A Pipeline to Evaluate the Effects of Noise on Machine Learning Detection of Laryngeal Cancer

Cases of laryngeal cancer are rising, with diagnosis often involving invasive biopsy procedures. An alternate approach is to identify high-risk patients by analysis of voice recordings which can alert clinical teams to those patients that need prioritisation. We propose a pipeline for evaluating speech classifier performance in the presence of noise. We perform experiments using the pipeline with several classifiers and denoising techniques. Random forest classifier performed best with an accuracy of 81.2% on clean data dropping to 63.8% when noise was added to recordings. The accuracy of all classifiers was reduced by added noise, signal denoising improved classifier accuracy but could not fully reverse the effects of noise. The effects of noise on classification is a complex issue which must be resolved for these detection systems to be implemented in clinical practice. We show that the proposed pipeline allows for the evaluation of classifier performance in the presence of noise

The impact of microRNAs on transcriptional heterogeneity and gene co-expression across single embryonic stem cells

MicroRNAs act posttranscriptionally to suppress multiple target genes within a cell population. To what extent this multi-target suppression occurs in individual cells and how it impacts transcriptional heterogeneity and gene co-expression remains unknown. Here we used single-cell sequencing combined with introduction of individual microRNAs. miR-294 and let-7c were introduced into otherwise microRNA-deficient Dgcr8 knockout mouse embryonic stem cells. Both microRNAs induce suppression and correlated expression of their respective gene targets. The two microRNAs had opposing effects on transcriptional heterogeneity within the cell population, with let-7c increasing and miR-294 decreasing the heterogeneity between cells. Furthermore, let-7c promotes, whereas miR-294 suppresses, the phasing of cell cycle genes. These results show at the individual cell level how a microRNA simultaneously has impacts on its many targets and how that in turn can influence a population of cells. The findings have important implications in the understanding of how microRNAs influence the co-expression of genes and pathways, and thus ultimately cell fate

Heart rate, pr, and qt intervals in normal children: A 24‐hour holter monitoring study

A dynamic electrocardiographic Holter monitoring study was performed in 32 healthy children (20 males and 12 females, age range 6-11 years old), without heart disease, according to clinical and noninvasive instrumental examination. We evaluated atrioventricular conduction time (PR), heart rate (HR), and QT interval patterns defining the range of normality of these electrocardiographic parameters. The PR interval ranged from 154 +/- 10 ms (mean +/- SD) for HR less than or equal to 60 to 102 +/- 12 ms for HR greater than or equal to 120 (range 85-180). The absolute mean HR was 87 +/- 10 beats/min (range 72-104), the minimum observed HR being 61 +/- 10 (range 51-79), the maximum 160 +/- 20 beats/min (range 129-186). Daytime mean HR gave a mean value of 93 +/- 10 (range 71-148), while during night hours it was 74 +/- 11 (range 54-98). The minimum QT interval averaged 261 +/- 10 ms for HR greater than 120 and the maximum 389 +/- 9 ms for HR less than or equal to 60; the corresponding mean value of QTc (i.e., QT corrected for HR) ranged from 388 +/- 8 for HR less than or equal to 60 beats/min to 403 +/- 14 ms for HR greater than 120 beats/min. The results of the present study provide data of normal children which can be readily compared against those of subjects in whom cardiac abnormalities are suspect or patient.(ABSTRACT TRUNCATED AT 250 WORDS

Mantra 2.0: An online collaborative resource for drug mode of action and repurposing by network analysis

Elucidation of molecular targets of a compound (mode of action, MoA) and of its off-targets is a crucial step in drug development. We developed an online collaborative resource (MANTRA 2.0) that supports this process by exploiting similarities between drug-induced transcriptional profiles. Drugs are organised in a network of nodes (drugs) and edges (similarities) highlighting “communities” of drugs sharing a similar MoA. A user can upload gene expression profiles (GEPs) before and after drug treatment in one or multiple cell types. An automated processing pipeline transforms the GEPs into a unique drug ”node” embedded in the drug-network. Visual inspection of the neighbouring drugs and communities helps in revealing its MoA, and to suggest new applications of known drugs (drug repurposing). MANTRA 2.0 allows storing and sharing user-generated network nodes, thus making MANTRA 2.0 a collaborative ever-growing resource

