As the internet is increasingly embedded in the everyday things in our homes, we notice a need for greater focus on the role care plays in those relationships—and therefore an opportunity to realize unseen potential in reimagining home Internet of Things (IoT). In this paper we report on our inquiry of home dwellers’ relationships to caring for their everyday things and homes (referred to as thingcare). Findings from our design ethnography reveal four thematic qualities of their relationships to thingcare: Care Spectacle, Care Liminality, Ontological Braiding, and Care Condition. Using these themes as touchstones, we co-speculated to produce four speculative IoT concepts to explore what care as a design ethic might look like for IoT and reflect on nascent opportunities and challenges for domestic IoT design. We conclude by considering structures of power and privilege embedded within care practices that critically open new design imaginaries for IoT.

Abstract The discovery of new medications in a cost-effective manner has become the top priority for many pharmaceutical companies. Despite decades of innovation, many of their processes arguably remain relatively inefficient. One such process is the prediction of biological activity. This paper describes a new deep learning model, capable of conducting a preliminary screening of chemical compounds in-silico. The model has been constructed using a variation autoencoder to generate chemical compound fingerprints, which have been used to create a regression model to predict their LogD property and a classification model to predict binding in selected assays from the ChEMBL dataset. The conducted experiments demonstrate accurate prediction of the properties of chemical compounds only using structural definitions and also provide several opportunities to improve upon this model in the future.

Metagenomics is proliferating for studying environmental microbial communities and their role in animal functions. This paper aims to study the role of functions of microbial communities present in cattle (Bos taurus) and their relation to dietary supplement usage. The functional study was conducted as part of the EU H2020 MetaPlat project11MetaPlat, http://www.metaplat.eu. In this research, we proposed a novel phylogeny-driven approach to classify 16S rRNA samples from cattle rumen microbiome and relate them to the functional phenotype of diet (referred to as functional analysis). Phylogeny covers biological relationships from different taxonomical levels combined with their respective evolutionary measures. We performed this analysis by proposing a novel method based on phylogeny-adjusted distance-based indices. These indices are used in ranking microbial feature space derived from the topology of the phylogenetic tree. The integrative approach incorporating phylogeny into feature engineering as part of machine learning (ML) modeling, achieved high predictive performance with Accuracy of 0.962 and Kappa of 0.950 for classifying cattle microbiome into the phenotype of a diet supplemented with oil, nitrate, combined (with oil and nitrate) and controls.

There has been an exponential growth in the performance and output of sequencing technologies (omics data) with full genome sequencing now producing gigabases of reads on a daily basis. These data may hold the promise of personalized medicine, leading to routinely available sequencing tests that can guide patient treatment decisions. In the era of high-throughput sequencing (HTS), computational considerations, data governance and clinical translation are the greatest rate-limiting steps. To ensure that the analysis, management and interpretation of such extensive omics data is exploited to its full potential, key factors, including sample sourcing, technology selection and computational expertise and resources, need to be considered, leading to an integrated set of high-performance tools and systems. This article provides an up-to-date overview of the evolution of HTS and the accompanying tools, infrastructure and data management approaches that are emerging in this space, which, if used within in a multidisciplinary context, may ultimately facilitate the development of personalized medicine.

"Metagenomics" is the study of genomic sequences obtained directly from environmental microbial communities with the aim to linking their structures with functional roles. The field has been aided in the unprecedented advancement through high-throughput omics data sequencing. The outcome of sequencing are biologically rich data sets. Metagenomic data consisting of microbial species which outnumber microbial samples, lead to the "curse of dimensionality" in datasets. Hence, the focus in metagenomics studies has moved towards developing efficient computational models using Machine Learning (ML), reducing the computational cost. In this paper, we comprehensively assessed various ML approaches to classifying high-dimensional human microbiota effectively into their functional phenotypes. We propose the application of embedded feature selection methods, namely, Extreme Gradient Boosting and Penalized Logistic Regression to determine important microbial species. The resultant feature set enhanced the performance of one of the most popular state-of-the-art methods, Random Forest (RF) over metagenomic studies. Experimental results indicate that the proposed method achieved best results in terms of accuracy, area under the Receiver Operating Characteristic curve (ROC-AUC), and major improvement in processing time. It outperformed other feature selection methods of filters or wrappers over RF and classifiers such as Support Vector Machine (SVM), Extreme Learning Machine (ELM), and k- Nearest Neighbors (k-NN).

