Reliability, safety, and robustness are essential requirements for safety critical applications.Implementing these properties in artificial intelligence based systems introduces additional challenges, particularly in the development and validation of data driven models.To address these requirements, new techniques are needed for assessing predictive uncertainty, ensuring robustness against intentional or environmental input perturbations, integrating explicit safety constraints into model architectures, and enabling human oversight and interpretability to support auditing and supervision.

Diabetic retinopathy (DR) is a severe complication of diabetes mellitus (DM) that produces damage to the retinal blood vessels. It can thus be detected from retinal images. In this work, we address the problem of DR machine learning-based prediction from clinical records and retinal images that suffer from an underrepresentation of the DR cases in the clinical dataset. Several classification models are compared in the task of predicting DR in DM type 1 patients, applying different class imbalance correction techniques. The results show the ability of class weighting and a combined undersampling and oversampling approach to significantly improve the classification of DR cases, which is the class of interest in the medical application.

Mental illnesses influence the emotion recognition capabilities of those who suffer them.This article presents a study that involves the prediction, using multi-class classification models, of several human standard emotions from facial expressions.It is based on a publicly available dataset for emotion recognition that includes socio-demographic information and psychiatric profiles of individuals with mental illnesses.The study aims to explore how effectively these models can identify and classify emotions based on facial cues, considering the diverse psychiatric backgrounds of the subjects.It also aims to investigate to what extent the severity of the psychiatric condition affects the level of certainty of the predictions.

Artificial Intelligence (AI) both in general and in its current predominant version, mostly based on connectionist tenets, lives in the paradox of aiming to reproduce and simulate the workings of an immensely complex system, the biological brain, which are still to a large extent unknown.This gives us latitude for some interesting domain interplay: concepts from the cognitive sciences can be used to improve AI models, while AI can be used in data science mode to analyze cognitive processes in neuroscience, as well as brain pathologies from a medical standpoint.

Enhancing the robustness of object detection systems under adverse weather conditions is crucial for the advancement of autonomous driving technology. This study presents a novel approach leveraging the diffusion model Instruct Pix2Pix to develop prompting methodologies that generate realistic datasets with weather-based augmentations aiming to mitigate the impact of adverse weather on the perception capabilities of state-of-the-art object detection models, including Faster R-CNN and YOLOv10. Experiments were conducted in two environments, in the CARLA simulator where an initial evaluation of the proposed data augmentation was provided, and then on the real-world image data sets BDD100K and ACDC demonstrating the effectiveness of the approach in real environments.The key contributions of this work are twofold: (1) identifying and quantifying the performance gap in object detection models under challenging weather conditions, and (2) demonstrating how tailored data augmentation strategies can significantly enhance the robustness of these models. This research establishes a solid foundation for improving the reliability of perception systems in demanding environmental scenarios, and provides a pathway for future advancements in autonomous driving.

G-protein coupled receptors (GPCRs) are the most abundant and varied family of transmembrane proteins in eukaryotic cells. Beyond their basic biological functions, they are relevant to pharmacology, as they are involved in a variety of human pathologies. Molecular dynamics (MD) are a reliable and effective simulation technique that allows us to investigate and examine the structure and activity that shape biomolecular signals. In this study, Dimensionality Reduction (DR) methods are applied to GPCR 3D simulations from a publicly accessible GPCR MD database to find the most effective simplified representation of these proteins that will help us to analyze their deactivation and activation pathways. The protein representations generated by several DR methods for inactive, intermediate, and active conformational GPCR states are compared through their entropy quantification and visual representation. In addition, experiments involve three types of MD data transformations, based on the 3D position of amino acids, the distances between the helices of the molecules, and the dihedral angles representing the torsion angles Psi and Phi of each amino acid. The results of our study show how some of the DR methods under investigation capture the smooth temporal evolution of the GPCR representations and their state transitions.

The investigation of protein functionality often relies on the knowledge of crystal 3-D structure. This structure is not always known or easily unravelled, which is the case of eukaryotic cell membrane proteins such as G Protein-Coupled Receptors (GPCRs) and specially of those of class C, which are the target of the current study. In the absence of information about tertiary or quaternary structures, functionality can be investigated from the primary structure, that is, from the amino acid sequence. In previous research, we found that the different subtypes of class C GPCRs could be discriminated with a high level of accuracy from the n-gram transformation of their complete primary sequences, using a method that combined two-stage feature selection with kernel classifiers. This study aims at discovering whether subunits of the complete sequence retain such discrimination capabilities. We report experiments that show that the extracellular N-terminal domain of the receptor suffices to retain the classification accuracy of the complete sequence and that it does so using a reduced selection of n-grams whose length of up to five amino acids opens up an avenue for class C GPCR signature motif discovery.

BACKGROUND: The characterization of proteins in families and subfamilies, at different levels, entails the definition and use of class labels. When the adscription of a protein to a family is uncertain, or even wrong, this becomes an instance of what has come to be known as a label noise problem. Label noise has a potentially negative effect on any quantitative analysis of proteins that depends on label information. This study investigates class C of G protein-coupled receptors, which are cell membrane proteins of relevance both to biology in general and pharmacology in particular. Their supervised classification into different known subtypes, based on primary sequence data, is hampered by label noise. The latter may stem from a combination of expert knowledge limitations and the lack of a clear correspondence between labels that mostly reflect GPCR functionality and the different representations of the protein primary sequences. RESULTS: In this study, we describe a systematic approach, using Support Vector Machine classifiers, to the analysis of G protein-coupled receptor misclassifications. As a proof of concept, this approach is used to assist the discovery of labeling quality problems in a curated, publicly accessible database of this type of proteins. We also investigate the extent to which physico-chemical transformations of the protein sequences reflect G protein-coupled receptor subtype labeling. The candidate mislabeled cases detected with this approach are externally validated with phylogenetic trees and against further trusted sources such as the National Center for Biotechnology Information, Universal Protein Resource, European Bioinformatics Institute and Ensembl Genome Browser information repositories. CONCLUSIONS: In quantitative classification problems, class labels are often by default assumed to be correct. Label noise, though, is bound to be a pervasive problem in bioinformatics, where labels may be obtained indirectly through complex, many-step similarity modelling processes. In the case of G protein-coupled receptors, methods capable of singling out and characterizing those sequences with consistent misclassification behaviour are required to minimize this problem. A systematic, Support Vector Machine-based method has been proposed in this study for such purpose. The proposed method enables a filtering approach to the label noise problem and might become a support tool for database curators in proteomics.