a very heterogeneous disease that can develop via distinct pathways where metastasis is the primary cause of death. Therefore, it is crucial to understand the molecular mechanisms underlying metastasis. RNA-sequencing is an essential tool used for studying the transcriptional landscape. However, the high-dimensionality of gene expression data makes selecting novel metastatic biomarkers problematic. To distinguish early-stage CRC patients at risk of developing metastasis from those that are not, three types of binary classification approaches were used: (1) classification methods (decision trees, linear and radial kernel support vector machines, logistic regression, and random forest) using differentially expressed genes (DEGs) as input features; (2) regularized logistic regression based on the Elastic Net penalty and the proposed iTwiner-a network-based regularizer accounting for gene correlation information; and (3) classification methods based on the genes pre-selected using regularized logistic regression. Classifiers using the DEGs as features showed similar results, with random forest showing the highest accuracy. Using regularized logistic regression on the full dataset yielded no improvement in the methods' accuracy. Further classification using the pre-selected genes found by different penalty factors, instead of the DEGs, significantly improved the accuracy of the binary classifiers. Moreover, the use of network-based correlation information (iTwiner) for gene selection produced the best classification results and the identification of more stable and robust gene sets. Some are known to be tumor suppressor genes (OPCML-IT2), to be related to resistance to cancer therapies (RAC1P3), or to be involved in several cancer processes such as genome stability (XRCC6P2), tumor growth and metastasis (MIR602) and regulation of gene transcription (NME2P2). We show that the classification of CRC patients based on pre-selected features by regularized logistic regression is a valuable alternative to using DEGs, significantly increasing the models' predictive performance. Moreover, the use of correlation-based penalization for biomarker selection stands as a promising strategy for predicting patients' groups based on RNA-seq data.

In the healthcare sector, resorting to big data and advanced analytics is a great advantage when dealing with complex groups of patients in terms of comorbidities, representing a significant step towards personalized targeting. In this work, we focus on understanding key features and clinical pathways of patients with multimorbidity suffering from Dementia. This disease can result from many heterogeneous factors, potentially becoming more prevalent as the population ages. We present a set of methods that allow us to identify medical appointment patterns within a cohort of 1924 patients followed from January 2007 to August 2021 in Hospital da Luz (Lisbon), and to stratify patients into subgroups that exhibit similar patterns of interaction. With Markov Chains, we are able to identify the most prevailing medical appointments attended by Dementia patients, as well as recurring transitions between these. To perform patient stratification, we applied AliClu, a temporal sequence alignment algorithm for clustering longitudinal clinical data, which allowed us to successfully identify patient subgroups with similar medical appointment activity. A feature analysis per cluster obtained allows the identification of distinct patterns and characteristics. This pipeline provides a tool to identify prevailing clinical pathways of medical appointments within the dataset, as well as the most common transitions between medical specialities within Dementia patients. This methodology, alongside demographic and clinical data, has the potential to provide early signalling of the most likely clinical pathways and serve as a support tool for health providers in deciding the best course of treatment, considering a patient as a whole.

The bone is an organ in constant change. There are two main cells involved in the bone remodelling process: the osteoclasts and osteoblasts, responsible for the resorption and formation of bone, respectively. When tumour cells are present in the bone environment, they interfere with the bone remodelling process by promoting osteoclasts’ activity and causing the release of growth factors favorable to the survival and proliferation of tumour cells. This article presents differential equation models with a fractional order spatial derivative that include the emergence of drug resistance and the effect of therapy, taking the drug’s PK/PD into account. Fractional orders of differentiation, corresponding to anomalous diffusion, are considered in all models, and random mutations are included in a model where they were previously not considered. Simulations of the models are performed using a numerical approach.

The understanding of glioma disease has been evolving drastically with dedicated research into the genetic and molecular profiling of glioma tumour tissue. Molecular biomarkers have gained progressive and substantial importance in providing diagnostic information, leading to groundbreaking changes in the tumour classification system, criteria and taxonomy standardised by the 2016 and 2021 editions of the World Health Organization Classification of Tumours of the Central Nervous System’s guidelines (WHO-2016 and WHO-2021, respectively). Some of the insights into glioma disease derived from extensive research on open-source multi-omics databases, such as the Cancer Genome Atlas (TCGA). However, given the substantial changes in glioma classification, retrospective databases may harbour outdated diagnostic annotations, suboptimal for further research. Here we propose two methods for updating the tumour classification of TCGA glioma samples in accordance with WHO-2016 and WHO-2021 guidelines through the integration of curated molecular profiling information. Our methods allowed for the diagnostic update of 98% and 87% of evaluated TCGA glioma cases according to WHO-2016 and -2021, respectively, and highlighted changes in patient-specific diagnosis across both guidelines’ editions. Our reclassification pipelines are provided in software R, facilitating direct reproduction or tailoring upon new releases of WHO guidelines.### Competing Interest StatementThe authors have declared no competing interest.

