MAINE: a web tool for multi-omics feature selection and rule-based data exploration.

SUMMARY: Patient multi-omics datasets are often characterized by a high dimensionality; however, usually only a small fraction of the features is informative, that is change in their value is directly related to the disease outcome or patient survival. In medical sciences, in addition to a robust feature selection procedure, the ability to discover human-readable patterns in the analyzed data is also desirable. To address this need, we created MAINE-Multi-omics Analysis and Exploration. The unique functionality of MAINE is the ability to discover multidimensional dependencies between the selected multi-omics features and event outcome prediction as well as patient survival probability. Learned patterns are visualized in the form of interpretable decision/survival trees and rules. AVAILABILITY AND IMPLEMENTATION: MAINE is freely available at maine.ibemag.pl as an online web application. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Sensor-Based Predictive Maintenance with Reduction of False Alarms-A Case Study in Heavy Industry.

In this paper, the problem of the identification of undesirable events is discussed. Such events can be poorly represented in the historical data, and it is predominantly impossible to learn from past examples. The discussed issue is considered in the work in the context of two use cases in which vibration and temperature measurements collected by wireless sensors are analysed. These use cases include crushers at a coal-fired power plant and gantries in a steelworks converter. The awareness, resulting from the cooperation with industry, of the need for a system that works in cold start conditions and does not flood the machine operator with alarms was the motivation for proposing a new predictive maintenance method. The proposed solution is based on the methods of outlier identification. These methods are applied to the collected data that was transformed into a multidimensional feature vector. The novelty of the proposed solution stems from the creation of a methodology for the reduction of false positive alarms, which was applied to a system identifying undesirable events. This methodology is based on the adaptation of the system to the analysed data, the interaction with the dispatcher, and the use of the XAI (eXplainable Artificial Intelligence) method. The experiments performed on several data sets showed that the proposed method reduced false alarms by 90.25% on average in relation to the performance of the stand-alone outlier detection method. The obtained results allowed for the implementation of the developed method to a system operating in a real industrial facility. The conducted research may be valuable for systems with a cold start problem where frequent alarms can lead to discouragement and disregard for the system by the user.

Learning rule sets from survival data.

BACKGROUND: Survival analysis is an important element of reasoning from data. Applied in a number of fields, it has become particularly useful in medicine to estimate the survival rate of patients on the basis of their condition, examination results, and undergoing treatment. The recent developments in the next generation sequencing open new opportunities in survival study as they allow vast amount of genome-, transcriptome-, and proteome-related features to be investigated. These include single nucleotide and structural variants, expressions of genes and microRNAs, DNA methylation, and many others. RESULTS: We present LR-Rules, a new algorithm for rule induction from survival data. It works according to the separate-and-conquer heuristics with a use of log-rank test for establishing rule body. Extensive experiments show LR-Rules to generate models of superior accuracy and comprehensibility. The detailed analysis of rules rendered by the presented algorithm on four medical datasets concerning leukemia as well as breast, lung, and thyroid cancers, reveals the ability to discover true relations between attributes and patients' survival rate. Two of the case studies incorporate features obtained with a use of high throughput technologies showing the usability of the algorithm in the analysis of bioinformatics data. CONCLUSIONS: LR-Rules is a viable alternative to existing approaches to survival analysis, particularly when the interpretability of a resulting model is crucial. Presented algorithm may be especially useful when applied on the genomic and proteomic data as it may contribute to the better understanding of the background of diseases and support their treatments.

The activity concentration of post-Chernobyl ¹³7Cs in the area of the Opole Anomaly (southern Poland).

During the years 2007 and 2010, the activity concentration of (137)Cs accumulated in soil, mosses Pleurozium schreberi and lichens Hypogymnia physodes was measured. The studies covered the areas of the so-called Opole Anomaly. In consequence of the Chernobyl nuclear power plant breakdown in 1986, relatively large amounts of this radionuclide were deposited in this area. In some areas of the Anomaly, over 100 times higher surface activity of (137)Cs was detected, compared to the lowest values registered in Poland. Currently, (137)Cs is still present in woodlands and wastelands. As at 2 April 2013, the surface activity concentration of (137)Cs in soil on the tested area was from 0.34 to 67.5 kBq m(-2). In comparison, the surface activity concentration of (137)Cs as at 1 June 1986, soon after deposition, was from 2.08 to over 125 kBq m(-2). The maximum specific activity concentrations of (137)Cs in mosses and lichens sampled for testing in 2010 were respectively 1234 and 959 Bq kg(-1). It was also proven that the changes in activity concentration of (137)Cs in the area of the Anomaly are mainly the consequence of the radioactive decay of this radionuclide.

Cholecalciferol supplementation lowers leptin and TMAO but increases NO and VEGF-A levels in obese vitamin D deficient patients: Is it one of the potential cardioprotective mechanisms of vitamin D?

