A transfer-learning approach to feature extraction from cancer transcriptomes with deep autoencoders

Publicado en Lecture Notes in Computer Science.The diagnosis and prognosis of cancer are among the more challenging tasks that oncology medicine deals with. With the main aim of fitting the more appropriate treatments, current personalized medicine focuses on using data from heterogeneous sources to estimate the evolu- tion of a given disease for the particular case of a certain patient. In recent years, next-generation sequencing data have boosted cancer prediction by supplying gene-expression information that has allowed diverse machine learning algorithms to supply valuable solutions to the problem of cancer subtype classification, which has surely contributed to better estimation of patient’s response to diverse treatments. However, the efficacy of these models is seriously affected by the existing imbalance between the high dimensionality of the gene expression feature sets and the number of sam- ples available for a particular cancer type. To counteract what is known as the curse of dimensionality, feature selection and extraction methods have been traditionally applied to reduce the number of input variables present in gene expression datasets. Although these techniques work by scaling down the input feature space, the prediction performance of tradi- tional machine learning pipelines using these feature reduction strategies remains moderate. In this work, we propose the use of the Pan-Cancer dataset to pre-train deep autoencoder architectures on a subset com- posed of thousands of gene expression samples of very diverse tumor types. The resulting architectures are subsequently fine-tuned on a col- lection of specific breast cancer samples. This transfer-learning approach aims at combining supervised and unsupervised deep learning models with traditional machine learning classification algorithms to tackle the problem of breast tumor intrinsic-subtype classification.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Targeted knock-down of miR21 primary transcripts using snoMEN vectors induces apoptosis in human cancer cell lines

We have previously reported an antisense technology, 'snoMEN vectors', for targeted knock-down of protein coding mRNAs using human snoRNAs manipulated to contain short regions of sequence complementarity with the mRNA target. Here we characterise the use of snoMEN vectors to target the knock-down of micro RNA primary transcripts. We document the specific knock-down of miR21 in HeLa cells using plasmid vectors expressing miR21-targeted snoMEN RNAs and show this induces apoptosis. Knock-down is dependent on the presence of complementary sequences in the snoMEN vector and the induction of apoptosis can be suppressed by over-expression of miR21. Furthermore, we have also developed lentiviral vectors for delivery of snoMEN RNAs and show this increases the efficiency of vector transduction in many human cell lines that are difficult to transfect with plasmid vectors. Transduction of lentiviral vectors expressing snoMEN targeted to pri-miR21 induces apoptosis in human lung adenocarcinoma cells, which express high levels of miR21, but not in human primary cells. We show that snoMEN-mediated suppression of miRNA expression is prevented by siRNA knock-down of Ago2, but not by knock-down of Ago1 or Upf1. snoMEN RNAs colocalise with Ago2 in cell nuclei and nucleoli and can be co-immunoprecipitated from nuclear extracts by antibodies specific for Ago2

Climate Variability and Ross River Virus Transmission in Townsville Region, Australia 1985 to 1996

Background How climate variability affects the transmission of infectious diseases at a regional level remains unclear. In this paper, we assessed the impact of climate variation on the Ross River virus (RRv) transmission in the Townsville region, Queensland, north-east Australia. Methods Population-based information was obtained on monthly variations in RRv cases, climatic factors, sea level, and population growth between 1985 and 1996. Cross-correlations were computed for a series of associations between climate variables (rainfall, maximum temperature, minimum temperature, relative humidity and high tide) and the monthly incidence of RRv disease over a range of time lags. The impact of climate variability on RRv transmission was assessed using the seasonal auto-regressive integrated moving average (SARIMA) model. Results There were significant correlations of the monthly incidence of RRv to rainfall, maximum temperature, minimum temperature and relative humidity, all at a lag of 2 months, and high tide in the current month. The results of SARIMA models show that monthly average rainfall (β=0.0012, p=0.04) and high tide (β=0.0262, p=0.01) were significantly associated with RRv transmission, although temperature and relative humidity did not seem to have played an important role in the Townsville region. Conclusions Rainfall, and high tide were likely to be key determinants of RRv transmission in the Townsville region

Characterizing genomic alterations in cancer by complementary functional associations.

Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment. We used REVEALER to uncover complementary genomic alterations associated with the transcriptional activation of β-catenin and NRF2, MEK-inhibitor sensitivity, and KRAS dependency. REVEALER successfully identified both known and new associations, demonstrating the power of combining functional profiles with extensive characterization of genomic alterations in cancer genomes

Display of probability densities for data from a continuous distribution

Based on cumulative distribution functions, Fourier series expansion and Kolmogorov tests, we present a simple method to display probability densities for data drawn from a continuous distribution. It is often more efficient than using histograms.Comment: 5 pages, 4 figures, presented at Computer Simulation Studies XXIV, Athens, GA, 201

