1,721,199 research outputs found
Artificial neural network technologies to identify biomarkers for therapeutic intervention
No full textHigh-throughput technologies such as DNA/RNA microarrays, mass spectrometry and protein chips are creating unprecedented opportunities to accelerate towards the understanding of living systems and the identification of target genes and pathways for drug development and therapeutic intervention. However, the increasing volumes of data generated by molecular profiling experiments pose formidable challenges to investigate an overwhelming mass of information and turn it into predictive, deployable markers. Advanced biostatistics and machine learning methods from computer science have been applied to analyze and correlate numerical values of profiling intensities to physiological states. This article reviews the application of artificial neural networks, an information-processing tool, to the identification of sets of diagnostic/prognostic biomarkers from high-throughput profiling data
Artificial neural network technologies to identify biomarkers for therapeutic intervention
No full textAnalysis of gene expression profiles at chromosomal level
No full textTranscriptional profiling of whole genomes using cDNA or oligonucleotide high-density arrays is becoming increasingly popular among the biomedical research community. Although advances in technology and the rapid rise in microarray data availability are leading to new insight into fundamental biological problems, investigators are still confronted with the major problem of upgrading the information content of regulated gene lists obtained from microarray experiments. Indeed, the efficient exploitation of gene expression databases requires not only computational tools for management, analysis, and functional annotation of primary data, but also integrating lists of modulated genes with of other sources of genomic information, such as gene sequence, locus or structural characteristics. In particular, integration between expression profiles and chromosomal localizations could be effective in detecting gene structural abnormalities such as genomic gains and losses and/or translocations. The aim of the present study is to apply computational tools for mapping transcriptional data at chromosomal level and detecting clusters of regionally modulated genes in cancer specimens.Statistical tests and signal processing procedures are used to integrate expression profiles and gene sequence information and identify peculiar regions of modulated expression. In particular, the method is based on the application of a smoothing, coordinate-dependent function (e.g., cubic splines) to a standard transcriptional specificity statistic (e.g., standard F-statistic), commonly used to detect differentially expressed genes.This computational tool has been tested on different microarray data sets obtained from various human tumor samples (e.g., solid tumors and hematological disorders). In particular, the application of chromosomal level analysis to the transcriptional database presented by Bhattacharjee (Bhattacharjee et al., 2001), Armstrong (Armstrong et al., 2002), and Ross (Ross et al., 2003) allowed the detection of regional signals corresponding to known as well as putative loci with high frequent genomic losses and gains or marking translocation events
A locally adaptive statistical procedure (LAP) to identify differentially expressed chromosomal regions
No full textMotivation: The systematic integration of expression profiles and other types of gene information, such as chromosomal localization, ontological annotations and sequence characteristics, still represents a challenge in the gene expression arena. In particular, the analysis of transcriptional data in context of the physical location of genes in a genome appears promising in detecting chromosomal regions with transcriptional imbalances often characterizing cancer.Results: A computational tool named locally adaptive statistical procedure (LAP), which incorporates transcriptional data and structural information for the identification of differentially expressed chromosomal regions, is described. LAP accounts for variations in the distance between genes and in gene density by smoothing standard statistics on gene position before testing the significance of their differential levels of gene expression. The procedure smoothes parameters and computes p-values locally to account for the complex structure of the genome and to more precisely estimate the differential expression of chromosomal regions. The application of LAP to three independent sets of raw expression data allowed identifying differentially expressed regions that are directly involved in known chromosomal aberrations characteristic of tumors
COVID-19 health policy evaluation: integrating health and economic perspectives with a data envelopment analysis approach
No full textAbstract The COVID-19 pandemic is a global challenge to humankind. To improve the knowledge regarding relevant, efficient and effective COVID-19 measures in health policy, this paper applies a multi-criteria evaluation approach with population, health care, and economic datasets from 19 countries within the OECD. The comparative investigation was based on a Data Envelopment Analysis approach as an efficiency measurement method. Results indicate that on the one hand, factors like population size, population density, and country development stage, did not play a major role in successful pandemic management. On the other hand, pre-pandemic healthcare system policies were decisive. Healthcare systems with a primary care orientation and a high proportion of primary care doctors compared to specialists were found to be more efficient than systems with a medium level of resources that were partly financed through public funding and characterized by a high level of access regulation. Roughly two weeks after the introduction of ad hoc measures, e.g., lockdowns and quarantine policies, we did not observe a direct impact on country-level healthcare efficiency, while delayed lockdowns led to significantly lower efficiency levels during the first COVID-19 wave in 2020. From an economic perspective, strategies without general lockdowns were identified as a more efficient strategy than the full lockdown strategy. Additionally, governmental support of short-term work is promising. Improving the efficiency of COVID-19 countermeasures is crucial in saving as many lives as possible with limited resources.Abstract The COVID-19 pandemic is a global challenge to humankind. To improve the knowledge regarding relevant, efficient and effective COVID-19 measures in health policy, this paper applies a multi-criteria evaluation approach with population, health care, and economic datasets from 19 countries within the OECD. The comparative investigation was based on a Data Envelopment Analysis approach as an efficiency measurement method. Results indicate that on the one hand, factors like population size, population density, and country development stage, did not play a major role in successful pandemic management. On the other hand, pre-pandemic healthcare system policies were decisive. Healthcare systems with a primary care orientation and a high proportion of primary care doctors compared to specialists were found to be more efficient than systems with a medium level of resources that were partly financed through public funding and characterized by a high level of access regulation. Roughly two weeks after the introduction of ad hoc measures, e.g., lockdowns and quarantine policies, we did not observe a direct impact on country-level healthcare efficiency, while delayed lockdowns led to significantly lower efficiency levels during the first COVID-19 wave in 2020. From an economic perspective, strategies without general lockdowns were identified as a more efficient strategy than the full lockdown strategy. Additionally, governmental support of short-term work is promising. Improving the efficiency of COVID-19 countermeasures is crucial in saving as many lives as possible with limited resources
