Performance of a demand controlled mechanical extract ventilation system for dwellings

The main aim of ventilation is to guarantee a goodindoor air quality, related to the energy consumed forheating and fan(s). Active or passive heat recoverysystems seem to focus on the reduction of heatingconsumption at the expense of fan electricityconsumption and maintenance. In this study, demandcontrolledmechanical extract ventilation systems ofRenson (DCV1 and DCV2), based on natural supply inthe habitable rooms and mechanical extraction in thewet rooms (or even the bedrooms), was analysed forone year by means of multi-zone Contam simulationson a reference detached house and compared withstandard MEV and mechanical extract ventilationsystems with heat recovery (MVHR). To this end, IAQ, total energy consumption,CO2 emissions and total cost of the systems aredetermined. The results show that DCV systems withincreased supply air flow rates or direct mechanicalextract from bedrooms can significantly improve IAQ,while reducing total energy consumption comparedto MEV. Applying DCV reduces primary heatingenergy consumption and yearly fan electricityconsumption at most by 65% to 50% compared toMEV. Total operational energy costs and CO2emissions of DCV are similar when compared toMVHR. Total costs of DCV systems over 15 years aresmaller when compared to MVHR due to lowerinvestment and maintenance costs

Ontology of core data mining entities

In this article, we present OntoDM-core, an ontology of core data mining entities. OntoDM-core defines themost essential datamining entities in a three-layered ontological structure comprising of a specification, an implementation and an application layer. It provides a representational framework for the description of mining structured data, and in addition provides taxonomies of datasets, data mining tasks, generalizations, data mining algorithms and constraints, based on the type of data. OntoDM-core is designed to support a wide range of applications/use cases, such as semantic annotation of data mining algorithms, datasets and results; annotation of QSAR studies in the context of drug discovery investigations; and disambiguation of terms in text mining. The ontology has been thoroughly assessed following the practices in ontology engineering, is fully interoperable with many domain resources and is easy to extend

Multi-Target Prediction: A Unifying View on Problems and Methods

Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this paper, we present a unifying view on MTP problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research

S.cerevisiae Complex Function Prediction with Modular Multi-Relational Framework

Proceeding of: 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, Córdoba, Spain, June 1-4, 2010Determining the functions of genes is essential for understanding how the metabolisms work, and for trying to solve their malfunctions. Genes usually work in groups rather than isolated, so functions should be assigned to gene groups and not to individual genes. Moreover, the genetic knowledge has many relations and is very frequently changeable. Thus, a propositional ad-hoc approach is not appropriate to deal with the gene group function prediction domain. We propose the Modular Multi-Relational Framework (MMRF), which faces the problem from a relational and flexible point of view. The MMRF consists of several modules covering all involved domain tasks (grouping, representing and learning using computational prediction techniques). A specific application is described, including a relational representation language, where each module of MMRF is individually instantiated and refined for obtaining a prediction under specific given conditions.This research work has been supported by CICYT, TRA 2007-67374-C02-02 project and by the expert biological knowledge of the Structural Computational Biology Group in Spanish National Cancer Research Centre (CNIO). The authors would like to thank members of Tilde tool developer group in K.U.Leuven for providing their help and many useful suggestions.Publicad

Predicting gene function using hierarchical multi-label decision tree ensembles

<p>Abstract</p> <p>Background</p> <p><it>S. cerevisiae</it>, <it>A. thaliana </it>and <it>M. musculus </it>are well-studied organisms in biology and the sequencing of their genomes was completed many years ago. It is still a challenge, however, to develop methods that assign biological functions to the ORFs in these genomes automatically. Different machine learning methods have been proposed to this end, but it remains unclear which method is to be preferred in terms of predictive performance, efficiency and usability.</p> <p>Results</p> <p>We study the use of decision tree based models for predicting the multiple functions of ORFs. First, we describe an algorithm for learning hierarchical multi-label decision trees. These can simultaneously predict all the functions of an ORF, while respecting a given hierarchy of gene functions (such as FunCat or GO). We present new results obtained with this algorithm, showing that the trees found by it exhibit clearly better predictive performance than the trees found by previously described methods. Nevertheless, the predictive performance of individual trees is lower than that of some recently proposed statistical learning methods. We show that ensembles of such trees are more accurate than single trees and are competitive with state-of-the-art statistical learning and functional linkage methods. Moreover, the ensemble method is computationally efficient and easy to use.</p> <p>Conclusions</p> <p>Our results suggest that decision tree based methods are a state-of-the-art, efficient and easy-to-use approach to ORF function prediction.</p

Homologous recombination deficiency scar: mutations and beyond—implications for precision oncology

Homologous recombination deficiency (HRD) is a prevalent in approximately 17% of tumors and is associated with enhanced sensitivity to anticancer therapies inducing double-strand DNA breaks. Accurate detection of HRD would therefore allow improved patient selection and outcome of conventional and targeted anticancer therapies. However, current clinical assessment of HRD mainly relies on determining germline BRCA1/2 mutational status and is insufficient for adequate patient stratification as mechanisms of HRD occurrence extend beyond functional BRCA1/2 loss. HRD, regardless of BRCA1/2 status, is associated with specific forms of genomic and mutational signatures termed HRD scar. Detection of this HRD scar might therefore be a more reliable biomarker for HRD. This review discusses and compares different methods of assessing HRD and HRD scar, their advances into the clinic, and their potential implications for precision oncology

Role of variant allele fraction and rare SNP filtering to improve cellular DNA repair endpoint association

BackgroundLarge cancer genome studies continue to reveal new players in treatment response and tumorigenesis. The discrimination of functional alterations from the abundance of passenger genetic alterations still poses challenges and determines DNA sequence variant selection procedures. Here we evaluate variant selection strategies that select homozygous variants and rare SNPs and assess its value in detecting tumor cells with DNA repair defects.MethodsTo this end we employed a panel of 29 patient-derived head and neck squamous cell carcinoma (HNSCC) cell lines, of which a subset harbors DNA repair defects. Mitomycin C (MMC) sensitivity was used as functional endpoint of DNA crosslink repair deficiency. 556 genes including the Fanconi anemia (FA) and homologous recombination (HR) genes, whose products strongly determine MMC response, were capture-sequenced.ResultsWe show a strong association between MMC sensitivity, thus loss of DNA repair function, and the presence of homozygous and rare SNPs in the relevant FA/HR genes. Excluding such selection criteria impedes the discrimination of crosslink repair status by mutation analysis. Applied to all KEGG pathways, we find that the association with MMC sensitivity is strongest in the KEGG FA pathway, therefore also demonstrating the value of such selection strategies for exploratory analyses. Variant analyses in 56 clinical samples demonstrate that homozygous variants occur more frequently in tumor suppressor genes than oncogenes further supporting the role of a homozygosity criterion to improve gene function association or tumor suppressor gene identification studies.ConclusionTogether our data show that the detection of relevant genes or of repair pathway defected tumor cells can be improved by the consideration of allele zygosity and SNP allele frequencies