Search results for " Learning"

DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning

2020

Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the functional annotation of variants is typically inferred by post hoc analyses. A specific class of deep learning-based methods allows for the prediction of regulatory effects per variant on several cell type-specific chromatin features. We here describe “DeepWAS”, a new approach that integrates these regulatory effect predictions of single variants into a multivariate GWAS setting. Thereby, single variants associated with a trait or disease are directly coupled to their impact on a chromatin feature in a cell type. Up to…

The Effects of Static and Dynamic Visual Representations as Aids for Primary School Children in Tasks of Auditory Discrimination of Sound Patterns. A…

2018

It has been proposed that non-conventional presentations of visual information could be very useful as a scaffolding strategy in the learning of Western music notation. As a result, this study has attempted to determine if there is any effect of static and dynamic presentation modes of visual information in the recognition of sound patterns. An intervention-based quasi-experimental design was adopted with two groups of fifth-grade students in a Spanish city. Students did tasks involving discrimination, auditory recognition and symbolic association of the sound patterns with non-musical representations, either static images (S group), or dynamic images (D group). The results showed neither s…

A clustering package for nucleotide sequences using Laplacian Eigenmaps and Gaussian Mixture Model.

2018

International audience; In this article, a new Python package for nucleotide sequences clustering is proposed. This package, freely available on-line, implements a Laplacian eigenmap embedding and a Gaussian Mixture Model for DNA clustering. It takes nucleotide sequences as input, and produces the optimal number of clusters along with a relevant visualization. Despite the fact that we did not optimise the computational speed, our method still performs reasonably well in practice. Our focus was mainly on data analytics and accuracy and as a result, our approach outperforms the state of the art, even in the case of divergent sequences. Furthermore, an a priori knowledge on the number of clust…

Intrinsic volatility of synaptic connections — a challenge to the synaptic trace theory of memory

2017

According to the synaptic trace theory of memory, activity-induced changes in the pattern of synaptic connections underlie the storage of information for long periods. In this framework, the stability of memory critically depends on the stability of the underlying synaptic connections. Surprisingly however, synaptic connections in the living brain are highly volatile, which poses a fundamental challenge to the synaptic trace theory. Here we review recent experimental evidence that link the initial formation of a memory with changes in the pattern of connectivity, but also evidence that synaptic connections are considerably volatile even in the absence of learning. Then we consider different…

2020

Human movements are characterized by highly non-linear and multi-dimensional interactions within the motor system. Recently, an increasing emphasis on machine-learning applications has led to a significant contribution to the field of gait analysis, e.g., in increasing the classification performance. In order to ensure the generalizability of the machine-learning models, different data preprocessing steps are usually carried out to process the measured raw data before the classifications. In the past, various methods have been used for each of these preprocessing steps. However, there are hardly any standard procedures or rather systematic comparisons of these different methods and their im…

Evaluation of tumor immune contexture among intrinsic molecular subtypes helps to predict outcome in early breast cancer

2021

BackgroundThe prognosis of early breast cancer is linked to clinic-pathological stage and the molecular characteristics of intrinsic tumor cells. In some patients, the amount and quality of tumor-infiltrating immune cells appear to affect long term outcome. We aimed to propose a new tool to estimate immune infiltrate, and link these factors to patient prognosis according to breast cancer molecular subtypes.MethodsWe performed in silico analyses in more than 2800 early breast cancer transcriptomes with corresponding clinical annotations. We first developed a new gene expression deconvolution algorithm that accurately estimates the quantity of immune cell populations (tumor immune contexture,…

Asynchronous and pathological windows of implantation: two causes of recurrent implantation failure

2018

STUDY QUESTION: Is endometrial recurrent implantation failure (RIF) only a matter of an asynchronous (displaced) window of implantation (WOI), or could it also be a pathological (disrupted) WOI? SUMMARY ANSWER: Our predictive results demonstrate that both displaced and disrupted WOIs exist and can present independently or together in the same RIF patient. WHAT IS KNOWN ALREADY: Since 2002, many gene expression signatures associated with endometrial receptivity and RIF have been described. Endometrial transcriptomics prediction has been applied to the human WOI in two previous studies. One study describes endometrial RIF to be the result of a temporal displacement of the WOI. The other indic…

NF1 microdeletion syndrome: case report of two new patients

2019

Abstract Background 17q11.2 microdeletions, which include the neurofibromatosis type 1 (NF1) gene region, are responsible for the NF1 microdeletion syndrome, observed in 4.2% of all NF1 patients. Large deletions of the NF1 gene and its flanking regions are associated with a more severe NF1 phenotype than the NF1 general population. Case presentation We hereby describe the clinical and molecular features of two girls (aged 2 and 4 years, respectively), with non-mosaic atypical deletions. Patient 1 showed fifteen café-au-lait spots and axillary freckling, as well as a Lisch nodule in the left eye, strabismus, high-arched palate, malocclusion, severe kyphoscoliosis, bilateral calcaneovalgus fo…

Hippocampal electrical stimulation disrupts associative learning when targeted at dentate spikes

2017

KEY POINTS Dentate spikes are fast fluctuations of hilar local-field potentials that take place during rest and are thought to reflect input arriving from the entorhinal cortex to the hippocampus. During dentate spikes, neuronal firing in hippocampal input (dentate gyrus) and output (CA1/CA3) regions is uncoupled. To date, the behavioural significance of dentate spikes is unknown. Here, we provide evidence that disrupting the dentate spike-related uncoupling of the dentate gyrus and the CA1/CA3 subregions for 1 h after training retards associative learning. We suggest dentate spikes play a significant role in memory consolidation. ABSTRACT Hippocampal electrophysiological oscillations, name…

Automatic sleep scoring: A deep learning architecture for multi-modality time series

2020

Background: Sleep scoring is an essential but time-consuming process, and therefore automatic sleep scoring is crucial and urgent to help address the growing unmet needs for sleep research. This paper aims to develop a versatile deep-learning architecture to automate sleep scoring using raw polysomnography recordings. Method: The model adopts a linear function to address different numbers of inputs, thereby extending model applications. Two-dimensional convolution neural networks are used to learn features from multi-modality polysomnographic signals, a “squeeze and excitation” block to recalibrate channel-wise features, together with a long short-term memory module to exploit long-range co…