In-line monitoring of the key reactions in automated solid-phase peptide synthesis
No full textA novel strategy for the in-line monitoring of the reactions involved in the automated synthesis of peptides is presented
Analysis of un-replicated time-course microarray experiments
No full textSince transcriptional control is the result of complex networks, analyzing dynamical states of gene expression is of paramount importance to detect the multivariate nature of biological mechanisms. Although hundreds of studies fully demonstrated the relevancy of microarrays in describing different physiological conditions, to reconstruct complex interaction pathways it is necessary to analyze the temporal evolution of transcriptional states. However, a robust experimental design for identifying differentially expressed genes over a temporal window would require large amounts of microarrays. Unfortunately, replicates for each time point and experimental condition are not always available, because of cost limitations and/or biological samples scarcity. In addition, common data analysis tools, like ANOVA, require replicates and disregard correlation structure among times. We present a method for the identification of differentially expressed genes in un-replicated time-course experiments. The procedure does not assume any model or distribution function, takes into account the correlation of data, and does not require sample replicates at the various time points, other than the presence of an initial time point for all analyzed conditions. The identification of differentially expressed genes as the result of a system perturbation is formally stated as a hypothesis testing problem in which a defined statistic is used to rank transcripts in order of evidence against the null hypothesis. Specifically, i) data are structured so that measurements are correlated in time, within the same biological condition; ii) the null hypothesis is formulated so that changes in expression levels at different time points are equivalent; iii) time point t0 represents the system before the perturbation. Therefore, modulated genes are detected testing the statistical significance of expression differences between physiological states at each time point, once corrected by the variability at t0, and given an empirical null distribution constructed using permutations. Statistical significance is assessed by the q-value. The method has been tested on time-course microarray experiments aimed at studying the temporal changes of gene expression in: i) skeletal muscle cells treated with a histone deacetylase inhibitor (Iezzi et al., Dev Cell; 2004) and ii) immature mouse dendritic cells (DC) exposed to larval and egg stages of S. mansoni (Trottein et al., J Immunol; 2004). Differentially expressed genes, identified using the proposed algorithm, have been compared with results obtained from ANOVA model and SAM paired test. The biological significance and soundness of selected transcripts was also verified using global functional profiling by means of OntoTools. Results demonstrate that this novel procedure allows the identification of biologically relevant genes using half of the replicates required by standard model-based approaches
Marker identification and classification of cancer types using gene expression data and SIMCA
Full text linkObjectives. High-throughput technologies are radically boosting the understanding of living systems, thus creating enormous opportunities to elucidate the biological processes of cells in different physiological states. In particular, the application of DNA microarrays to monitor expression profiles from tumor cells is improving cancer analysis to levels that classical methods have been unable to reach. However, molecular diagnostics based on expression profiling requires addressing computational issues as the overwhelming number of variables and the complex, multi-class nature of tumor samples. Thus, the objective of the present research has been the development of a computational procedure for feature extraction and classification of gene expression data.Methods. The Soft Independent Modeling of Class Analogy (SIMCA) approach has been implemented in a data mining scheme, which allows the identification of those genes that are most likely to confer robust and accurate classification of samples from multiple tumor types.Results: The proposed method has been tested on two different microarray data sets, namely Golub's analysis of acute human leukemia [1] and the small round blue cell tumors study presented by Khan et al. [2]. The identified features represent a rational and dimensionally reduced base for understanding the biology of diseases, defining targets of therapeutic intervention, and developing diagnostic tools for classification of pathological states.Conclusions: The analysis of the SIMCA model residuals allows the identification of specific phenotype markers. At the some time, the class analogy approach provides the assignment to multiple classes, such as different pathological conditions or tissue samples, for previously unseen instances
Temporal Sequence Pattern Learning and Dynamic System Control (DSC)
No full textWe have investigated the role of temporal sequence learning, using an unsuper- vised artificial neural network (1), called Monoconnected Autoreflexive Neural Net- work, for better understanding the implicit learning process role, involved during elementary associative learning processes. Several neural network models have been proposed to describe implicit learning (IL), using unsupervised and self-organized models (2, 3). In our experiments we used a real biochemical data set consisting of 15 features, that deals with penicillin production (112 temporal sequence blocks with 11 sequence points per block (1232 patterns). The prediction task requires that the neural network can predict the correct sequence position, after a preliminary training (50% of all patterns). After training, the neural network learn to find the correct position in the temporal sequences with good accuracy. Our results seem to confirm that elementary associative learning, could be used in temporal sequence learning and that dynamic system control (DSC) tasks (for instance to know which features are more sensitive to a better penicillin production), could be derived from the implicit learning process, using the importance of different features, recovered from the weight matrix analysis
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