Metagenomics is an unobtrusive science of studying uncultivated microbes sampled directly from an environment, e.g. soil, ocean, air, human body, or animals, etc. Functional metagenomics particularly deals with linking microbes to environmental derivations, such as classifying the role of human gut microbiome into a diseased or non-diseased state. Ongoing research in this area includes analyzing the structure of microbial communities, and relate it to functional analysis. We present an integrative experimental framework for functional metagenomics, including data driven (abundance count of microbial species) and knowledge driven (phylogenetic tree structure) contexts. Our related experiments, indicate that i) feature selection improves the performance of classifying human microbiome samples, ii) the classification of human microbiome remains a challenging problem while incorporating phylogenetic structures. For example, our best accuracy attained on the Costello body site (CBH) dataset with forehead and external ear as body sites, is 89.13 % with a non-phylogenetic model, and 78.26 % with a phylogenetic model. This forms a potential research direction of further exploration of space for incorporating phylogeny in microbial analysis and hence developing integrative computational models for deriving functional phenotypes, based on metagenomic sequencing data.

Metagenomics involves the genetic analysis of microbial DNA extracted from communities in an environment sample. Advent and falling costs of next-generation sequencing technologies has accelerated metagenomics research providing an improved understanding of microbial communities. In this study we investigate if the traits methane production and feed conversion rates in the rumen microbial community overlap with top genes ranked by topological metrics in a co-abundance network. A co-abundance network was constructed from abundance values of 1570 microbial genes in rumen samples of 8 cattle identified in a metagenomics study at the Beef and Sheep Research Centre of Scotland's Rural College. We used 4 different topological measures: Degree Centrality, Betweenness Centrality, Bonacich Power Centrality and PageRank to the network. Using permutation testing, we discovered, methane production trait genes significantly overlapped with top ranked genes obtained using the metrics PageRank and Bonacich Power Centrality. Feed conversion trait genes overlapped with top ranked genes using Bonacich Power Centrality and Betweenness. Furthermore, we observed the top ranked genes from PageRank and Bonacich Power Centrality significantly overlapped with genes involved in the KEGG methane metabolism pathway and ranked highly key methanogenesis genes such as mcrA and fmdB. Identified functional clusters containing most methane and feed conversion genes were also analyzed in terms of overlap with top ranked genes from topological metrics.

We have reached the era of full genome sequencing using high throughput sequencing technologies pouring out gigabases of reads in a day. To fully benefit from such a profusion of data high performance tools and systems are needed to extract the information lying within the sequences. This paper provides an overview of the evolution of high-throughput sequencing and the tools, infrastructure and data management developing in this space to support a key area in personalized medicine. The paper concludes by providing an outlook in the future of such technologies and their applications and how they might shape clinical governance.

A better understanding of the composition of rumen microbial communities and the association between host genetic and microbial activities has important applications and implication in bioscience. Being capable of revealing the full extent of microbial gene diversity, metagenomics-based approaches hold great promises in this endeavor. This study investigates the rumen microbial community in cattle through the integration of metagenomic and network-based approaches. Based on the relative abundance of 1570 microbial genes identified in a metagenomics analysis, the co-abundance network was constructed and functional modules of microbial genes were identified. One of the main contributions of this study is to develop a random matrix theory-based approach to automatically determine the correlation threshold used to construct the co-abundance network. It has been shown that the network exhibits a highly modular structure with each of the three main modules well separated. The involvement of KEGG pathways in each module was analysed. A close look at the abundance profiles highlights that Module B is strongly associated with methane emissions while Module C is highly enriched with microbial genes associated with feed conversion efficiency.

The analysis of biological markers (biomarkers) have the ability to improve clinical outcomes of a disease through prediction and early detection. The constant improvement of next generation sequencing (NGS) technologies coupled with falling equipment price are driving research and application especially in the medical domain. NGS technology is being applied to cancer research promising greater understanding of carcinogenesis. However, as sequence capacity grows, algorithmic speed is becoming an important bottleneck. To understand these challenges we present a review on difficulties currently faced in discovering biomarkers. The review places the spotlight on sequencing technologies and the bottlenecks encountered in these pipelines. Cloud computing and high performance computing technologies such as Grid are summarized in tackling computational challenges presented by technologies and algorithms used in biomarker research.

