Abstract Background The draft genome assemblies produced by new sequencing technologies present important challenges for automatic gene prediction pipelines, leading to less accurate models. New benchmark methods are needed evaluate the accuracy of in face incomplete assemblies, low coverage and quality, complex structures, or a lack suitable sequences evidence-based annotations. Results We describe construction benchmark, called G3PO (benchmark Gene Protein Prediction PrOgrams), designed...

Ab initio prediction of splice sites is an essential step in eukaryotic genome annotation. Recent predictors have exploited Deep Learning algorithms and reliable gene structures from model organisms. However, methods for non-model organisms are lacking.We developed Spliceator to predict a wide range species, including uses convolutional neural network trained on carefully validated data over 100 We show that achieves consistently high accuracy (89-92%) compared existing independent...

Abstract Background Recent advances in sequencing technologies have led to an explosion the number of genomes available, but accurate genome annotation remains a major challenge. The prediction protein-coding genes eukaryotic is especially problematic, due their complex exon–intron structures. Even best gene algorithms can make serious errors that will significantly affect subsequent analyses. Results We first investigated prevalence large set 176,478 proteins from ten primate proteomes...

Medical acts, such as imaging, lead to the production of various medical text reports that describe relevant findings. This induces multimodality in patient data by combining image with free-text and consequently, multimodal have become central drive research improve diagnoses. However, exploitation is problematic ecosystem analysis tools fragmented according type (images, text, genetics), task (processing, exploration) domain interest (clinical phenotype, histology). To address challenges,...

Many real-world environments are non-deterministic, thus presenting learning challenges for Anticipatory Learning Classifier Systems (ALCS). Maze problems have been widely used in ALCS literature, as they provide such environments. However, few can efficiently run mazes, having trouble to build a complete and accurate internal representation of their environment. implement Probability-Enhanced Predictions (PEP) classifiers. Those PEP permit handle non-deterministic environments, but never...

Abstract Background: The draft genome assemblies produced by new sequencing technologies present important challenges for automatic gene prediction pipelines, leading to less accurate models. New benchmark methods are needed evaluate the accuracy of in face incomplete assemblies, low coverage and quality, complex structures, or a lack suitable sequences evidence-based annotations. Results: We describe construction benchmark, called G3PO (benchmark Gene Protein Prediction PrOgrams), designed...

The complexity of biological tissue morphogenesis makes in silico simulations such system very interesting order to gain a better understanding the underlying mechanisms ruling development multicellular tissues. This is mainly due two elements: firstly, tissues comprise large amount cells; secondly, these cells exhibit complex interactions and behaviors. To address issues, we propose tools: first one virtual cell model that main mechanical structure (membrane, cytoskeleton, cortex) behaviors...

This paper describes how transfer-learning can turn a Beowulf cluster into full super-computer with supra-linear qualitative acceleration.Harmonic Analysis is used as real-world example to show the kind of result that be achieved proposed supercomputer architecture, locally exploits absolute space-time parallelism on each machine (SIMD parallelism) and loosely-coupled relative parallelization between different machines (loosely coupled MIMD).

In many real-world environments, only partial observations are provided, thus presenting challenges for Anticipatory Learning Classifier Systems (ALCS). The perceptual aliasing issue occurs when systems cannot differentiate situations that truly distinct. To tackle the issue, ALCS classifiers can be chained in order to build Behavioral Sequences. Those sequences permit deal with this but they have never been implemented within ACS2 (Anticipatory System 2), although is one of most advanced...

Abstract Background: The draft genome assemblies produced by new sequencing technologies present important challenges for automatic gene prediction pipelines, leading to less accurate models. New benchmark methods are needed evaluate the accuracy of in face incomplete assemblies, low coverage and quality, complex structures, or a lack suitable sequences evidence-based annotations. Results: We describe construction benchmark, called G3PO (benchmark Gene Protein Prediction PrOgrams), designed...

Rule Compaction of populations Learning Classifier Systems (LCS) has always been a topic interest to get more insights into the discovered underlying patterns from data or remove useless classifiers populations. However, these techniques have neither used nor adapted Anticipatory (ALCS). ALCS differ other LCS in that they build models their environments which decision policies solve learning tasks are learned. We thus propose CRACS (Compaction Rules Systems), compaction algorithm for aims...

Abstract This paper deals with simulations of real‐time interactive character behavior. The underlying idea is to take into account principles from cognitive science, in particular, the human ability anticipate and simulate world For that purpose, we propose a conceptual framework where entity possesses an autonomous simulation within simulation, which it can itself (with its own model behavior) environment abstract representation, be learnt, other entities behaviors). principle illustrated...

The computationally expensive nature of Deep Neural Networks, along with their significant hunger for labeled data, can impair the overall performance these models. Among other techniques, this challenge be tackled by Transfer Learning, which consists in re-using knowledge previously learned a model: method is widely used and has proven effective enhancing models low resources contexts. However, there are relatively few contributions regarding actual transferability features deep learning...

This paper presents a novel multi-threaded quantum inspired optimization algorithm targeted at global search in continuous domains. The proposed approach is based on Diffusion Monte Carlo (DMC) physical model and characterized by set of parallel walk processes. effectiveness the demonstrated experimental results 24 noiseless functions from Black Box Optimization Benchmark Comparing Continuous benchmarking platform (COCO).

In the field of Reinforcement Learning, models based on neural networks are highly performing, but explaining their decisions is very challenging. Instead seeking to open these "black boxes" meet increasing demand for explainability, another approach used rule-based machine learning that explainable by design, such as Anticipatory Learning Classifier Systems (ALCS). ALCS able develop simultaneously a complete representation environment and decision policy this solve tasks. This paper focuses...

Magnetic refrigeration (MR) is an alternative technology to conventional vapour compression with a high potential reduce energy consumption and greenhouse gases. The working principle of MR based on the property magneto caloric materials (MCMs) respond applied external magnetic field by variation in their temperature. To find inexpensive MCMs needed physical properties still issue. This paper describes new method, whose objective automate characterization process MCM properties. method...