Harmful algal blooms are natural phenomena that cause shellfish contamination due to the rapid accumulation of marine biotoxins. To prevent public health risks, the Portuguese Institute of the Ocean and the Atmosphere (IPMA) regularly monitors toxic phytoplankton in shellfish production areas and temporarily closes shellfish production when biotoxins concentration exceeds safety limits. However, this reactive response does not allow shellfish producers to anticipate toxic events and reduce economic losses. Causality techniques applied to multivariate time series data can identify the variables that most influence marine biotoxin contamination and, based on these causal relationships, can help forecast shellfish contamination, providing a proactive approach to mitigate economic losses. This study used causality discovery algorithms to analyze biotoxin concentration in mussels Mytilus galloprovincialis and environmental data from IPMA and Copernicus Marine Environment Monitoring Service. We concluded that the toxins that cause diarrhetic and paralytic shellfish poisoning had more predictors than the toxins that cause amnesic poisoning. Moreover, maximum atmospheric temperature, DSP toxins-producing phytoplankton and wind intensity showed causal relationships with toxicity in mussels with shorter lags, while chlorophyll a (chl-a), mean sea surface temperature and rainfall showed causal associations over longer periods. Causal relationships were also found between toxins in nearby production areas, indicating a spread of biotoxins contamination. This study proposes a novel approach to infer the relationships between environmental variables to enhance decision-making and public health safety regarding shellfish consumption in Portugal.

Shellfish accumulation of marine biotoxins at levels unsafe for human consumption may severely impact their harvesting and farming, which has been grown worldwide in response to the growing demand for nutritious food and protein sources. In Southern European countries, diarrhetic shellfish poisoning (DSP) toxins are the most abundant and frequent toxins derived from algal blooms, affecting shellfish production yearly. Therefore, it is essential to understand the natural phenomenon of DSP toxins accumulation in shellfish and the meteorological and biological parameters that may regulate and influence its occurrence. In this work, we studied the relationship between the time series of several meteorological and biological variables and the time series of the concentration of DSP toxins in mussels on the Portuguese coast, using the Pearson’s correlation coefficient, time series regression modeling, Granger causality, and dynamic Bayesian networks using the MAESTRO tool. The results show that, for the models tested, the mean sea surface and air temperature time series with a one, two, or three-week lag can be valuable candidate predictors for forecasting the DSP concentration in mussels. Overall, this proof-of-concept study emphasizes the importance of statistical learning methodologies for analyzing time series environmental data and illustrates the importance of several variables in predicting DSP biotoxins concentration, which can help the shellfish production sector mitigate the negative impacts of DSP biotoxins accumulation.

A short introduction to the application of informationtheoretic and machine learning methods to biomolecular and medical data is provided as the motivating material that supports special session dedicated to this topic at ESANN 2022.In particular, we highlight current developments of foundation such as interpretability and model certainty.Further, we emphasize how theoretic models provide a natural framework to deal with heterogeneous and complex data structures as frequently occurring in biomedical research.

Diarrhetic Shellfish Poisoning (DSP) is an acute intoxication caused by the consumption of contaminated shellfish, which is common in many regions of the world. To safeguard human health, most countries implement programs focused on the surveillance of toxic phytoplankton abundance and shellfish toxicity levels, an effort that can be complemented by a deeper understanding of the underlying phenomena. In this work, we identify patterns of seasonality in shellfish toxicity across the Portuguese coast and analyse time-lagged correlations between this toxicity and various potential risk factors. We extend the understanding of these relations through the introduction of temporal lags, allowing the analysis of time series at different points in time and the study of the predictive power of the tested variables. This study confirms previous findings about toxicity seasonality patterns on the Portuguese coast and provides further quantitative data about the relations between shellfish toxicity and geographical location, shellfish species, toxic phytoplankton abundances, and environmental conditions. Furthermore, multiple pairs of areas and shellfish species are identified as having correlations high enough to allow for a predictive analysis. These results represent the first step towards understanding the dynamics of DSP toxicity in Portuguese shellfish producing areas, such as temporal and spatial variability, and towards the development of a shellfish safety forecasting system.