Search for the Decays B^0 -> D^{(*)+} D^{(*)-}

Using the CLEO-II data set we have searched for the Cabibbo-suppressed decays B^0 -> D^{(*)+} D^{(*)-}. For the decay B^0 -> D^{*+} D^{*-}, we observe one candidate signal event, with an expected background of 0.022 +/- 0.011 events. This yield corresponds to a branching fraction of Br(B^0 -> D^{*+} D^{*-}) = (5.3^{+7.1}_{-3.7}(stat) +/- 1.0(syst)) x 10^{-4} and an upper limit of Br(B^0 -> D^{*+} D^{*-}) D^{*\pm} D^\mp and B^0 -> D^+ D^-, no significant excess of signal above the expected background level is seen, and we calculate the 90% CL upper limits on the branching fractions to be Br(B^0 -> D^{*\pm} D^\mp) D^+ D^-) < 1.2 x 10^{-3}.Comment: 12 page postscript file also available through http://w4.lns.cornell.edu/public/CLNS, submitted to Physical Review Letter

Improved Measurement of the Pseudoscalar Decay Constant fDsf_{D_{s}}

We present a new determination of the Ds decay constant, f_{Ds} using 5 million continuum charm events obtained with the CLEO II detector. Our value is derived from our new measured ratio of widths for Ds -> mu nu/Ds -> phi pi of 0.173+/- 0.021 +/- 0.031. Taking the branching ratio for Ds -> phi pi as (3.6 +/- 0.9)% from the PDG, we extract f_{Ds} = (280 +/- 17 +/- 25 +/- 34){MeV}. We compare this result with various model calculations.Comment: 23 page postscript file, postscript file also available through http://w4.lns.cornell.edu/public/CLN

Comparative Effectiveness Research: An Empirical Study of Trials Registered in ClinicalTrials.gov

Background The $1.1 billion investment in comparative effectiveness research will reshape the evidence-base supporting decisions about treatment effectiveness, safety, and cost. Defining the current prevalence and characteristics of comparative effectiveness (CE) research will enable future assessments of the impact of this program. Methods We conducted an observational study of clinical trials addressing priority research topics defined by the Institute of Medicine and conducted in the US between 2007 and 2010. Trials were identified in ClinicalTrials.gov. Main outcome measures were the prevalence of comparative effectiveness research, nature of comparators selected, funding sources, and impact of these factors on results. Results 231 (22.3%; 95% CI 19.8%–24.9%) studies were CE studies and 804 (77.7%; 95% CI, 75.1%–80.2%) were non-CE studies, with 379 (36.6%; 95% CI, 33.7%–39.6%) employing a placebo control and 425 (41.1%; 95% CI, 38.1%–44.1%) no control. The most common treatments examined in CE studies were drug interventions (37.2%), behavioral interventions (28.6%), and procedures (15.6%). Study findings were favorable for the experimental treatment in 34.8% of CE studies and greater than twice as many (78.6%) non-CE studies (P<0.001). CE studies were more likely to receive government funding (P = 0.003) and less likely to receive industry funding (P = 0.01), with 71.8% of CE studies primarily funded by a noncommercial source. The types of interventions studied differed based on funding source, with 95.4% of industry trials studying a drug or device. In addition, industry-funded CE studies were associated with the fewest pediatric subjects (P<0.001), the largest anticipated sample size (P<0.001), and the shortest study duration (P<0.001). Conclusions In this sample of studies examining high priority areas for CE research, less than a quarter are CE studies and the majority is supported by government and nonprofits. The low prevalence of CE research exists across CE studies with a broad array of interventions and characteristics.National Library of Medicine (U.S.) (5G08LM009778)National Institutes of Health (U.S.

Effects of strategies to promote children\u27s physical activity on potential mediators

The aim of this paper is to review evidence of the effectiveness of interventions that present physical activity outcomes and potential mediators of behavioural change among 4&ndash;12-year-old children. A systematic search of electronic databases for original research articles published in peer-review journals between January 1985 and the end of June 2006 was carried out. A total of 19 studies that reported intervention effects on physical activity and mediators of behavioural change were identified. The most common mediators reported included physical activity knowledge or beliefs (11 studies); self-efficacy (8 studies); and enjoyment or preference for physical activity (6 studies). Less frequently reported mediators included attitudes, behavioural capability, intentions, outcome expectancies, social norms, social support and self-concept. Seven of the 11 interventions that reported intervention effects on knowledge/beliefs stated positive changes in this mediator. Four of the eight studies that reported intervention effects on self-efficacy had significant improvements; however, only two out of six interventions reported significant improvements in physical activity enjoyment or preference. None of the studies reviewed reported whether changes in these constructs mediated changes in children\u27s physical activity behaviours. Although more than half of the studies reviewed reported a positive intervention effect on children\u27s physical activity, no study carried out a mediating analysis to attempt to identify the mechanisms of change. Future research should more clearly identify the mediators of behavioural change that are being targeted and whether this explains intervention effects.<br /