OscoNet: Inferring oscillatory gene networks

Background: Oscillatory genes, with periodic expression at the mRNA and/or protein level, have been shown to play a pivotal role in many biological contexts. However, with the exception of the circadian clock and cell cycle, only a few such genes are known. Detecting oscillatory genes from snapshot single-cell experiments is a challenging task due to the lack of time information. Oscope is a recently proposed method to identify co-oscillatory gene pairs using single-cell RNA-seq data. Although promising, the current implementation of Oscope does not provide a principled statistical criterion for selecting oscillatory genes. Results: We improve the optimisation scheme underlying Oscope and provide a wellcalibrated non-parametric hypothesis test to select oscillatory genes at a given FDR threshold. We evaluate performance on synthetic data and three real datasets and show that our approach is more sensitive than the original Oscope formulation, discovering larger sets of known oscillators while avoiding the need for less interpretable thresholds. We also describe how our proposed pseudo-time estimation method is more accurate in recovering the true cell order for each gene cluster while requiring substantially less computation time than the extended nearest insertion approach. Conclusions: OscoNet is a robust and versatile approach to detect oscillatory gene networks from snapshot single-cell data addressing many of the limitations of the original Oscope method

ROBustness In Network (robin): an R Package for Comparison and Validation of Communities

In network analysis, many community detection algorithms have been developed. However, their implementation leaves unaddressed the question of the statistical validation of the results. Here, we present robin (ROBustness In Network), an R package to assess the robustness of the community structure of a network found by one or more methods to give indications about their reliability. The procedure initially detects if the community structure found by a set of algorithms is statistically significant and then compares two selected detection algorithms on the same graph to choose the one that better fits the network of interest. We demonstrate the use of our package on the American College Football benchmark dataset

SoNeUCON_{ABC}Pro: an access control model for social networks with translucent user provenance

Proceedings of: SecureComm 2017 International Workshops, ATCS and SePrIoT, Niagara Falls, ON, Canada, October 22–25, 2017Web-Based Social Networks (WBSNs) are used by millions of people worldwide. While WBSNs provide many benefits, privacy preservation is a concern. The management of access control can help to assure data is accessed by authorized users. However, it is critical to provide sufficient flexibility so that a rich set of conditions may be imposed by users. In this paper we coin the term user provenance to refer to tracing users actions to supplement the authorisation decision when users request access. For example restricting access to a particular photograph to those which have “liked” the owners profile. However, such a tracing of actions has the potential to impact the privacy of users requesting access. To mitigate this potential privacy loss the concept of translucency is applied. This paper extends SoNeUCONABC model and presents SoNeUCONABCPro, an access control model which includes translucent user provenance. Entities and access control policies along with their enforcement procedure are formally defined. The evaluation demonstrates that the system satisfies the imposed goals and supports the feasibility of this model in different scenarios.This work was supported by the MINECO grants TIN2013-46469-R (SPINY: Security and Privacy in the Internet of You) and TIN2016-79095-C2-2-R (SMOG-DEV); by the CAM grant S2013/ICE-3095 (CIBERDINE: Cybersecurity, Data, and Risks); and by the Programa de Ayudas para la Movilidad of Carlos III University of Madrid, Spain (J. M. de Fuentes and L. Gonzalez-Manzano grants)

Identification of genes with oscillatory expression in glioblastoma: the paradigm of SOX2

Quiescence, a reversible state of cell-cycle arrest, is an important state during both normal development and cancer progression. For example, in glioblastoma (GBM) quiescent glioblastoma stem cells (GSCs) play an important role in re-establishing the tumour, leading to relapse. While most studies have focused on identifying differentially expressed genes between proliferative and quiescent cells as potential drivers of this transition, recent studies have shown the importance of protein oscillations in controlling the exit from quiescence of neural stem cells. Here, we have undertaken a genome-wide bioinformatic inference approach to identify genes whose expression oscillates and which may be good candidates for controlling the transition to and from the quiescent cell state in GBM. Our analysis identified, among others, a list of important transcription regulators as potential oscillators, including the stemness gene SOX2, which we verified to oscillate in quiescent GSCs. These findings expand on the way we think about gene regulation and introduce new candidate genes as key regulators of quiescence

BATS: a Bayesian user-friendly software for Analyzing Time Series microarray experiments

BATS is a user-friendly software for Bayesian Analysis of Time Series microarray experiments based on the novel, truly functional and fully Bayesian approach proposed in Angelini et at. (2006). The software is specifically designed for time series data. It allows an user to automatically identify and rank differentially expressed genes and to estimate their expression profiles. BATS successfully manages various technical difficulties which arise in microarray time-course experiments, such as a small number of observations, non-uniform sampling intervals, and presence of missing or multiple data. BATS can carry out analysis with both simulated and real experimental data. It also handles data from different platforms. 1 Availability: BATS is written in Matlab and executable in Windows (Macintosh and Linux version are currently under development). It is freely available upon request from the authors.