A Boolean Approach to Linear Prediction for Signaling Network Modeling

The task of the DREAM4 (Dialogue for Reverse Engineering Assessments and Methods) “Predictive signaling network modeling” challenge was to develop a method that, from single-stimulus/inhibitor data, reconstructs a cause-effect network to be used to predict the protein activity level in multi-stimulus/inhibitor experimental conditions. The method presented in this paper, one of the best performing in this challenge, consists of 3 steps: 1. Boolean tables are inferred from single-stimulus/inhibitor data to classify whether a particular combination of stimulus and inhibitor is affecting the protein. 2. A cause-effect network is reconstructed starting from these tables. 3. Training data are linearly combined according to rules inferred from the reconstructed network. This method, although simple, permits one to achieve a good performance providing reasonable predictions based on a reconstructed network compatible with knowledge from the literature. It can be potentially used to predict how signaling pathways are affected by different ligands and how this response is altered by diseases

Reverse Engineering Gene Networks with ANN: Variability in Network Inference Algorithms

Motivation :Reconstructing the topology of a gene regulatory network is one of the key tasks in systems biology. Despite of the wide variety of proposed methods, very little work has been dedicated to the assessment of their stability properties. Here we present a methodical comparison of the performance of a novel method (RegnANN) for gene network inference based on multilayer perceptrons with three reference algorithms (ARACNE, CLR, KELLER), focussing our analysis on the prediction variability induced by both the network intrinsic structure and the available data. Results: The extensive evaluation on both synthetic data and a selection of gene modules of "Escherichia coli" indicates that all the algorithms suffer of instability and variability issues with regards to the reconstruction of the topology of the network. This instability makes objectively very hard the task of establishing which method performs best. Nevertheless, RegnANN shows MCC scores that compare very favorably with all the other inference methods tested. Availability: The software for the RegnANN inference algorithm is distributed under GPL3 and it is available at the corresponding author home page (http://mpba.fbk.eu/grimaldi/regnann-supmat

Algebraic Comparison of Partial Lists in Bioinformatics

The outcome of a functional genomics pipeline is usually a partial list of genomic features, ranked by their relevance in modelling biological phenotype in terms of a classification or regression model. Due to resampling protocols or just within a meta-analysis comparison, instead of one list it is often the case that sets of alternative feature lists (possibly of different lengths) are obtained. Here we introduce a method, based on the algebraic theory of symmetric groups, for studying the variability between lists ("list stability") in the case of lists of unequal length. We provide algorithms evaluating stability for lists embedded in the full feature set or just limited to the features occurring in the partial lists. The method is demonstrated first on synthetic data in a gene filtering task and then for finding gene profiles on a recent prostate cancer dataset

Deep Convolutional Neural Network for Survival Estimation of Amyotrophic Lateral Sclerosis patients

We propose a convolutional neural network (CNN) coupled with a fully connected top layer for survival estimation. We design an objective function to directly estimate the probability of survival at discrete time intervals, conditional to the patient not having incurred any adverse event at previous time points. We test our CNN and objective function on a large dataset of longitudinal data of patients with Amyotrophic Lateral Sclerosis (ALS). We compare our CNN and the objective function against other neural networks designed for survival analysis, and against the optimization of Cox-partial-likelihood or a simple logistic classifier. The use of our objective function outperforms both Cox-partial-likelihood and logistic classifier, independently of the network architecture, and our deep CNN provides the best results in terms of AU-ROC, accuracy and mean absolute erro

DYNAMITE: Integrating Archetypal Analysis and Process Mining for Interpretable Disease Progression Modelling

: DYNAMITE, an acronym for DYNamic Archetypal analysis for MIning disease TrajEctories, is a new methodology developed specifically to model disease progression by exploiting information available in longitudinal clinical datasets. First, archetypal analysis is applied to data organised in matrix form, with the aim of finding extreme and representative disease states (archetypes) linked to the original data through convex coefficients. Then, each original observation is associated with a single archetype based on their similarity; finally, an event log is created encoding the progression of disease states for each patient in terms of archetype states. In the last stage of the procedure, archetypal analysis is coupled with process mining, which allows the event log archetypes to be visualised graphically as sequences of disease states, allowing the clinical trajectories of patients to be extracted and examined. As a proof of concept, we applied the proposed method to data from a cohort of amyotrophic lateral sclerosis patients whose progression was monitored using the 12-item ALSFRS-R questionnaire. Without any a priori knowledge, DYNAMITE identified six archetypes clearly describing different types and severity of impairment and provided reliable clinical trajectories consistent with the prognosis of amyotrophic lateral sclerosis patients. DYNAMITE offers high interpretability at every stage of the analysis, which makes it particularly suitable for use in healthcare where explainability is paramount, and enables analysis of clinical trajectories at both individual and population levels

The transition from restrictive anorexia nervosa to binging and purging: a systematic review and meta-analysis.

Abstract: Numerous studies addressed the topic of behavioral and symptomatic changes in eating disorders. Rates of transition vary widely across studies, ranging from 0 to 70.8%, depending on the diagnoses taken into account and the study design. Evidence shows that the specific transition from restrictive-type anorexia nervosa (AN-R) to disorders involving binging and purging behaviors (BPB) is related to a worsening of the clinical picture and worse long-term outcomes. The aim of this systematic review and meta-analysis is to focus on this specific transition, review existing literature, and summarize related risk factors. Medline and PsycINFO databases were searched, including prospective and retrospective studies on individuals with AN-R. The primary outcome considered was the rate of onset of BPB. Twelve studies (N = 725 patients) were included in the qualitative and quantitative analysis. A total of 41.84% (95% CI 33.58–50.11) of patients with AN-R manifested BPB at some point during follow-up. Risk factors for the onset of BPB included potentially treatable and untreatable factors such as the family environment, unipolar depression and higher premorbid BMI. These findings highlight that patients with AN-R frequently transition to BPB over time, with a worsening of the clinical picture. Existing studies in this field are still insufficient and heterogeneous, and further research is needed. Mental health professionals should be aware of the frequent onset of BPB in AN-R and its risk factors and take this information into account in the treatment of AN-R. Level of evidence: Evidence obtained from a systematic review and meta-analysis, Level I