BACKGROUND: Vitamin D deficiency is one of the most common health issues in developed countries. Obese patients are most at risk of having serum 25-hydroxyvitamin D3 (25(OH)D3) levels that are too low due to the accumulation of vitamin D in adipose tissue. While the effects of a deficiency on the skeletal or immune system are known, the effects on the cardiovascular system are not yet clear. Our study investigates the effect of cholecalciferol supplementation in obese patients on selected biomarkers associated with cardiovascular diseases (CVDs). METHODS: The study enrolled 33 obese patients with insufficient 25(OH)D3 levels. For three months, the subjects supplemented with cholecalciferol at a dose of 2000 IU/day. Concentrations of nitric oxide (NO), vascular endothelial growth factor A (VEGF-A), leptin, trimethylamine N-oxide (TMAO) and soluble suppression of tumorigenicity 2 (sST2) were measured in baseline samples using ELISA (BioTek EPOCH). 25(OH)D3 levels measured on Beckman Coulter DXI 800 by chemiluminescence method. RESULTS: After supplementation, 25(OH)D3 levels increased significantly. Normal levels were achieved in most patients. A statistically significant reduction leptin and TMAO levels was observed. At the same time, NO and VEGF-A levels increased statistically significantly. CONCLUSION: This study indicates that restoring normal 25(OH)D3 levels in obese people reduces the concentration of pro-inflammatory factors associated with cardiovascular diseases. Reducing inflammation and the potential impact on vascular reactivity leads to the conclusion that cholecalciferol supplementation in obese patients may benefit the cardiovascular system.

The Influence of Body Mass Index on Growth Factor Composition in the Platelet-Rich Plasma in Patients with Knee Osteoarthritis.

BACKGROUND: An abnormally high body mass index is strongly associated with knee osteoarthritis. Usually, obese patients are excluded from clinical trials involving PRP intra-articular injections. Growth factors have been demonstrated to have a disease-modifying effect on KOA treatment, even though data on their influence on treatment effectiveness in obese patients are lacking. PURPOSE: To prospectively compare the level of selected growth factors including transforming growth factor-b (TGF-ß), epidermal growth factor (EGF), fibroblast growth factor, insulin-like growth factor-1 (IGF-1), platelet-derived growth factor (PDGF), vascular endothelial growth factor (VEGF), and fibroblast growth factor-2 (FGF-2) in platelet-rich plasma (PRP) in obese patients and patients with normal BMI. METHODS: A total of 49 patients were included in the study according to inclusion and exclusion criteria. The groups strongly differed in body mass index (median values 21.6 vs. 32.15). Concentrations of growth factors were measured with an enzyme-linked immunosorbent assay. Statistical significance was determined with the Mann-Whitney U test. The compliance of the distribution of the results with the normal distribution was checked using the Shapiro-Wilk test separately for both groups. RESULTS: There were no statistically significant differences in median marker levels between groups. Statistically significant Pearson correlations were observed between IGF-1 serum level and age (weak negative, r = -0.294, p = 0.041) and gender (moderate positive, r = 0.392, 0.005). CONCLUSIONS: BMI does not influence the level of selected growth factors in patients with knee osteoarthritis. Obese and non-obese patients had similar compositions of PDGF, TGF-ß, EGF, FGF-2, IGF-1, and VEGF. PRP can be used in both groups with similar effects associated with growth factors' influence on articular cartilage.

Machine Learning Based Analysis of Relations between Antigen Expression and Genetic Aberrations in Childhood B-Cell Precursor Acute Lymphoblastic Leukaemia.

Flow cytometry technique (FC) is a standard diagnostic tool for diagnostics of B-cell precursor acute lymphoblastic leukemia (BCP-ALL) assessing the immunophenotype of blast cells. BCP-ALL is often associated with underlying genetic aberrations, that have evidenced prognostic significance and can impact the disease outcome. Since the determination of patient prognosis is already important at the initial phase of BCP-ALL diagnostics, we aimed to reveal specific genetic aberrations by finding specific multiple antigen expression patterns with FC immunophenotyping. The FC immunophenotype data were analysed using machine learning methods (gradient boosting, decision trees, classification rules). The obtained results were verified with the use of repeated cross-validation. The t(12;21)/ETV6-RUNX1 aberration occurs more often when blasts present high expression of CD10, CD38, low CD34, CD45 and specific low expression of CD81. The t(v;11q23)/KMT2A is associated with positive NG2 expression and low CD10, CD34, TdT and CD24. Hyperdiploidy is associated with CD123, CD66c and CD34 expression on blast cells. In turn, high expression of CD81, low expression of CD45, CD22 and lack of CD123 and NG2 indicates that none of the studied aberrations is present. Detecting aberrations in pediatric BCP-ALL, based on the expression of multiple markers, can be done with decent efficiency.

Data on methane concentration collected by underground coal mine sensors.

Coal mining requires working in hazardous conditions. Miners in an underground coal mine can face several threats, such as, e.g. methane explosions. To provide protection for people working underground, systems for active monitoring of production processes are typically used. One of their fundamental applications is screening dangerous gas concentrations (methane in particular) to prevent spontaneous explosions. Such a system is the source of the data set containing raw data collected at an underground coal mine. The data is collected from 28 different sensors placed at various locations around the coal mine. All the attributes except one are numeric, and the examples collected form a time series. This data set can be used in a variety of analytical tasks, including classification, regression, time series and stream data analysis.