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Backgrounds: A large number of long intergenic non-coding RNAs (lincRNAs) are linked to a broad spectrum of human diseases. The disease association with many other lincRNAs still remain as puzzle. Validation of such links between the two entities through biological experiments are expensive. However, a plethora lincRNA-data are available now, thanks to the High Throughput Sequencing (HTS) platforms, Genome Wide Association Studies (GWAS), etc, which opens the opportunity for cutting-edge machine learning and data mining approaches to extract meaningful relationships among lincRNAs and diseases. However, there are only a few in silico lincRNA-disease association inference tools available to date, and none of them utilizes side information of both the entities simultaneously in a single framework. Methods: The recently developed Inductive Matrix Completion (IMC) technique provides a recommendation platform among two entities considering respective side information about them. However, the formulation of IMC is incapable of handling noise and outliers that may be present in the datasets, while data sparsity consideration is another issue with the standard IMC method. Thus, a robust version of IMC is needed that can solve the two issues. As a remedy, in this paper, we propose Stable Robust Inductive Matrix Completion (SRIMC) that utilizes the l2,1 norm based regularization to optimize the objective function with a unique 2-step stable solution approach. Results: We applied SRIMC to the available association data between human lincRNAs and OMIM disease phenotypes as well as a diverse set of side information about the lincRNAs and the diseases. The method performs better than the state-of-the-art methods in terms of precision@k and recall@k at the top-k disease prioritization to the subject lincRNAs. We also demonstrate that SRIMC is equally effective for querying about novel lincRNAs, as well as predicting rank of a newly known disease for a set of well-characterized lincRNAs. Conclusions: With the experimental results and computational evaluation, we show that SRIMC is robust in handling datasets with noise and outliers as well as dealing with novel lincRNAs and disease phenotypes. [ABSTRACT FROM AUTHOR]

The present study investigated whether the sequencing of exemplars affects learning of rule-based science categories. We taught undergraduate students chemical categories by showing them diagrams of organic chemical compounds accompanied by their category label in either an interleaved or blocked fashion. On a categorization test of novel exemplars administered 2 days later, interleaved presentation produced superior performance than blocked presentation for categories that were visually simple (Experiment 1) and relatively more complex (Experiment 2). The advantage of interleaved presentation was still observed when the diagnostic features of each category were highlighted during study (Experiments 3 and 4), suggesting that the benefit of interleaving may not be due solely to increasing the salience of contrastive features that differentiate the categories. These findings demonstrate that interleaved presentation can improve learning of educationally relevant categories, even when coupled with pedagogical techniques that emphasize the diagnostic features of each category. [ABSTRACT FROM AUTHOR]

Mixing examples of different categories (interleaving) has been shown to promote inductive learning as compared with presenting examples of the same category together (massing). In three studies, we tested whether the advantage of interleaving is exclusively due to the mixing of examples from different categories or to the temporal gap introduced between presentations. In addition, we also tested the role of working memory capacity (WMC). Results showed that the mixing of examples might be the key component that determines improved induction. WMC might also be involved in the interleaving effect: participants with high spans seemed to profit more than participants with low spans from interleaved presentations. Our findings have relevant implications for education. Practice schedules should be individually customised so society as a whole can profit from differences between learners. [ABSTRACT FROM AUTHOR]

The high cost of data labeling often results in node label shortage in real applications. To improve node classification accuracy, graph-based semi-supervised learning leverages the ample unlabeled nodes to train together with the scarce available labeled nodes. However, most existing methods require the information of all nodes, including those to be predicted, during model training, which is not practical for dynamic graphs with newly added nodes. To address this issue, an adversarially regularized graph attention model is proposed to classify newly added nodes in a partially labeled graph. An attention-based aggregator is designed to generate the representation of a node by aggregating information from its neighboring nodes, thus naturally generalizing to previously unseen nodes. In addition, adversarial training is employed to improve the model's robustness and generalization ability by enforcing node representations to match a prior distribution. Experiments on real-world datasets demonstrate the effectiveness of the proposed method in comparison with the state-of-the-art methods. The code is available at https://github.com/JiarenX/AGAIN. • Inductive learning on partially labeled attributed graphs is addressed. • An adversarially regularized graph attention model is proposed. • Experiments on real-world datasets demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Double-blind randomized controlled trials (RCTs) are viewed as the golden standard of drug research in Western medicine. However, RCTs are far from "golden" in many respects. They are impractical for many therapies, such as for surgeries and complex lifestyle changes. They encourage a one-size-fits-all approach to medical treatment that fails to address the huge diversity among individual patients in terms of their physical and emotional symptoms, social and cultural upbringing, and other factors. Perhaps, more importantly, they do not help doctors make the best medical decisions required to produce optimal patient outcomes. To guide a search for an alternate model of medical research, three principles based on Ayurveda, an ancient and powerful system of health care that has stood the test of time, are presented. These principles, arrived at after mining Ayurvedic epistemology, are: Inductive learning, whole systems thinking, and individually optimized therapy. In honor of the ancient sages or "Rishis," whose voice is used to deliver Ayurvedic knowledge in the ancient texts of Ayurveda, these are referred to as the "Rishi principles." Individually optimized therapy is interpreted using the lens of decision analysis. Common research methodologies are examined for embodiment of these principles.

Il saggio presenta alcuni risultati di una collaborazione tra Politecnico di Torino e il CSI Piemonte (Società di servizi Informatizzati partecipata dalla Regione Piemonte). Sono stati selezionati e studiati diversi strumenti di visualizzazione dei dati scientifici (Gapminder, ManyEyes, Open eXplorer e Fineo) al fine di individuare quello più utile per una lettura induttiva di grandi quantità di dati informativi (big data). Lo sfruttamento intelligente dei dati digitali può portare a uno sviluppo conoscitivo ma anche a un profitto, le cui soglie di sfruttamento possono essere misurate in un sistema economico. Nell’irreversibile fenomeno di crescita dei dati digitali, la disciplina del “Data Visualization” diventa cruciale per accedere e comprendere informazioni complesse. Few, guru della comunicazione visiva, scrive che “scopriamo il mondo attraverso gli occhi”; le forme di comunicazione e interpretazione tradizionali dei dati hanno puntato sulla dimensione visuale per migliorare la comprensione e hanno permesso sia agli analisti sia agli utenti la sperimentazione di nuove interazioni (“story-telling”). Come urbanisti e cittadini, ci affidiamo alla vista che gestisce molti dei sensori (70%) legati alla percezione, alle mappe cognitive, agli errori e ai nuovi pensieri. L’ipotesi di fondo di questo articolo vuole generare delle riflessioni sui Big Data come strategia importante per le imprese pubbliche e private che intendono imparare a cambiare dalle informazioni digitali di cui oggi disponiamo. Attraverso l’uso di uno strumento analitico di visualizzazione dei dati informativi, si descrive un recente caso di studio in un contesto territoriale come quello dei nuovi consorzi amministrativi (Unione dei Comuni NordEst Torino). In questo esperimento torna a essere attuale la necessità di pianificare le scelte in modo sistematico anche cercando di utilizzare in modo nuovo e semplice i sistemi informativi territoriali già disponibili.

Classification means assigning each object in a collection or dataset to a category or class. This mapping can also be called classification model or classifier. When using textual data, the objecto to be classified can be either documents in a collection or words or sentences that belong to those documents. This tutorial consists on an introduction to the task of document classification, one of the best known problems in Text Mining. Some popular algorithms that are used throughout the literature to classify document are presented here. Besides, we also present some techniques whose goal is to evaluate the quality of a classification model.