Model-based clustering with data correction for removing artifacts in gene expression data

The NIH Library of Integrated Network-based Cellular Signatures (LINCS) contains gene expression data from over a million experiments, using Luminex Bead technology. Only 500 colors are used to measure the expression levels of the 1,000 landmark genes measured, and the data for the resulting pairs of genes are deconvolved. The raw data are sometimes inadequate for reliable deconvolution leading to artifacts in the final processed data. These include the expression levels of paired genes being flipped or given the same value, and clusters of values that are not at the true expression level. We propose a new method called model-based clustering with data correction (MCDC) that is able to identify and correct these three kinds of artifacts simultaneously. We show that MCDC improves the resulting gene expression data in terms of agreement with external baselines, as well as improving results from subsequent analysis.Comment: 28 page

A Posterior Probability Approach for Gene Regulatory Network Inference in Genetic Perturbation Data

Inferring gene regulatory networks is an important problem in systems biology. However, these networks can be hard to infer from experimental data because of the inherent variability in biological data as well as the large number of genes involved. We propose a fast, simple method for inferring regulatory relationships between genes from knockdown experiments in the NIH LINCS dataset by calculating posterior probabilities, incorporating prior information. We show that the method is able to find previously identified edges from TRANSFAC and JASPAR and discuss the merits and limitations of this approach

Machine Learning, Misinformation, and Citizen Science

Current methods of operationalizing concepts of misinformation in machine learning are often problematic given idiosyncrasies in their success conditions compared to other models employed in the natural and social sciences. The intrinsic value-ladenness of misinformation and the dynamic relationship between citizens' and social scientists' concepts of misinformation jointly suggest that both the construct legitimacy and the construct validity of these models needs to be assessed via more democratic criteria than has previously been recognized

Edgeworth’s Mathematization of Social Well-Being

Francis Ysidro Edgeworth’s unduly neglected monograph New and Old Methods of Ethics (1877) advances a highly sophisticated and mathematized account of social well-being in the utilitarian tradition of his 19th-century contemporaries. This article illustrates how his usage of the ‘calculus of variations’ was combined with findings from empirical psychology and economic theory to construct a consequentialist axiological framework. A conclusion is drawn that Edgeworth is a methodological predecessor to several important methods, ideas, and issues that continue to be discussed in contemporary social well-being studies

Econophysics: making sense of a chimera

The history of economic thought witnessed several prominent economists who took seriously models and concepts in physics for the elucidation and prediction of economic phenomena. Econophysics is an emerging discipline at the intersection of heterodox economics and the physics of complex systems, with practitioners typically engaged in two overlapping but distinct methodological programs. The first is to export mathematical methods used in physics for the purposes of studying economic phenomena. The second is to export mechanisms in physics into economics. A conclusion is drawn that physics transfer is often justified at the level of mathematical transfer but unjustified at the level of mechanistic transfer

Edgeworth’s Mathematization of Social Well-Being

Francis Ysidro Edgeworth’s unduly neglected monograph New and Old Methods of Ethics (1877) advances a highly sophisticated and mathematized account of social well-being in the utilitarian tradition of his 19th-century contemporaries. This article illustrates how his usage of the ‘calculus of variations’ was combined with findings from empirical psychology and economic theory to construct a consequentialist axiological framework. A conclusion is drawn that Edgeworth is a methodological predecessor to several important methods, ideas, and issues that continue to be discussed in contemporary social well-being studies

Detecting Financial Statement Fraud: An Alternative Evaluation of Automated Tools Using Portfolio Performance

This article investigates the effect of using financial statement fraud detection models in constructing investment portfolios. Three financial statement fraud detection models are recreated and used to inform portfolio construction. Portfolio performance is compared between two strategies investing in companies on the S&P 500 predicted to have the highest (lowest) likelihood of financial statement fraud according to three models. Investment performance under the two strategies and across the three models are assessed using Fama-French regressions over a trading period from 2003 to 2021 and during market shocks. The portfolio of companies with the highest likelihood of fraud underperforms, characterized by inadequate returns relative to risk exposures. In the case of low-likelihood firms, results are consistent with risk-reward expectations. Financial results were consistent across all three fraud models, indicating that each model effectively discriminates between companies predicted to exhibit financial statement fraud. This research investigates the effect of financial statement fraud risk on investment performance and provides an alternative evaluation of financial statement fraud detection models, complementing the traditional accounting analysis of such models

A three-timepoint network analysis of Covid-19's impact on schizotypal traits, paranoia and mental health through loneliness

