68 research outputs found

    Bioinformatics and Medicine in the Era of Deep Learning

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    Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by technological breakthroughs in data acquisition technologies. It has been argued that bioinformatics could quickly become the field of research generating the largest data repositories, beating other data-intensive areas such as high-energy physics or astroinformatics. Over the last decade, deep learning has become a disruptive advance in machine learning, giving new live to the long-standing connectionist paradigm in artificial intelligence. Deep learning methods are ideally suited to large-scale data and, therefore, they should be ideally suited to knowledge discovery in bioinformatics and biomedicine at large. In this brief paper, we review key aspects of the application of deep learning in bioinformatics and medicine, drawing from the themes covered by the contributions to an ESANN 2018 special session devoted to this topic

    Bioinformatics and Medicine in the Era of Deep Learning

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    Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by technological breakthroughs in data acquisition technologies. It has been argued that bioinformatics could quickly become the field of research generating the largest data repositories, beating other data-intensive areas such as high-energy physics or astroinformatics. Over the last decade, deep learning has become a disruptive advance in machine learning, giving new live to the long-standing connectionist paradigm in artificial intelligence. Deep learning methods are ideally suited to large-scale data and, therefore, they should be ideally suited to knowledge discovery in bioinformatics and biomedicine at large. In this brief paper, we review key aspects of the application of deep learning in bioinformatics and medicine, drawing from the themes covered by the contributions to an ESANN 2018 special session devoted to this topic

    Rejection-oriented learning without complete class information

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    Machine Learning is commonly used to support decision-making in numerous, diverse contexts. Its usefulness in this regard is unquestionable: there are complex systems built on the top of machine learning techniques whose descriptive and predictive capabilities go far beyond those of human beings. However, these systems still have limitations, whose analysis enable to estimate their applicability and confidence in various cases. This is interesting considering that abstention from the provision of a response is preferable to make a mistake in doing so. In the context of classification-like tasks, the indication of such inconclusive output is called rejection. The research which culminated in this thesis led to the conception, implementation and evaluation of rejection-oriented learning systems for two distinct tasks: open set recognition and data stream clustering. These system were derived from WiSARD artificial neural network, which had rejection modelling incorporated into its functioning. This text details and discuss such realizations. It also presents experimental results which allow assess the scientific and practical importance of the proposed state-of-the-art methodology.Aprendizado de Máquina é comumente usado para apoiar a tomada de decisão em numerosos e diversos contextos. Sua utilidade neste sentido é inquestionável: existem sistemas complexos baseados em técnicas de aprendizado de máquina cujas capacidades descritivas e preditivas vão muito além das dos seres humanos. Contudo, esses sistemas ainda possuem limitações, cuja análise permite estimar sua aplicabilidade e confiança em vários casos. Isto é interessante considerando que a abstenção da provisão de uma resposta é preferível a cometer um equívoco ao realizar tal ação. No contexto de classificação e tarefas similares, a indicação desse resultado inconclusivo é chamada de rejeição. A pesquisa que culminou nesta tese proporcionou a concepção, implementação e avaliação de sistemas de aprendizado orientados `a rejeição para duas tarefas distintas: reconhecimento em cenário abertos e agrupamento de dados em fluxo contínuo. Estes sistemas foram derivados da rede neural artificial WiSARD, que teve a modelagem de rejeição incorporada a seu funcionamento. Este texto detalha e discute tais realizações. Ele também apresenta resultados experimentais que permitem avaliar a importância científica e prática da metodologia de ponta proposta

    Artificial intelligence for the artificial kidney: Pointers to the future of a personalized hemodialysis therapy

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    Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment, or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve patient’s quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of Big Data and will require real-time predictive models. These may come from the fields of Machine Learning and Computational Intelligence, both included in Artificial Intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of Artificial Intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in Artificial Intelligence and Machine Learning, a scientific meeting was organized in the Hospital of Bellvitge (Barcelona, Spain). As an outcome of that meeting, the aim of this review is to investigate Artificial Intelligence experiences on dialysis, with a focus on potential barriers, challenges and prospects for future applications of these technologies.Postprint (author's final draft

    How Fast Can We Play Tetris Greedily With Rectangular Pieces?

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    Consider a variant of Tetris played on a board of width ww and infinite height, where the pieces are axis-aligned rectangles of arbitrary integer dimensions, the pieces can only be moved before letting them drop, and a row does not disappear once it is full. Suppose we want to follow a greedy strategy: let each rectangle fall where it will end up the lowest given the current state of the board. To do so, we want a data structure which can always suggest a greedy move. In other words, we want a data structure which maintains a set of O(n)O(n) rectangles, supports queries which return where to drop the rectangle, and updates which insert a rectangle dropped at a certain position and return the height of the highest point in the updated set of rectangles. We show via a reduction to the Multiphase problem [P\u{a}tra\c{s}cu, 2010] that on a board of width w=Θ(n)w=\Theta(n), if the OMv conjecture [Henzinger et al., 2015] is true, then both operations cannot be supported in time O(n1/2ϵ)O(n^{1/2-\epsilon}) simultaneously. The reduction also implies polynomial bounds from the 3-SUM conjecture and the APSP conjecture. On the other hand, we show that there is a data structure supporting both operations in O(n1/2log3/2n)O(n^{1/2}\log^{3/2}n) time on boards of width nO(1)n^{O(1)}, matching the lower bound up to a no(1)n^{o(1)} factor.Comment: Correction of typos and other minor correction
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