Large-scale, high-throughput technologies and genome-wide studies have been pivotal in the identification of disease-gene candidates from patient cohorts. Output from these studies often result in gene candidate lists which are large in size. Therefore, there is a pressing need for computational tools to integrate heterogeneous data and prioritize disease-gene candidates for further experimental investigation. To address this need, we propose a computational pipeline for the prioritization of disease-gene candidates. Our pipeline integrates diverse heterogeneous data including: gene-expression, protein-protein interaction network, ontology-based similarity and betweenness measures. Furthermore, we incorporate tissue-specific gene expression data into the evaluation section of our approach. The pipeline was applied to prioritize Alzheimer's Disease (AD) genes, whereby a list of 31 prioritized genes was generated. This approach correctly identified key AD susceptible genes: INPP5D and PSEN1. Biological process enrichment analysis revealed the prioritized genes are modulated in AD pathogenesis including: regulation of neurogenesis and generation of neurons. KEGG pathway analysis identified significant hub involvement in the Neurotrophin signaling and Huntington Disease pathways. Furthermore, our evaluation demonstrated a relatively high predictive performance (AUC: 0.73) when classifying AD and normal gene expression profiles from individuals using leave-one-out cross validation. This work provides a foundation for future investigation of diverse heterogeneous data integration for disease-gene prioritization.

Recently, scientists start to examine the dynamics of biological networks from a control theory perspective. Based on the determination of minimum dominating sets (MDSets), this paper investigated the properties associated with MDSet proteins in the context of the analysis of protein interaction networks specific to the yeast cell cycle. Statistically significant differences between MDSet and non-MDSet proteins were observed in terms of topological features, Gene Ontology-driven semantic similarities, and the number of protein domains associated with each protein. However, unlike previous studies, MDSet proteins were found to be enriched with essential genes. Furthermore, we constructed and analyzed a PPI network specific to the human cell cycle and highlighted that the distinction between MDSet and non-MDSet proteins is far more complex than that observed in yeast. The system used to determine a minimum dominating set in a protein interaction network was implemented as a user-friendly Java-based plugin for Cytoscape.

Decision makers are required to make critical decisions throughout all stages of a life-cycle in large-scale projects. These decisions are important as they impact upon the outcome and the success of projects. In this paper we present an evidential reasoning framework to aid decision-makers in the decision making process. This approach utilizes the Dezert-Smarandache Theory (DSm) to fuse heterogeneous evidence sources that suffer from levels of uncertainty, imprecision and conflicts to provide beliefs for decision options. To analyze the impact that source reliability and priority has upon the decision making process a reliability discounting technique along with a priority discounting technique are applied. Application of the evidential reasoning framework is illustrated using a Case Study based in the Aerospace domain.

This paper investigates the integration of functional genomic data for the prediction of protein-protein interactions (PPI) in Saccharomyces cerevisiae. A previous benchmark study observed a marginal increase in predictive power when integrating diverse features. Classification performance was evaluated using the Receiver Operating Characteristic (ROC) curve. In this study we propose the implementation of a likelihood ratio based Bayesian classifier to reassess the limits of genomic integration. The classifier combines seven genomic features ranging from co-expression to essentiality. Due to the imbalance of the dataset in this study, ROC curves may present an overly optimistic view of the classification performance. We use the true positive/false positive (TP/FP) rate and sensitivity as comparative predictive measures to the ROC curve. Predicted interactions are verified using a Gold Standard constructed from the Munich Database of Interacting Proteins Complex Catalogue. Using the measures TP/FP and sensitivity, a clear increase in classification performance was observed with the integration of features. This framework could be extended to the analysis of PPI in more complex organisms such as Drosophila melanogaster and Homo sapiens.

This paper evaluates three classification techniques: Naive Bayesian (NB), multilayer perceptron (MLP) and K-nearest neighbour (KNN) that integrate diverse, large-scale functional data to infer pairwise (PW) and module-based (MB) interaction networks in Saccharomyces cerevisiae. Existing multi-source functional data from S. cerevisiae were merged and transformed to construct MB datasets. The results indicate that selection of a classifier depends upon the specific PPI classification problem. Feature integration and encoding methods proposed significantly impact the predictive performance of the classifiers. Generation of PPI maps for S. cerevisiae and beyond will be improved with new, high-quality, large-scale datasets with increased interactome coverage and the integration of classification methods.