Surfing the OCEAN: The machine learning psycholexical approach 2.0 to detect personality traits in texts.

Objective: We aimed to develop a machine learning model to infer OCEAN traits from text.

Background: The psycholexical approach allows retrieving information about personality traits from human language. However, it has rarely been applied because of methodological and practical issues that current computational advancements could overcome.

Edge and modular significance assessment in individual-specific networks.

Individual-specific networks, defined as networks of nodes and connecting edges that are specific to an individual, are promising tools for precision medicine. When such networks are biological, interpretation of functional modules at an individual level becomes possible. An under-investigated problem is relevance or "significance" assessment of each individual-specific network.

Non Stationary Multi-Armed Bandit: Empirical Evaluation of a New Concept Drift-Aware Algorithm.

The Multi-Armed Bandit (MAB) problem has been extensively studied in order to address real-world challenges related to sequential decision making. In this setting, an agent selects the best action to be performed at time-step , based on the past rewards received by the environment. This formulation implicitly assumes that the expected payoff for each action is kept stationary by the environment through time.

Modeling Approaches Reveal New Regulatory Networks in Metabolism.

Systems biology approaches are extensively used to model and reverse-engineer gene regulatory networks from experimental data. Indoleamine 2,3-dioxygenases (IDOs)-belonging in the heme dioxygenase family-degrade l-tryptophan to kynurenines. These enzymes are also responsible for the de novo synthesis of nicotinamide adenine dinucleotide (NAD+).

A comparison between discrete and continuous time Bayesian networks in learning from clinical time series data with irregularity.

Background: Recently, mobile devices, such as smartphones, have been introduced into healthcare research to substitute paper diaries as data-collection tools in the home environment. Such devices support collecting patient data at different time points over a long period, resulting in clinical time-series data with high temporal complexity, such as time irregularities. Analysis of such time series poses new challenges for machine-learning techniques.

Continuous time Bayesian networks identify Prdm1 as a negative regulator of TH17 cell differentiation in humans.

T helper 17 (TH17) cells represent a pivotal adaptive cell subset involved in multiple immune disorders in mammalian species. Deciphering the molecular interactions regulating TH17 cell differentiation is particularly critical for novel drug target discovery designed to control maladaptive inflammatory conditions. Using continuous time Bayesian networks over a time-course gene expression dataset, we inferred the global regulatory network controlling TH17 differentiation.

Gene network inference using continuous time Bayesian networks: a comparative study and application to Th17 cell differentiation.

Background: Dynamic aspects of gene regulatory networks are typically investigated by measuring system variables at multiple time points. Current state-of-the-art computational approaches for reconstructing gene networks directly build on such data, making a strong assumption that the system evolves in a synchronous fashion at fixed points in time. However, nowadays omics data are being generated with increasing time course granularity.

Classification of dendritic cell phenotypes from gene expression data.

Background: The selection of relevant genes for sample classification is a common task in many gene expression studies. Although a number of tools have been developed to identify optimal gene expression signatures, they often generate gene lists that are too long to be exploited clinically. Consequently, researchers in the field try to identify the smallest set of genes that provide good sample classification.

[Does free assumption of water from one hour after extubation reduce thirst without increasing complications in heart surgery patients?].

Object: The aim of this study was to assess whether free assumption of water in heart surgery patients, as early as one hour after extubation, produces measurable differences in thirst, nausea and vomiting.

Methods: Randomized controlled trial (pilot phase), by sex and age. Eventual cases of dysphagia are identified by both a functional examination and a water test.

Conformational and functional analysis of molecular dynamics trajectories by self-organising maps.

Background: Molecular dynamics (MD) simulations are powerful tools to investigate the conformational dynamics of proteins that is often a critical element of their function. Identification of functionally relevant conformations is generally done clustering the large ensemble of structures that are generated. Recently, Self-Organising Maps (SOMs) were reported performing more accurately and providing more consistent results than traditional clustering algorithms in various data mining problems.

Some numerical aspects of the training problem for feed-forward neural nets.

This paper considers the feed-forward training problem from the numerical point of view, in particular the conditioning of the problem. It is well known that the feed-forward training problem is often ill-conditioned; this affects the behaviour of training algorithms, the choice of such algorithms and the quality of the solutions achieved. A geometric interpretation of ill-conditioning is explored and an example of function approximation is analysed in detail.