Search results for "ComputingMethodologies_PATTERNRECOGNITION"
showing 10 items of 296 documents
MFCC-based Recurrent Neural Network for automatic clinical depression recognition and assessment from speech
2022
Abstract Clinical depression or Major Depressive Disorder (MDD) is a common and serious medical illness. In this paper, a deep Recurrent Neural Network-based framework is presented to detect depression and to predict its severity level from speech. Low-level and high-level audio features are extracted from audio recordings to predict the 24 scores of the Patient Health Questionnaire and the binary class of depression diagnosis. To overcome the problem of the small size of Speech Depression Recognition (SDR) datasets, expanding training labels and transferred features are considered. The proposed approach outperforms the state-of-art approaches on the DAIC-WOZ database with an overall accura…
The Repurposing of Old Drugs or Unsuccessful Lead Compounds by in Silico Approaches: New Advances and Perspectives
2015
Have you a compound in your lab, which was not successful against the designed target, or a drug that is no more attractive? The drug repurposing represents the right way to reconsider them. It can be defined as the modern and rationale approach of the traditional methods adopted in drug discovery, based on the knowledge, insight and luck, alias known as serendipity. This repurposing approach can be applied both in silico and in wet. In this review we report the molecular modeling facilities that can be of huge support in the repurposing of drugs and/or unsuccessful lead compounds. In the last decades, different methods were proposed to help the scientists in drug design and in drug repurpo…
A Nonlinear Label Compression and Transformation Method for Multi-label Classification Using Autoencoders
2016
Multi-label classification targets the prediction of multiple interdependent and non-exclusive binary target variables. Transformation-based algorithms transform the data set such that regular single-label algorithms can be applied to the problem. A special type of transformation-based classifiers are label compression methods, which compress the labels and then mostly use single label classifiers to predict the compressed labels. So far, there are no compression-based algorithms that follow a problem transformation approach and address non-linear dependencies in the labels. In this paper, we propose a new algorithm, called Maniac (Multi-lAbel classificatioN usIng AutoenCoders), which extra…
A label compression method for online multi-label classification
2018
Abstract Many modern applications deal with multi-label data, such as functional categorizations of genes, image labeling and text categorization. Classification of such data with a large number of labels and latent dependencies among them is a challenging task, and it becomes even more challenging when the data is received online and in chunks. Many of the current multi-label classification methods require a lot of time and memory, which make them infeasible for practical real-world applications. In this paper, we propose a fast linear label space dimension reduction method that transforms the labels into a reduced encoded space and trains models on the obtained pseudo labels. Additionally…
Multi-label classification using boolean matrix decomposition
2012
This paper introduces a new multi-label classifier based on Boolean matrix decomposition. Boolean matrix decomposition is used to extract, from the full label matrix, latent labels representing useful Boolean combinations of the original labels. Base level models predict latent labels, which are subsequently transformed into the actual labels by Boolean matrix multiplication with the second matrix from the decomposition. The new method is tested on six publicly available datasets with varying numbers of labels. The experimental evaluation shows that the new method works particularly well on datasets with a large number of labels and strong dependencies among them.
Instance-Based Multi-Label Classification via Multi-Target Distance Regression
2021
Interest in multi-target regression and multi-label classification techniques and their applications have been increasing lately. Here, we use the distance-based supervised method, minimal learning machine (MLM), as a base model for multi-label classification. We also propose and test a hybridization of unsupervised and supervised techniques, where prototype-based clustering is used to reduce both the training time and the overall model complexity. In computational experiments, competitive or improved quality of the obtained models compared to the state-of-the-art techniques was observed. peerReviewed
UNCLES: Method for the identification of genes differentially consistently co-expressed in a specific subset of datasets
2015
Background Collective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently repres…
First spatial isotopic separation of relativistic uranium projectile fragments
1994
Abstract Spatial isotopic separation of relativistic uranium projectile fragments has been achieved for the first time. The fragments were produced in peripheral nuclear collisions and spatially separated in-flight with the fragment separator FRS at GSI. A two-fold magnetic-rigidity analysis was applied exploiting the atomic energy loss in specially shaped matter placed in the dispersive central focal plane. Systematic investigations with relativistic projectiles ranging from oxygen up to uranium demonstrate that the FRS is a universal and powerful facility for the production and in-flight separation of monoisotopic, exotic secondary beams of all elements up to Z = 92. This achievement has …
BOGENVI: A Biomedical Ontology for Modelling Gene*Environment Interactions on Intermediate Phenotypes in Nutrigenomics Research
2008
Nutritional Genomics is demanding computing models and technological platforms in order to support acquisition, storage, management and presentation of all the information generated coming from heterogeneous sources: genotypes, environmental factors (diet and other life-style factors) and phenotypes (intermediate and final phenotypes). Our aim is to build a biomedical ontology in order to modelling gene*environment interactions on intermediate phenotypes by means of formalising and integrating genomic, environmental and phenotypic data, in the field of research on Nutritional Genomics applied to cardiovascular diseases and associated phenotypes. This ontology is part of a Health Information…
UPC++ for bioinformatics: A case study using genome-wide association studies
2014
Modern genotyping technologies are able to obtain up to a few million genetic markers (such as SNPs) of an individual within a few minutes of time. Detecting epistasis, such as SNP-SNP interactions, in Genome-Wide Association Studies is an important but time-consuming operation since statistical computations have to be performed for each pair of measured markers. Therefore, a variety of HPC architectures have been used to accelerate these studies. In this work we present a parallel approach for multi-core clusters, which is implemented with UPC++ and takes advantage of the features available in the Partitioned Global Address Space and Object Oriented Programming models. Our solution is base…