TELIOS: A Tool for the Automatic Generation of Logic Programming Machines

Abstract In this paper the tool TELIOS is presented, for the automatic generation of a hardware machine, corresponding to a given logic program. The machine is implemented using an FPGA, where a corresponding inference machine, in application specific hardware, is created on the FPGA, based on a BNF parser, to carry out the inference mechanism. The unification mechanism is based on actions embedded between the non-terminal symbols and implemented using special modules on the FPGA

Machine learning methodologies versus cardiovascular risk scores, in predicting disease risk

BACKGROUND: The use of Cardiovascular Disease (CVD) risk estimation scores in primary prevention has long been established. However, their performance still remains a matter of concern. The aim of this study was to explore the potential of using ML methodologies on CVD prediction, especially compared to established risk tool, the HellenicSCORE. METHODS: Data from the ATTICA prospective study (n = 2020 adults), enrolled during 2001-02 and followed-up in 2011-12 were used. Three different machine-learning classifiers (k-NN, random forest, and decision tree) were trained and evaluated against 10-year CVD incidence, in comparison with the HellenicSCORE tool (a calibration of the ESC SCORE). Training datasets, consisting from 16 variables to only 5 variables, were chosen, with or without bootstrapping, in an attempt to achieve the best overall performance for the machine learning classifiers. RESULTS: Depending on the classifier and the training dataset the outcome varied in efficiency but was comparable between the two methodological approaches. In particular, the HellenicSCORE showed accuracy 85%, specificity 20%, sensitivity 97%, positive predictive value 87%, and negative predictive value 58%, whereas for the machine learning methodologies, accuracy ranged from 65 to 84%, specificity from 46 to 56%, sensitivity from 67 to 89%, positive predictive value from 89 to 91%, and negative predictive value from 24 to 45%; random forest gave the best results, while the k-NN gave the poorest results. CONCLUSIONS: The alternative approach of machine learning classification produced results comparable to that of risk prediction scores and, thus, it can be used as a method of CVD prediction, taking into consideration the advantages that machine learning methodologies may offer

Ten simple rules for making training materials FAIR

Author summary: Everything we do today is becoming more and more reliant on the use of computers. The field of biology is no exception; but most biologists receive little or no formal preparation for the increasingly computational aspects of their discipline. In consequence, informal training courses are often needed to plug the gaps; and the demand for such training is growing worldwide. To meet this demand, some training programs are being expanded, and new ones are being developed. Key to both scenarios is the creation of new course materials. Rather than starting from scratch, however, it’s sometimes possible to repurpose materials that already exist. Yet finding suitable materials online can be difficult: They’re often widely scattered across the internet or hidden in their home institutions, with no systematic way to find them. This is a common problem for all digital objects. The scientific community has attempted to address this issue by developing a set of rules (which have been called the Findable, Accessible, Interoperable and Reusable [FAIR] principles) to make such objects more findable and reusable. Here, we show how to apply these rules to help make training materials easier to find, (re)use, and adapt, for the benefit of all

Four cycles of paclitaxel and carboplatin as adjuvant treatment in early-stage ovarian cancer: a six-year experience of the Hellenic Cooperative Oncology Group

BACKGROUND: Surgery can cure a significant percentage of ovarian carcinoma confined to the pelvis. Nevertheless, there is still a 10–50% recurrence rate. We administered paclitaxel/carboplatin as adjuvant treatment in early-stage ovarian carcinoma. METHODS: Patients with stages Ia or Ib, Grade 2 or 3 and Ic to IIb (any grade) were included. Patients were treated with 4 cycles of Paclitaxel 175 mg/m(2 )and Carboplatin [area under the curve (AUC) 6 (Calvert Formula)] every 3 weeks. RESULTS: Sixty-nine patients with no residual disease following cytoreductive surgery and minimal or modified surgical staging were included in this analysis. Grade 3 or 4 neutropenia occured in 29.9% of patients, while neutropenic fever was reported in 4.5%. Neurotoxicity (all Grade 1 or 2) was reported in 50% of cases. Median follow-up was 62 months. 5-year overall survival (OS) and relapse-free survival (RFS) were: 87% (95% confidence intervals [CI]: 78–96) and 79% (95% CI: 69–89), respectively. Significantly fewer patients with stages Ic-IIb and tumor grade 2 or 3 achieved a 5-year RFS than patients with only one of these two factors (73% vs 92%, p = 0.03). CONCLUSION: Paclitaxel/Carboplatin chemotherapy is a safe and effective adjuvant treatment in early-stage ovarian carcinoma. Patients with stages Ic-IIb and tumor grade 2 or 3 may benefit from more extensive treatment

Ten simple rules for making training materials FAIR.

