HypeRS: Building a Hypergraph-driven ensemble Recommender System

Recommender systems are designed to predict user preferences over collections of items. These systems process users' previous interactions to decide which items should be ranked higher to satisfy their desires. An ensemble recommender system can achieve great recommendation performance by effectively combining the decisions generated by individual models. In this paper, we propose a novel ensemble recommender system that combines predictions made by different models into a unified hypergraph ranking framework. This is the first time that hypergraph ranking has been employed to model an ensemble of recommender systems. Hypergraphs are generalizations of graphs where multiple vertices can be connected via hyperedges, efficiently modeling high-order relations. We differentiate real and predicted connections between users and items by assigning different hyperedge weights to individual recommender systems. We perform experiments using four datasets from the fields of movie, music and news media recommendation. The obtained results show that the ensemble hypergraph ranking method generates more accurate recommendations compared to the individual models and a weighted hybrid approach. The assignment of different hyperedge weights to the ensemble hypergraph further improves the performance compared to a setting with identical hyperedge weights

Deep tree-ensembles for multi-output prediction

Recently, deep neural networks have expanded the state-of-art in various scientific fields and provided solutions to long standing problems across multiple application domains. Nevertheless, they also suffer from weaknesses since their optimal performance depends on massive amounts of training data and the tuning of an extended number of parameters. As a countermeasure, some deep-forest methods have been recently proposed, as efficient and low-scale solutions. Despite that, these approaches simply employ label classification probabilities as induced features and primarily focus on traditional classification and regression tasks, leaving multi-output prediction under-explored. Moreover, recent work has demonstrated that tree-embeddings are highly representative, especially in structured output prediction. In this direction, we propose a novel deep tree-ensemble (DTE) model, where every layer enriches the original feature set with a representation learning component based on tree-embeddings. In this paper, we specifically focus on two structured output prediction tasks, namely multi-label classification and multi-target regression. We conducted experiments using multiple benchmark datasets and the obtained results confirm that our method provides superior results to state-of-the-art methods in both tasks

Management of an extrasphincteric fistula in an HIV-positive patient by using fibrin glue: a case report with tips and tricks

<p>Abstract</p> <p>Background</p> <p>Individuals with impaired immunity are at higher risk of perianal diseases. Concerning complex anal fistulas impaired healing and complication rates are also higher. Definitive treatment of a fistula aims controlling the purulent discharge and prevents its recurrence. It depends mainly on the trajectory of the fistula and the underlying disease.</p> <p>We present a case of a HIV-positive patient with a complex extrasphincteric anal fistula who was treated successfully with fibrin glue application. We further, discuss tips and tricks when applying fibrin glue as plugging material in complex anal fistulas.</p> <p>Case presentation</p> <p>A sixty-one-year-old HIV-positive male referred to us for warts and extrasphincteric fistula. Because of the patients' immunological status, we opted against surgery and recommended fibrin glue plugging. The patient was discharged the same day. A follow-up examination was performed 5 days after the initial fibrin glue application showing that the fistula canal was obstructed. Three months and a year post-intervention the fistula tract remains closed.</p> <p>Conclusion</p> <p>The best treatment for a disease gives at least the same result with the other treatments with minimised risk for the life of the patient and minimal application effort. Conservative closure of fistula with fibrin plugging is simple, safe and with less morbidity than surgery. Our patient was successfully treated without endangering his life despite his precarious medical state. Not everybody believes in the effectiveness of fibrin glue application, however we consider this solution in cases of complex fistulas at least as primary procedure in special populations such as the immunosupressed.</p

Making sense of big data in health research: Towards an EU action plan.

Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of "big data" for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans

Network inference with ensembles of bi-clustering trees

Network inference is crucial for biomedicine and systems biology. Biological entities and their associations are often modeled as interaction networks. Examples include drug protein interaction or gene regulatory networks. Studying and elucidating such networks can lead to the comprehension of complex biological processes. However, usually we have only partial knowledge of those networks and the experimental identification of all the existing associations between biological entities is very time consuming and particularly expensive. Many computational approaches have been proposed over the years for network inference, nonetheless, efficiency and accuracy are still persisting open problems. Here, we propose bi-clustering tree ensembles as a new machine learning method for network inference, extending the traditional tree-ensemble models to the global network setting. The proposed approach addresses the network inference problem as a multi-label classification task. We extended traditional tree-ensemble methods, such as Extremely Randomized Trees (ERT) and Random Forests (RF) to ensembles of bi-clustering trees, integrating background information from both node sets of a heterogeneous network into the same learning framework. We performed an empirical evaluation, comparing the proposed approach to currently used tree-ensemble based approaches as well as other approaches from the literature. We demonstrated the effectiveness of our approach in different interaction prediction (network inference) settings. For evaluation purposes, we used several benchmark datasets that represent drug-protein and gene regulatory networks. We also applied our proposed method to two versions of a chemical-protein association network extracted from the STITCH database, demonstrating the potential of our model in predicting non-reported interactions. Bi-clustering trees outperform existing tree-based strategies as well as machine learning methods based on other algorithms. Since our approach is based on tree-ensembles it inherits the advantages of tree-ensemble learning, such as handling of missing values, scalability and interpretability.status: publishe

Feature Induction and Network Mining with Clustering Tree Ensembles

The volume of data generated and collected using modern technologies grows exponentially. This vast amount of data often follows a complex structure, and the problem of efficiently mining and analyzing such data is crucial for the performance of various machine learning tasks. Here, a novel data mining framework for unsupervised learning tasks is proposed based on decision tree learning and ensembles of trees. The proposed approach introduces an informative feature representation and is able to handle data diversity and complexity. Moreover, a new scheme is proposed based on the aforementioned approach for mining interaction data. These data are often modeled as homogeneous or heterogeneous networks and they are present in various fields, such as social media, recommender systems, and bioinformatics. The learning process is performed in an unsupervised manner, following also the inductive setup. The experimental evaluation confirms the effectiveness of the proposed approach.status: publishe

Mining Features for Biomedical Data using Clustering Tree Ensembles

The volume of biomedical data available to the machine learning community grows very rapidly. A rational question is how informative these data really are or how discriminant the features describing the data instances are. Several biomedical datasets suffer from lack of variance in the instance representation, or even worse, contain instances with identical features and different class labels. Indisputably, this directly affects the performance of machine learning algorithms, as well as the ability to interpret their results. In this article, we emphasize on the aforementioned problem and propose a target-informed feature induction method based on tree ensemble learning. The method brings more variance into the data representation, thereby potentially increasing predictive performance of a learner applied to the induced features. The contribution of this article is twofold. Firstly, a problem affecting the quality of biomedical data is highlighted, and secondly, a method to handle that problem is proposed. The efficiency of the presented approach is validated on multi-target prediction tasks. The obtained results indicate that the proposed approach is able to boost the discrimination between the data instances and increase the predictive performance.status: Published onlin

Feature Induction based on Extremely Randomized Tree Paths

The volume of data generated and collected using modern technologies grows exponentially. This vast amount of data often follows a complex structure, significantly affecting the performance of various machine learning tasks. Despite the effort made, the problem of efficiently mining and analyzing such data is still persisting. Here, a novel data mining framework for unsupervised learning tasks is proposed based on decision tree learning and ensembles of trees. The proposed approach introduces an informative feature representation and is able to handle data diversity (e.g., numerical, canonical, etc.) and complexity (e.g., graphs, networks, data containing missing values etc.). Learning is performed in an unsupervised manner, following also the inductive setup. The experimental evaluation confirms the effectiveness of the proposed approach.status: publishe

Tree based feature induction for biomedical data

During the recent years, a great advance in both biomedical data acquisition technologies and feature extraction methods has been witnessed. Harnessing these new tools and technologies has led to an indisputable increase in the number of available biomedical datasets. Despite the efforts made so far, the representational power of features used to describe a sample in such datasets, such as a gene in gene function prediction datasets or a protein in protein interaction datasets has yet to be improved. Here, the performed study focuses on the feature representation power from a machine learning perspective.status: publishe