Motivation: The existence of complex subpopulations of miRNA isoforms, or isomiRs, is well established. While many tools exist for investigating isomiR populations, they differ in how they characterize an isomiR, making it difficult to compare results across different tools. Thus, there is a need for a more comprehensive and systematic standard for defining isomiRs. Such a standard would allow investigation of isomiR population structure in progressively more refined sub-populations, permitting the identification of more subtle changes between conditions and leading to an improved understanding of the processes that generate these differences. Results: We developed Jasmine, a software tool that incorporates a hierarchal framework for characterizing isomiR populations. Jasmine is a Java application that can process raw read data in fastq/fasta format, or mapped reads in SAM format to produce a detailed characterization of isomiR populations. Thus, Jasmine can reveal structure not apparent in a standard miRNA-Seq analysis pipeline. Availability and implementation: Jasmine is implemented in Java and R and freely available at bitbucket https://bit bucket.org/bipous/jasmine/src/master/. Contact: simon.rayner@medisin.uio.no Supplementary information: Supplementary data are available at Bioinformatics online.

Despite the broad variety of available microRNA (miRNA) prediction tools, their application to the discovery and annotation of novel miRNA genes in domestic species is still limited. In this study we designed a comprehensive pipeline (eMIRNA) for miRNA identification in the yet poorly annotated porcine genome and demonstrated the usefulness of implementing a motif search positional refinement strategy for the accurate determination of precursor miRNA boundaries. The small RNA fraction from gluteus medius skeletal muscle of 48 Duroc gilts was sequenced and used for the prediction of novel miRNA loci. Additionally, we selected the human miRNA annotation for a homology-based search of porcine miRNAs with orthologous genes in the human genome. A total of 20 novel expressed miRNAs were identified in the porcine muscle transcriptome and 27 additional novel porcine miRNAs were also detected by homology-based search using the human miRNA annotation. The existence of three selected novel miRNAs (ssc-miR-483, ssc-miR484 and ssc-miR-200a) was further confirmed by reverse transcription quantitative real-time PCR analyses in the muscle and liver tissues of Göttingen minipigs. In summary, the eMIRNA pipeline presented in the current work allowed us to expand the catalogue of porcine miRNAs and showed better performance than other commonly used miRNA prediction approaches. More importantly, the flexibility of our pipeline makes possible its application in other yet poorly annotated non-model species.

MOTIVATION:The existence of complex subpopulations of miRNA isoforms, or isomiRs, is well established. While many tools exist for investigating isomiR populations, they differ in how they characterize an isomiR, making it difficult to compare results across different tools. Thus, there is a need for a more comprehensive and systematic standard for defining isomiRs. Such a standard would allow investigation of isomiR population structure in progressively more refined sub-populations, permitting the identification of more subtle changes between conditions and leading to an improved understanding of the processes that generate these differences. RESULTS:We developed Jasmine, a software tool that incorporates a hierarchal framework for characterizing isomiR populations. Jasmine is a Java application that can process raw read data in fastq/fasta format, or mapped reads in SAM format to produce a detailed characterization of isomiR populations. Thus, Jasmine can reveal structure not apparent in a standard miRNA-Seq analysis pipeline. AVAILABILITY:Jasmine is implemented in Java and R and freely available at bitbucket https://bitbucket.org/bipous/jasmine/src/master/. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

Resumen del trabajo presentado a la 37 International Society for Animal Genetics Conference (ISAG), celebrada en Lleida (Espana) del 7 al 12 de julio de 2019.

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression by binding to partially complementary regions within the 3'UTR of their target genes. Computational methods play an important role in target prediction and assume that the miRNA "seed region" (nt 2 to 8) is required for functional targeting, but typically only identify ∼80% of known bindings. Recent studies have highlighted a role for the entire miRNA, suggesting that a more flexible methodology is needed. We present a novel approach for miRNA target prediction based on Deep Learning (DL) which, rather than incorporating any knowledge (such as seed regions), investigates the entire miRNA and 3'TR mRNA nucleotides to learn a uninhibited set of feature descriptors related to the targeting process. We collected more than 150,000 experimentally validated homo sapiens miRNA:gene targets and cross referenced them with different CLIP-Seq, CLASH and iPAR-CLIP datasets to obtain ∼20,000 validated miRNA:gene exact target sites. Using this data, we implemented and trained a deep neural network-composed of autoencoders and a feed-forward network-able to automatically learn features describing miRNA-mRNA interactions and assess functionality. Predictions were then refined using information such as site location or site accessibility energy. In a comparison using independent datasets, our DL approach consistently outperformed existing prediction methods, recognizing the seed region as a common feature in the targeting process, but also identifying the role of pairings outside this region. Thermodynamic analysis also suggests that site accessibility plays a role in targeting but that it cannot be used as a sole indicator for functionality. Data and source code available at: https://bitbucket.org/account/user/bipous/projects/MIRAW.