Everything we do today is becoming more and more reliant on the use of computers. The field of biology is no exception; but most biologists receive little or no formal preparation for the increasingly computational aspects of their discipline. In consequence, informal training courses are often needed to plug the gaps; and the demand for such training is growing worldwide. To meet this demand, some training programs are being expanded, and new ones are being developed. Key to both scenarios is the creation of new course materials. Rather than starting from scratch, however, it's sometimes possible to repurpose materials that already exist. Yet finding suitable materials online can be difficult: They're often widely scattered across the internet or hidden in their home institutions, with no systematic way to find them. This is a common problem for all digital objects. The scientific community has attempted to address this issue by developing a set of rules (which have been called the Findable, Accessible, Interoperable and Reusable [FAIR] principles) to make such objects more findable and reusable. Here, we show how to apply these rules to help make training materials easier to find, (re)use, and adapt, for the benefit of all

Combining Multiple RNA-Seq Data Analysis Algorithms Using Machine Learning Improves Differential Isoform Expression Analysis

RNA sequencing has become the standard technique for high resolution genome-wide monitoring of gene expression. As such, it often comprises the first step towards understanding complex molecular mechanisms driving various phenotypes, spanning organ development to disease genesis, monitoring and progression. An advantage of RNA sequencing is its ability to capture complex transcriptomic events such as alternative splicing which results in alternate isoform abundance. At the same time, this advantage remains algorithmically and computationally challenging, especially with the emergence of even higher resolution technologies such as single-cell RNA sequencing. Although several algorithms have been proposed for the effective detection of differential isoform expression from RNA-Seq data, no widely accepted golden standards have been established. This fact is further compounded by the significant differences in the output of different algorithms when applied on the same data. In addition, many of the proposed algorithms remain scarce and poorly maintained. Driven by these challenges, we developed a novel integrative approach that effectively combines the most widely used algorithms for differential transcript and isoform analysis using state-of-the-art machine learning techniques. We demonstrate its usability by applying it on simulated data based on several organisms, and using several performance metrics; we conclude that our strategy outperforms the application of the individual algorithms. Finally, our approach is implemented as an R Shiny application, with the underlying data analysis pipelines also available as docker containers

Combining Multiple RNA-Seq Data Analysis Algorithms Using Machine Learning Improves Differential Isoform Expression Analysis

RNA sequencing has become the standard technique for high resolution genome-wide monitoring of gene expression. As such, it often comprises the first step towards understanding complex molecular mechanisms driving various phenotypes, spanning organ development to disease genesis, monitoring and progression. An advantage of RNA sequencing is its ability to capture complex transcriptomic events such as alternative splicing which results in alternate isoform abundance. At the same time, this advantage remains algorithmically and computationally challenging, especially with the emergence of even higher resolution technologies such as single-cell RNA sequencing. Although several algorithms have been proposed for the effective detection of differential isoform expression from RNA-Seq data, no widely accepted golden standards have been established. This fact is further compounded by the significant differences in the output of different algorithms when applied on the same data. In addition, many of the proposed algorithms remain scarce and poorly maintained. Driven by these challenges, we developed a novel integrative approach that effectively combines the most widely used algorithms for differential transcript and isoform analysis using state-of-the-art machine learning techniques. We demonstrate its usability by applying it on simulated data based on several organisms, and using several performance metrics; we conclude that our strategy outperforms the application of the individual algorithms. Finally, our approach is implemented as an R Shiny application, with the underlying data analysis pipelines also available as docker containers

Sociodemographic indicators of health status using a machine learning approach and data from the english longitudinal study of aging (ELSA)

Background: Studies on the effects of sociodemographic factors on health in aging now include the use of statistical models and machine learning. The aim of this study was to evaluate the determinants of health in aging using machine learning methods and to compare the accuracy with traditional methods. Material/Methods: The health status of 6,209 adults, age <65 years (n=1,585), 65–79 years (n=3,267), and >80 years (n=1,357) were measured using an established health metric (0–100) that incorporated physical function and activities of daily living (ADL). Data from the English Longitudinal Study of Ageing (ELSA) included socio-economic and sociodemographic characteristics and history of falls. Health-trend and personal-fitted variables were generated as predictors of health metrics using three machine learning methods, random forest (RF), deep learning (DL) and the linear model (LM), with calculation of the percentage increase in mean square error (%IncMSE) as a measure of the importance of a given predictive variable, when the variable was removed from the model. Results: Health-trend, physical activity, and personal-fitted variables were the main predictors of health, with the%incMSE of 85.76%, 63.40%, and 46.71%, respectively. Age, employment status, alcohol consumption, and household income had the%incMSE of 20.40%, 20.10%, 16.94%, and 13.61%, respectively. Performance of the RF method was similar to the traditional LM (p=0.7), but RF significantly outperformed DL (p=0.006). Conclusions: Machine learning methods can be used to evaluate multidimensional longitudinal health data and may provide accurate results with fewer requirements when compared with traditional statistical modeling.The ATHLOS project has received funding from the European Union Horizon 2020 Research and Innovation Program under grant agreement No. 635316 (EU HORIZON2020-PHC-635316