455 research outputs found
Scoring and Classifying with Gated Auto-encoders
Auto-encoders are perhaps the best-known non-probabilistic methods for
representation learning. They are conceptually simple and easy to train. Recent
theoretical work has shed light on their ability to capture manifold structure,
and drawn connections to density modelling. This has motivated researchers to
seek ways of auto-encoder scoring, which has furthered their use in
classification. Gated auto-encoders (GAEs) are an interesting and flexible
extension of auto-encoders which can learn transformations among different
images or pixel covariances within images. However, they have been much less
studied, theoretically or empirically. In this work, we apply a dynamical
systems view to GAEs, deriving a scoring function, and drawing connections to
Restricted Boltzmann Machines. On a set of deep learning benchmarks, we also
demonstrate their effectiveness for single and multi-label classification
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Development of a deep learning-based computational framework for the classification of protein sequences
Dissertação de mestrado em BioinformaticsProteins are one of the more important biological structures in living organisms, since they
perform multiple biological functions. Each protein has different characteristics and properties,
which can be employed in many industries, such as industrial biotechnology, clinical applications,
among others, demonstrating a positive impact.
Modern high-throughput methods allow protein sequencing, which provides the protein
sequence data. Machine learning methodologies are applied to characterize proteins using
information of the protein sequence. However, a major problem associated with this method
is how to properly encode the protein sequences without losing the biological relationship
between the amino acid residues. The transformation of the protein sequence into a numeric
representation is done by encoder methods. In this sense, the main objective of this project is to
study different encoders and identify the methods which yield the best biological representation
of the protein sequences, when used in machine learning (ML) models to predict different labels
related to their function.
The methods were analyzed in two study cases. The first is related to enzymes, since
they are a well-established case in the literature. The second used transporter sequences, a
lesser studied case in the literature. In both cases, the data was collected from the curated
database Swiss-Prot. The encoders that were tested include: calculated protein descriptors;
matrix substitution methods; position-specific scoring matrices; and encoding by pre-trained
transformer methods. The use of state-of-the-art pretrained transformers to encode protein
sequences proved to be a good biological representation for subsequent application in state-of-the-art ML methods. Namely, the ESM-1b transformer achieved a Mathews correlation coefficient
above 0.9 for any multiclassification task of the transporter classification system.As proteínas são estruturas biológicas importantes dos organismos vivos, uma vez que estas desempenham múltiplas funções biológicas. Cada proteína tem características e propriedades diferentes, que podem ser aplicadas em diversas indústrias, tais como a biotecnologia industrial, aplicações clínicas, entre outras, demonstrando um impacto positivo. Os métodos modernos de alto rendimento permitem a sequenciação de proteínas, fornecendo dados da sequência proteica. Metodologias de aprendizagem de máquinas tem sido aplicada para caracterizar as proteínas utilizando informação da sua sequência. Um problema associado a este método e como representar adequadamente as sequências proteicas sem perder a relação biológica entre os resíduos de aminoácidos. A transformação da sequência de proteínas numa representação numérica é feita por codificadores. Neste sentido, o principal objetivo deste projeto é estudar diferentes codificadores e identificar os métodos que produzem a melhor representação biológica das sequências proteicas, quando utilizados em modelos de aprendizagem mecânica para prever a classificação associada à sua função a sua função. Os métodos foram analisados em dois casos de estudo. O primeiro caso foi baseado em enzimas, uma vez que são um caso bem estabelecido na literatura. O segundo, na utilização de proteínas de transportadores, um caso menos estudado na literatura. Em ambos os casos, os dados foram recolhidos a partir da base de dados curada Swiss-Prot. Os codificadores testados incluem: descritores de proteínas calculados; métodos de substituição por matrizes; matrizes de pontuação específicas da posição; e codificação por modelos de transformadores pré-treinados. A utilização de transformadores de última geração para codificar sequências de proteínas demonstrou ser uma boa representação biológica para aplicação subsequente em métodos ML de última geração. Nomeadamente, o transformador ESM-1b atingiu um coeficiente de correlação de Matthews acima de 0,9 para multiclassificação do sistema de classificação de proteínas transportadoras
AutoDiscern: Rating the Quality of Online Health Information with Hierarchical Encoder Attention-based Neural Networks
Patients increasingly turn to search engines and online content before, or in
place of, talking with a health professional. Low quality health information,
which is common on the internet, presents risks to the patient in the form of
misinformation and a possibly poorer relationship with their physician. To
address this, the DISCERN criteria (developed at University of Oxford) are used
to evaluate the quality of online health information. However, patients are
unlikely to take the time to apply these criteria to the health websites they
visit. We built an automated implementation of the DISCERN instrument (Brief
version) using machine learning models. We compared the performance of a
traditional model (Random Forest) with that of a hierarchical encoder
attention-based neural network (HEA) model using two language embeddings, BERT
and BioBERT. The HEA BERT and BioBERT models achieved average F1-macro scores
across all criteria of 0.75 and 0.74, respectively, outperforming the Random
Forest model (average F1-macro = 0.69). Overall, the neural network based
models achieved 81% and 86% average accuracy at 100% and 80% coverage,
respectively, compared to 94% manual rating accuracy. The attention mechanism
implemented in the HEA architectures not only provided 'model explainability'
by identifying reasonable supporting sentences for the documents fulfilling the
Brief DISCERN criteria, but also boosted F1 performance by 0.05 compared to the
same architecture without an attention mechanism. Our research suggests that it
is feasible to automate online health information quality assessment, which is
an important step towards empowering patients to become informed partners in
the healthcare process
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Efficient Beam Tree Recursion
Beam Tree Recursive Neural Network (BT-RvNN) was recently proposed as a
simple extension of Gumbel Tree RvNN and it was shown to achieve
state-of-the-art length generalization performance in ListOps while maintaining
comparable performance on other tasks. However, although not the worst in its
kind, BT-RvNN can be still exorbitantly expensive in memory usage. In this
paper, we identify the main bottleneck in BT-RvNN's memory usage to be the
entanglement of the scorer function and the recursive cell function. We propose
strategies to remove this bottleneck and further simplify its memory usage.
Overall, our strategies not only reduce the memory usage of BT-RvNN by
- times but also create a new state-of-the-art in ListOps while
maintaining similar performance in other tasks. In addition, we also propose a
strategy to utilize the induced latent-tree node representations produced by
BT-RvNN to turn BT-RvNN from a sentence encoder of the form into a sequence contextualizer of the
form . Thus, our
proposals not only open up a path for further scalability of RvNNs but also
standardize a way to use BT-RvNNs as another building block in the deep
learning toolkit that can be easily stacked or interfaced with other popular
models such as Transformers and Structured State Space models
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