A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases

Machine learning has provided, over the last decades, tools for knowledge extraction in complex medical domains. Most of these tools, though, are ad hoc solutions and lack the systematic approach that would be required to become mainstream in medical practice. In this brief paper, we define a machine learning-based analysis pipeline for helping in a difficult problem in the field of neuro-oncology, namely the discrimination of brain glioblastomas from single brain metastases. This pipeline involves source extraction using k-Meansinitialized Convex Non-negative Matrix Factorization and a collection of classifiers, including Logistic Regression, Linear Discriminant Analysis, AdaBoost, and Random Forests.Peer ReviewedPostprint (published version

Detecting adversarial examples with inductive Venn-ABERS predictors

Inductive Venn-ABERS predictors (IVAPs) are a type of probabilistic predictors with the theoretical guarantee that their predictions are perfectly calibrated. We propose to exploit this calibration property for the detection of adversarial examples in binary classification tasks. By rejecting predictions if the uncertainty of the IVAP is too high, we obtain an algorithm that is both accurate on the original test set and significantly more robust to adversarial examples. The method appears to be competitive to the state of the art in adversarial defense, both in terms of robustness as well as scalabilit

Bayesian semi non-negative matrix factorisation

Non-negative Matrix Factorisation (NMF) has become a standard method for source identification when data, sources and mixing coefficients are constrained to be positive-valued. The method has recently been extended to allow for negative-valued data and sources in the form of Semi-and Convex-NMF. In this paper, we re-elaborate Semi-NMF within a full Bayesian framework. This provides solid foundations for parameter estimation and, importantly, a principled method to address the problem of choosing the most adequate number of sources to describe the observed data. The proposed Bayesian Semi-NMF is preliminarily evaluated here in a real neuro-oncology problem.Peer ReviewedPostprint (published version

Bioinformatics and Medicine in the Era of Deep Learning

Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by technological breakthroughs in data acquisition technologies. It has been argued that bioinformatics could quickly become the field of research generating the largest data repositories, beating other data-intensive areas such as high-energy physics or astroinformatics. Over the last decade, deep learning has become a disruptive advance in machine learning, giving new live to the long-standing connectionist paradigm in artificial intelligence. Deep learning methods are ideally suited to large-scale data and, therefore, they should be ideally suited to knowledge discovery in bioinformatics and biomedicine at large. In this brief paper, we review key aspects of the application of deep learning in bioinformatics and medicine, drawing from the themes covered by the contributions to an ESANN 2018 special session devoted to this topic

A Quotient Basis Kernel for the prediction of mortality in severe sepsis patients

In this paper, we describe a novel kernel for multinomial distributions, namely the Quotient Basis Kernel (QBK), which is based on a suitable reparametrization of the input space through algebraic geometry and statistics. The QBK is used here for data transformation prior to classification in a medical problem concerning the prediction of mortality in patients suffering severe sepsis. This is a common clinical syndrome, often treated at the Intensive Care Unit (ICU) in a time-critical context. Mortality prediction results with Support Vector Machines using QBK compare favorably with those obtained using alternative kernels and standard clinical procedures.Postprint (published version

Perceptual Grouping through Competition in Coupled Oscillator Networks

Meier M, Haschke R, Ritter H. Perceptual Grouping through Competition in Coupled Oscillator Networks. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). Bruges (Belgium): d-side; 2013.In this paper we present a novel approach to model perceptual grouping based on phase and frequency synchronization in a network of coupled Kuramoto oscillators. Transferring the grouping concept from the Competitive Layer Model (CLM) to a network of Kuramoto oscillators, we preserve the excellent grouping capabilities of the CLM, while dramatically improving the convergence rate, robustness to noise, and computational performance, which is verified in a series of artificial grouping experiments

Misclassification of class C G-protein-coupled receptors as a label noise problem

G-Protein-Coupled Receptors (GPCRs) are cell membrane proteins of relevance to biology and pharmacology. Their supervised classification in subtypes is hampered by label noise, which stems from a combination of expert knowledge limitations and lack of clear correspondence between labels and different representations of the protein primary sequences. In this brief study, we describe a systematic approach to the analysis of GPCR misclassifications using Support Vector Machines and use it to assist the discovery of database labeling quality problems and investigate the extent to which GPCR sequence physicochemical transformations reflect GPCR subtype labeling. The proposed approach could enable a filtering approach to the label noise problem.Peer ReviewedPostprint (published version

Societal issues in machine learning: when learning from data is not enough

It has been argued that Artificial Intelligence (AI) is experiencing a fast process of commodification. Such characterization is on the interest of big IT companies, but it correctly reflects the current industrialization of AI. This phenomenon means that AI systems and products are reaching the society at large and, therefore, that societal issues related to the use of AI and Machine Learning (ML) cannot be ignored any longer. Designing ML models from this human-centered perspective means incorporating human-relevant requirements such as safety, fairness, privacy, and interpretability, but also considering broad societal issues such as ethics and legislation. These are essential aspects to foster the acceptance of ML-based technologies, as well as to ensure compliance with an evolving legislation concerning the impact of digital technologies on ethically and privacy sensitive matters. The ESANN special session for which this tutorial acts as an introduction aims to showcase the state of the art on these increasingly relevant topics among ML theoreticians and practitioners. For this purpose, we welcomed both solid contributions and preliminary relevant results showing the potential, the limitations and the challenges of new ideas, as well as refinements, or hybridizations among the different fields of research, ML and related approaches in facing real-world problems involving societal issues.Peer ReviewedPostprint (published version

Robust cartogram visualization of outliers in manifold learning

Most real data sets contain atypical observations, often referred to as outliers. Their presence may have a negative impact in data modeling using machine learning. This is particularly the case in data density estimation approaches. Manifold learning techniques provide low-dimensional data representations, often oriented towards visualization. The visualization provided by density estimation manifold learning methods can be compromised by the presence of outliers. Recently, a cartogram-based representation of model-generated distortion was presented for nonlinear dimensionality reduction. Here, we investigate the impact of outliers on this visualization when using manifold learning techniques that behave robustly in their presence.Postprint (published version

Bioinformatics and Medicine in the Era of Deep Learning

Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by technological breakthroughs in data acquisition technologies. It has been argued that bioinformatics could quickly become the field of research generating the largest data repositories, beating other data-intensive areas such as high-energy physics or astroinformatics. Over the last decade, deep learning has become a disruptive advance in machine learning, giving new live to the long-standing connectionist paradigm in artificial intelligence. Deep learning methods are ideally suited to large-scale data and, therefore, they should be ideally suited to knowledge discovery in bioinformatics and biomedicine at large. In this brief paper, we review key aspects of the application of deep learning in bioinformatics and medicine, drawing from the themes covered by the contributions to an ESANN 2018 special session devoted to this topic