The appearance of new wearable and non-invasive sensors is providing new and disruptive solutions for the monitoring of patients, disease management and follow-up of treatment adherence, and opens the door to the appearance of new and more personalized treatment methods. A sensor is a device that measures a physical quantity and transforms it into a digital signal. It provides great amounts of continuous raw measurements that can be difficult to interpret by physicians or nurses. Hence, processing the raw measurements into potential clinical findings and biomarkers (towards the digital phenotyping) is becoming a key issue. In that regard, artificial intelligence techniques play an important role. Nevertheless, the huge amount of existing methods requires skilled people able to identify the suitable knowledge representation to be used, the most appropriate machine learning technique, which in turn depend on the kind of data available, the task to be performed (e.g. diagnosing or treatment), and how the quality of the learned findings will be measured.

The kids of European and occidental countries are threatened by obesity. They are potential persons to become chronic patients. mHealth technology can help them to change their nutrition and physical activity habits. This paper presents MATCHuP, a platform that involves several agents (kids, parents, healthcare providers) that collaborate and compete by games in a social network in order to create a enjoyable environment to promote a behavioural change towards a healthier life.

Lower-limb fracture surgery is one of the major causes for autonomy loss among aged people. For care institutions, tackling with an optimized rehabilitation process is a key factor as it improves both the patients quality of life and the associated costs of the after surgery process.This paper presents bag-of-steps, a new methodology to predict the rehabilitation length and discharge date of a patient using insole force sensors and a predictive model based on the bag-of-words technique. The sensors information is used to characterize the patients gait creating a set of step descriptors. This descriptors are later used to define a vocabulary of steps using a clustering method. The vocabulary is used to describe rehabilitation sessions which are finally entered to a classifier that performs the final rehabilitation estimation. The methodology has been tested using real data from patients that underwent surgery after a lower-limb fracture.

This research project has been partially funded through BRUdG Scholarship of the University of Girona granted to Ferran Torrent-Fontbona. Work developed with the support of the research group SITES awarded with distinction by the Generalitat de Catalunya (SGR 2014-2016) and the MESC project funded by the Spanish MINECO (Ref. DPI2013- 47450-C2-1-R)

Multi-attribute resource allocation problems involve the allocation of resources on the basis of several attributes, therefore, the definition of a fairness method for this kind of auctions should be formulated from a multi-dimensional perspective. Under such point of view, fairness should take into account all the attributes involved in the allocation problem, since focusing on just a single attribute may compromise the allocations regarding the remainder attributes (e.g. incurring in delayed or bad quality tasks). In this paper, we present a multi-dimensional fairness approach based on priorities. For that purpose, a recurrent auction scenario is assumed, in which the auctioneer keeps track of winner and losers. From that information, the priority methods are defined based on the lost auctions number, the number of consecutive loses, and the fitness of their loser bids. Moreover, some methods contain a probabilistic parameter that enables handling wealth ranking disorders due to fairness. We test our approach in real-data based simulator which emulates an industrial production environment where several resource providers compete to perform different tasks. The results pointed that multi-dimensional fairness incentives agents to remain in the market whilst it improves the equity of the wealth distribution without compromising the quality of the allocation attributes.

Resource and task allocation for workflows poses an allocation problem in which several attributes may be involved (economic cost, delivery time, CO2 emissions...), therefore, it must be treated from a multi-criteria perspective so that all of the attributes are taken into account when deciding the optimal assignments. Auction mechanisms offer the chance to allocate resources and services in a competitive market environment whilst optimizing outcomes for all of the participants. In this thesis, we propose the use of multi-attribute auctions for allocating resources to workflows occurring in dynamic environments where task performance is uncertain. To this end, we present an auction mechanism for allocationg multi-attribute tasks and resources in the workflow domain (PUMAA), a framework for customizing the outcomes of the auctions depending on the domain particularities (FMAAC) and a multi-dimensional fairness mechanism for favouring egalitarian allocations

In the recent years, there has been an increasing interest in ubiquitous computing. This paradigm is based on the idea that software should act according to the context where it is executed in what is known as context-awareness. The goal of this paper is to integrate context-awareness into case-based reasoning (CBR). To this end we propose thee methods which condition the retrieval and the reuse of information in CBR depending on the context of the query case. The methodology is tested using a breast-cancer diagnose database enriched with geospatial context. Results show that context-awareness can improve CBR.

