Search results for "missing"
showing 10 items of 174 documents
Metabolic flux understanding of Pichia pastoris grown on heterogenous culture media
2014
[EN] Within the emergent field of Systems Biology, mathematical models obtained from physical chemical laws (the so-called first principles-based models) of microbial systems are employed to discern the principles that govern cellular behaviour and achieve a predictive understanding of cellular functions. The reliance on this biochemical knowledge has the drawback that some of the assumptions (specific kinetics of the reaction system, unknown dynamics and values of the model parameters) may not be valid for all the metabolic possible states of the network. In this uncertainty context, the combined use of fundamental knowledge and data measured in the fermentation that describe the behaviour…
Deep Learning and Cultural Heritage: The CEPROQHA Project Case Study
2019
Cultural heritage takes an important part of the history of humankind as it is one of the most powerful tools for the transfer and preservation of moral identity. As a result, these cultural assets are considered highly valuable and sometimes priceless. Digital technologies provided multiple tools that address challenges related to the promotion and information access in the cultural context. However, the large data collections of cultural information have more potential to add value and address current challenges in this context with the recent progress in artificial intelligence (AI) with deep learning and data mining tools. Through the present paper, we investigate several approaches tha…
Using Deep Learning to Extrapolate Protein Expression Measurements
2020
Mass spectrometry (MS)-based quantitative proteomics experiments typically assay a subset of up to 60% of the ≈20 000 human protein coding genes. Computational methods for imputing the missing values using RNA expression data usually allow only for imputations of proteins measured in at least some of the samples. In silico methods for comprehensively estimating abundances across all proteins are still missing. Here, a novel method is proposed using deep learning to extrapolate the observed protein expression values in label-free MS experiments to all proteins, leveraging gene functional annotations and RNA measurements as key predictive attributes. This method is tested on four datasets, in…
Missing value imputation in proximity extension assay-based targeted proteomics data
2020
Targeted proteomics utilizing antibody-based proximity extension assays provides sensitive and highly specific quantifications of plasma protein levels. Multivariate analysis of this data is hampered by frequent missing values (random or left censored), calling for imputation approaches. While appropriate missing-value imputation methods exist, benchmarks of their performance in targeted proteomics data are lacking. Here, we assessed the performance of two methods for imputation of values missing completely at random, the previously top-benchmarked ‘missForest’ and the recently published ‘GSimp’ method. Evaluation was accomplished by comparing imputed with remeasured relative concentrations…
Study of the fragmentation of b quarks into B mesons at the Z peak
2001
The fragmentation of b quarks into B mesons is studied with four million hadronic Z decays collected by the ALEPH experiment during the years 1991-1995. A semi-exclusive reconstruction of B->l nu D(*) decays is performed, by combining lepton candidates with fully reconstructed D(*) mesons while the neutrino energy is estimated from the missing energy of the event. The mean value of xewd, the energy of the weakly-decaying B meson normalised to the beam energy, is found to be mxewd = 0.716 +- 0.006 (stat) +- 0.006 (syst) using a model-independent method; the corresponding value for the energy of the leading B meson is mxel = 0.736 +- 0.006 (stat) +- 0.006 (syst). The reconstructed spectra …
Tevatron Combination of Single-Top-Quark Cross Sections and Determination of the Magnitude of the Cabibbo-Kobayashi-Maskawa Matrix Element Vtb
2015
et al.
From Emotional (Dys)Regulation to Internet Addiction: A Mediation Model of Problematic Social Media Use among Italian Young Adults
2021
Internet addiction (IA) has mostly been investigated with the fear of missing out and difficulties in emotional regulation. The present study examined the link between IA and variables related to problematic social media use (i.e., fear of missing out, social media addiction), together with emotional (dys)regulation and personality traits, providing new insights and an integrated assessment of IA. In total, 397 participants, aged 18–35 years (M = 22.00; SD = 3.83), were administered a set of questionnaires pertaining to IA, problematic social media use, emotional (dys)regulation, and personality traits. Pearson’s correlations showed significant associations between IA and the investigated v…
Evolutionary Spectrum for Random Field and Missing Observations
2012
There are innumerable situations where the data observed from a non-stationary random field are collected with missing values. In this work a consistent estimate of the evolutionary spectral density is given where some observations are randomly missing.
Selection bias was reduced by recontacting nonparticipants
2016
Objective One of the main goals of health examination surveys is to provide unbiased estimates of health indicators at the population level. We demonstrate how multiple imputation methods may help to reduce the selection bias if partial data on some nonparticipants are collected. Study Design and Setting In the FINRISK 2007 study, a population-based health study conducted in Finland, a random sample of 10,000 men and women aged 25–74 years were invited to participate. The study included a questionnaire data collection and a health examination. A total of 6,255 individuals participated in the study. Out of 3,745 nonparticipants, 473 returned a simplified questionnaire after a recontact. Both…
A new methodology for Functional Principal Component Analysis from scarce data. Application to stroke rehabilitation.
2015
Functional Principal Component Analysis (FPCA) is an increasingly used methodology for analysis of biomedical data. This methodology aims to obtain Functional Principal Components (FPCs) from Functional Data (time dependent functions). However, in biomedical data, the most common scenario of this analysis is from discrete time values. Standard procedures for FPCA require obtaining the functional data from these discrete values before extracting the FPCs. The problem appears when there are missing values in a non-negligible sample of subjects, especially at the beginning or the end of the study, because this approach can compromise the analysis due to the need to extrapolate or dismiss subje…