The 2019 coronavirus (Covid-19) pandemic has impacted people's mental wellbeing. Studies to date have examined the prevalence of mental health symptoms (anxiety and depression), yet fewer longitudinal studies have compared across background factors and other psychological variables to identify vulnerable subgroups in the general population. This study tests to what extent higher levels of schizotypal traits and paranoia are associated with mental health variables 6- and 12-months since April 2020. Over 2300 adult volunteers (18-89 years, female = 74.9%) with access to the study link online were recruited from the UK, the USA, Greece and Italy. Self-reported levels of schizotypy, paranoia, anxiety, depression, aggression, loneliness and stress from three timepoints (17 April to 13 July 2020, N1 = 1599; 17 October to 31 January 2021, N2 = 774; and 17 April to 31 July 2021, N3 = 586) were mapped using network analysis and compared across time and background variables (sex, age, income, country). Schizotypal traits and paranoia were positively associated with poorer mental health through loneliness, with no effect of age, sex, income levels, countries and timepoints. Loneliness was the most influential variable across all networks, despite overall reductions in levels of loneliness, schizotypy, paranoia and aggression during the easing of lockdown (time 3). Individuals with higher levels of schizotypal traits/paranoia reported poorer mental health outcomes than individuals in the low-trait groups. Schizotypal traits and paranoia are associated with poor mental health outcomes through self-perceived feelings of loneliness, suggesting that increasing social/community cohesion may improve individuals' mental wellbeing in the long run

Using stochastic resonance and strength training as part of a rehabilitation programme for recurrent low back pain treatment: a case study

Low back pain (LBP) is a common disabling health problem that can cause decreased spine proprioception. Stochastic resonance (SR) can influence detection performance, besides improving patients with significant sensory deficits, but have not been thoroughly tested for LBP. This study aimed to examine the application of SR therapy (SRT) and strength training for LBP treatment. The subject was a resistance-trained male in his early thirties. His back pain was unbearable after a strength training session. Standard pain relief alleviated the pain but the LBP developed at a similar intensity after 4 weeks. SRT (4–5 sets ×90 sec, 30-sec rest interval, supine position) was prescribed along with other exercises for 3 weeks (phase 1), and followed by tailor-made strength training for 16 weeks (phase 2). The Oswestry Disability Index was 66.7% (interpreted as “crippled”) prior to first SRT, and reduced to minimal levels of 15.6% and 6.7% after four and seven SRT sessions, respectively. Similarly, pain intensity was ranging from 5 to 9 (distracting-severe) of the Numeric Rating Scale (NRS-11) prior to the first session but this was reduced considerably after four sessions (NRS-11: 0–1). During phase 2, the patient performed without complaining of LBP, two repetitions of bench press exercise at a load intensity of 1.2 his body weight and attained 4 min of plank stabilisation. This LBP management strategy has a clinically meaningful effect on pain intensity, disability, and functional mobility, by receding the recurrent distracting to severe LBP

Iterative Bayesian Model Averaging: a method for the application of survival analysis to high-dimensional microarray data

<p>Abstract</p> <p>Background</p> <p>Microarray technology is increasingly used to identify potential biomarkers for cancer prognostics and diagnostics. Previously, we have developed the iterative Bayesian Model Averaging (BMA) algorithm for use in classification. Here, we extend the iterative BMA algorithm for application to survival analysis on high-dimensional microarray data. The main goal in applying survival analysis to microarray data is to determine a highly predictive model of patients' time to event (such as death, relapse, or metastasis) using a small number of selected genes. Our multivariate procedure combines the effectiveness of multiple contending models by calculating the weighted average of their posterior probability distributions. Our results demonstrate that our iterative BMA algorithm for survival analysis achieves high prediction accuracy while consistently selecting a small and cost-effective number of predictor genes.</p> <p>Results</p> <p>We applied the iterative BMA algorithm to two cancer datasets: breast cancer and diffuse large B-cell lymphoma (DLBCL) data. On the breast cancer data, the algorithm selected a total of 15 predictor genes across 84 contending models from the training data. The maximum likelihood estimates of the selected genes and the posterior probabilities of the selected models from the training data were used to divide patients in the test (or validation) dataset into high- and low-risk categories. Using the genes and models determined from the training data, we assigned patients from the test data into highly distinct risk groups (as indicated by a p-value of 7.26e-05 from the log-rank test). Moreover, we achieved comparable results using only the 5 top selected genes with 100% posterior probabilities. On the DLBCL data, our iterative BMA procedure selected a total of 25 genes across 3 contending models from the training data. Once again, we assigned the patients in the validation set to significantly distinct risk groups (p-value = 0.00139).</p> <p>Conclusion</p> <p>The strength of the iterative BMA algorithm for survival analysis lies in its ability to account for model uncertainty. The results from this study demonstrate that our procedure selects a small number of genes while eclipsing other methods in predictive performance, making it a highly accurate and cost-effective prognostic tool in the clinical setting.</p