Dimensionality reduction of the data set representation for the construction of the quantitative structure-activity relationship classification models is an important research subject for the ...interpretability of the models and the computational cost efficiency of the classification algorithms. Feature selection techniques are appropriate as only a short number of relevant features should be used in the classification process because irrelevant and redundant features should be discarded, the same as the noninterpretable ones. In this paper, we propose an embedded feature selection technique for the construction of classification models using the rivality index neighborhood (RINH) algorithm. This technique uses a filter selection in the preprocessing stage considering the selectivity of the features as a selection criterion and a wrapper technique in the processing stage based on the improvement of the accuracy and reliability of the models generated using the RINH algorithm with LTN and GTN functions. The results obtained using the RINH algorithm with and without the selection of features and compared with those results obtained using 14 machine learning algorithms have demonstrated that the feature selection technique proposed in this paper is capable of clearly building more accurate and reliable models, reducing the data dimensionality around 90%, and generating high robust and interpretable models.
The reliability of a QSAR classification model depends on its capacity to achieve confident predictions of new compounds not considered in the building of the model. The results of this external ...validation process show the applicability domain (AD) of the QSAR model and, therefore, the robustness of the model to predict the property/activity of new molecules. In this paper we propose the use of the rivality and modelability indexes for the study of the characteristics of the datasets to be correctly modeled by a QSAR algorithm and to predict the reliability of the built model to prognosticate the property/activity of new molecules. The calculation of these indexes has a very low computational cost, not requiring the building of a model, thus being good tools for the analysis of the datasets in the first stages of the building of QSAR classification models. In our study, we have selected two benchmark datasets with similar number of molecules but with very different modelability and we have corroborated the capacity of the predictability of the rivality and modelability indexes regarding the classification models built using Support Vector Machine and Random Forest algorithms with 5-fold cross-validation and leave-one-out techniques. The results have shown the excellent ability of both indexes to predict outliers and the applicability domain of the QSAR classification models. In all cases, these values accurately predicted the statistic parameters of the QSAR models generated by the algorithms.
The current social impact of new technologies has produced major changes in all areas of society, creating the concept of a smart city supported by an electronic infrastructure, telecommunications ...and information technology. This paper presents a review of Bluetooth Low Energy (BLE), Near Field Communication (NFC) and Visible Light Communication (VLC) and their use and influence within different areas of the development of the smart city. The document also presents a review of Big Data Solutions for the management of information and the extraction of knowledge in an environment where things are connected by an "Internet of Things" (IoT) network. Lastly, we present how these technologies can be combined together to benefit the development of the smart city.
The appropriate selection of a chemical space represented by the data set, the selection of its chemical data representation, the development of a correct modeling process using a robust and ...reproducible algorithm, and the performance of an exhaustive training and external validation determine the usability and reproducibility of a quantitative structure-activity relationship (QSAR) classification model. In this paper, we show that the use of relative versus absolute data in the representation of the data sets produces better classification models when the other processes are not modified. Relative data considers a reference frame to measure the chemical characteristics involved in the classification model, refining the data set representation and smoothing the lack of chemical information. Three data sets with different characteristics have been used in this study, and classifications models have been built applying the support vector machine algorithm. For randomly selected training and test sets, values of accuracy and area under the receiver operating characteristic curve close to 100% have been obtained for the generation of the models and external validations in all cases.
Background. Several series predicting the prognosis of staphylococcal prosthetic joint infection (PJI) managed with debridement, antibiotics, and implant retention (DAIR) have been published, but ...some of their conclusions are controversial. At present, little is known regarding the efficacy of the different antibiotics that are used or their ability to eliminate methicillin-resistant S. aureus (MRSA) infection. Methods. This was a retrospective, multicenter, observational study of cases of PJI by S. aureus that were managed with DAIR (2003–2010). Cases were classified as failures when infection persistence/relapse, death, need for salvage therapy, or prosthesis removal occurred. The parameters that predicted failure were analyzed with logistic and Cox regression. Results. Out of 345 episodes (41% men, 73 years), 81 episodes were caused by MRSA. Fifty-two were hematogenous, with poorer prognoses, and 88% were caused by methicillin-susceptible S. aureus (MSSA). Antibiotics were used for a median of 93 days, with similar use of rifampin-based combinations in MSSA- and MRSA-PJI. Failure occurred in 45% of episodes, often early after debridement. The median survival time was 1257 days. There were no overall prognostic differences between MSSA- and MRSA-PJI, but there was a higher incidence of MRSA-PJI treatment failure during the period of treatment (HR 2.34), while there was a higher incidence of MSSA-PJI treatment failure after therapy. Rifampin-based combinations exhibited an independent protective effect. Other independent predictors of outcome were polymicrobial, inflammatory, and bacteremic infections requiring more than 1 debridement, immunosuppressive therapy, and the exchange of removable components of the prosthesis. Conclusions. This is the largest series of PJI by S. aureus managed with DAIR reported to date. The success rate was 55%. The use of rifampin may have contributed to homogenizing MSSA and MRSA prognoses, although the specific rifampin combinations may have had different efficacies.
Relative distance matrixes represent measurements of the structural characteristics of the molecules, having into account a reference pattern common to the whole data set considered in the ...development of QSAR regression models. These matrixes store relationships between the data set molecules, measuring the transformation cost between pairs of molecules and a pattern from the common fragments to the entire data set. These measurements are quite related with the activity value changes and, therefore, its use allows the building of robust QSAR regression models. In this paper, we describe the building of relative distance matrixes for the representation of two data sets with clearly different characteristics and previously used as benchmark. Applying Support Vector machine algorithms, several training models and external validation were carried out randomly selecting both sets. The results obtained with correlation coefficient greater than 0.9, low values of error and values of slope and bias close to the ideality have shown the goodness of the presented proposal, clearly improving the results obtained in the literature.
•Molecules' data set representation using relative distance measurements.•Robust QSAR regression models using fingerprint patterns referenced distances.•Correlation of distance measurements between weighted fingerprints with the activity of molecules.•Use of linear Support Vector Machine for the development of QSAR regression models.•Advantages of relative versus absolute measurements in the building of QSAR prediction models.
Fungi in Bronchiectasis: A Concise Review Máiz, Luis; Nieto, Rosa; Cantón, Rafael ...
International journal of molecular sciences,
01/2018, Letnik:
19, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Although the spectrum of fungal pathology has been studied extensively in immunosuppressed patients, little is known about the epidemiology, risk factors, and management of fungal infections in ...chronic pulmonary diseases like bronchiectasis. In bronchiectasis patients, deteriorated mucociliary clearance-generally due to prior colonization by bacterial pathogens-and thick mucosity propitiate, the persistence of fungal spores in the respiratory tract. The most prevalent fungi in these patients are
and
; these are almost always isolated with bacterial pathogens like
and
, making very difficult to define their clinical significance. Analysis of the mycobiome enables us to detect a greater diversity of microorganisms than with conventional cultures. The results have shown a reduced fungal diversity in most chronic respiratory diseases, and that this finding correlates with poorer lung function. Increased knowledge of both the mycobiome and the complex interactions between the fungal, viral, and bacterial microbiota, including mycobacteria, will further our understanding of the mycobiome's relationship with the pathogeny of bronchiectasis and the development of innovative therapies to combat it.
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