Context information has been proved to enhance user’s experience in mobile apps. In this paper we analyze the management of such information in health care apps, paying special attention to health recommenders and health monitoring applications. The paper first describes the kind of context which can be included in healthcare apps (geographical and temporal, environmental, and source related). Then, it discusses how this information can be handled in order to improve the outputs of the applications and their reasoning modules; to illustrate that we describe how context can be integrated into a well-known reasoning methodology such as knowledge-based reasoning. As a result, we describe how context is handled in an app for remote premature-baby monitoring.

Nowadays business process management is becoming a fundamental piece of many industrial processes. To manage the evolution and interactions between the business actions it is important to accurately model the steps to follow and the resources needed by a process. Workflows provide a way of describing the order of execution and the dependencies between the constituting activities of business processes. Workflow monitoring can help to improve and avoid delays in industrial environments where concurrent processes are carried out. In this article a new Petri net extension for modelling workflow activities together with their required resources is presented: resource-aware Petri nets (RAPN). An intelligent workflow management system for process monitoring and delay prediction is also introduced. Resource aware-Petri nets include time and resources within the classical Petri net workflow representation, facilitating the task of modelling and monitoring workflows. The workflow management system monitors the execution of workflows and detects possible delays using RAPN. In order to test this new approach, different services from a medical maintenance environment have been modelled and simulated.

Multi-attribute auctions allow agents to sell and purchase goods and services taking into account more attributes than just price (e.g. service time, tolerances, qualities, etc.). In this paper we analyze attributes involved during the auction process and propose to classify them between verifiable attributes, unverifiable attributes and auctioneer provided attributes. According to this classification we present VMA2, a new Vickrey-based reverse multi-attribute auction mechanism, which takes into account the different types of attributes involved in the auction and allows the auction customization in order to suit the auctioneer needs. On the one hand, the use of auctioneer provided attributes enables the inclusion of different auction concepts, such as social welfare, trust or robustness whilst, on the other hand, the use of verifiable attributes guarantee truthful bidding. The paper exemplifies the behavior of VMA2 describing how an egalitarian allocation can be achieved. The mechanism is then tested in a simulated manufacturing environment and compared with other existing auction allocation methods. (C) 2014 Elsevier Ltd. All rights reserved.

Social concerns about the environment and global warming suggest that industries must focus on reducing energy consumption, due to its social impact and changing laws. Furthermore, the smart grid will bring time-dependent tariffs that pose new challenges to the optimisation of resource allocation. In this paper we address the problem of optimising energy consumption in manufacturing processes by means of multi-attribute combinatorial auctions, so that resource price, delivery time, and energy consumed (and therefore environmental impact) are minimised. The proposed mechanism is tested with simulated data based on real examples, showing the impact of incorporating energy into task allocation problems. It is then compared with a sequential auction method.

The use of family information is a key issue to deal with inheritance illnesses. This kind of information use to come in the form of pedigree files, which contain structured information as tree or graphs, which explains the family relationships. Knowledge-based systems should incorporate the information gathered by pedigree tools to assess medical decision making. In this paper, we propose a method to achieve such a goal, which consists on the definition of new indicators, and methods and rules to compute them from family trees. The method is illustrated with several case studies. We provide information about its implementation and integration on a case-based reasoning tool. The method has been experimentally tested with breast cancer diagnosis data. The results show the feasibility of our methodology.

In this work we propose a user-friendly medically oriented tool for prognosis development systems and experimentation under a case-based reasoning methodology. The tool enables health care collaboration practice to be mapped in cases where different doctors share their expertise, for example, or where medical committee composed of specialists from different fields work together to achieve a final prognosis. Each agent with a different piece of knowledge classifies the given cases through metrics designed for this purpose. Since multiple solutions for the same case are useless, agents collaborate among themselves in order to achieve a final decision through a coordinated schema. For this purpose, the tool provides a weighted voting schema and an evolutionary algorithm (genetic algorithm) to learn robust weights. Moreover, to test the experiments, the tool includes stratified cross-validation methods which take the collaborative environment into account. In this paper the different collaborative facilities offered by the tool are described. A sample usage of the tool is also provided.

Demonstrating the incentive compatibility of an auction mechanism is always a hard but essential work in auction mechanism design. In this paper we discuss three different approaches to proof or check such property in regard of a multi-attribute auction mechanism: by analyzing well-known sufficient conditions, by mathematical analyzing the rules that govern the mechanism, and by empirically checking the mechanism. Particularly, for dealing with the second approach, we propose a new method which consists on seeking for a counterexample with a constraint solver.