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In this work we develop and demonstrate a probabilistic generative model for phytoplankton communities. The proposed model takes counts of a set of phytoplankton taxa in a timeseries as its training data, and models communities by learning sparse co-occurrence structure between the taxa. Our model is probabilistic, where communities are represented by probability distributions over the species, and...
In this paper, we address interesting questions about how feng shui influences house price from a data perspective. First, is feng shui likely to influence house price? Second, how do different feng shui features, e.g., house shape, master bedroom location, and other interior room arrangements, influence the price? Third, can we automatically diagnose the feng shui problems of a house? From a dataset...
Context-Aware Recommendation Systems has gained lots of attention in both industry and academic research. Factorization Machines (FM) based recommendation has been successfully used in sparse industrial datasets for user personalized video recommendations. FM is a collaborative filtering technique for predicting a target such as user rating, given observations of interaction between some users and...
In this paper we overview the modern forecast methods of monthly sunspot numbers, such as McNish-Lincoln and Hathaway-Wilson-Reichmann standard curve-fitting. Their disadvantages are presented, leading us to the necessity of researching a new technique for the solar activity prediction. For the long-term forecast we propose to use the established nonlinear dynamo model based on negative effective...
Most automatic speech recognition (ASR) systems are incapable of generating punctuation, making it difficult to read the transcribed output and less appropriate for tasks such as dictation. This paper introduces a procedure to automatically insert punctuation into unpunctuated sentences by using a bidirectional recurrent neural network with attention mechanism and Part-of-Speech (POS) Tags. Using...
Drug-target interaction identification is of highly importance in drug research and development. The traditional experimental paradigm is costly, while the previous in silico prediction paradigm remains a challenge because of diversified data production platforms and data scarcity. In this paper, we modeled drug-target interaction prediction as a binary classification task based on transcriptome data...
Predictive Complex Event Processing (CEP) constitutes the next phase of CEP evolution and provides future predictive states of the partially matched complex sequences. In this paper, we demonstrate our novel predictive CEP system and show that this problem can be solved while leveraging existing data modelling, query execution and optimisation frameworks. We model the predictive detection of events...
Exponential growth in electronic health record (EHR) data has resulted in new opportunities and urgent needs to discover meaningful data-driven representations and patterns of diseases, i.e., computational phenotyping. Recent success and development of deep learning provides promising solutions to the problem of prediction and feature discovery tasks, while lots of challenges still remain and prevent...
In the literature, a number of methods have been proposed for semi-supervised learning. Recently, graph-based methods of semi-supervised learning have become popular because of their capability of handling large amounts of unlabeled data. However, the existing graph based semi-supervised learning algorithms do not optimize the process of selecting better labeled data. We have developed a new selective...
The rapid growth of Electronic Health Records (EHRs), as well as the accompanied opportunities in Data-Driven Healthcare (DDH), has been attracting widespread interests and attentions. Recent progress in the design and applications of deep learning methods has shown promising results and is forcing massive changes in healthcare academia and industry, but most of these methods rely on massive labeled...
Classification models have proven useful for predicting clinical interventions and patient outcomes. One of the key issues that affect the predictive ability of supervised learning frameworks in the healthcare scenario is imbalance in data sets. In addition, non-uniform data collection processes in clinical scenarios lead to poor quality data sets. We designed a novel approach to predict Intensive...
In this study, we propose a two-stage method for material segmentation in hyperspectral images. The first stage employs a Convolutional Neural Network (CNN) to predict the material label at individual pixels. The second stage further refines the segmentation by a fully-connected Conditional Random Field (CRF) framework. For the first stage, we experimented with two different network architectures...
In this study, we introduce an ensemble-based approach for online machine learning. Here, instead of working on the original data, several Hoeffding tree classifiers classify and are updated on the lower dimensional projected data generated from originality by random projections. Since random projection is unstable, from one example, many diverse training data can be created to train the set of Hoeffding...
The use of RPE as a measure of Internal load has become a common methodology used in team sports owing to its low cost. The aim of this study was to build a machine learning process able to describe the players' RPE by the external load extracted from the GPS. In this paper, we propose a multidimensional approach to assess the RPE in professional soccer which is based on GPS measurements and machine...
We treat failure prediction in a supervised learning framework using a convolutional neural network (CNN). Due to the nature of the problem, learning a CNN model on this kind of dataset is generally associated with three primary problems: 1) negative samples (indicating a healthy system) outnumber positives (indicating system failures) by a great margin; 2) implementation design often requires chopping...
Predicting patients' risk of developing certain diseases is an important research topic in healthcare. Personalized predictive modeling, which focuses on building specific models for individual patients, has shown its advantages on utilizing heterogeneous health data compared to global models trained on the entire population. Personalized predictive models use information from similar patient cohorts,...
Protein-DNA docking is an important computational technique for generating native or near-native complex models. A docking program typically generates a number of complex conformations and predicts the docking solution based on interaction energies. However, incomplete sampling and energy function deficiencies can result in false positive protein-DNA complex models, which hampers its application in...
Sleep quality impacts virtually all aspects of life, including health, mood, emotions, cognition, memory, behavior, and performance. Actigraphy offers a lower-cost alternative to conventional polysomnography (PSG), the gold standard for measuring sleep quality. Effective use of actigraphy for assessing sleep quality requires reliable methods for detecting sleep/wake states from actigraphy measurements...
Down syndrome (DS) is a genetic disorder with genome dosage imbalances and micro-duplications of human chromosome 21. It is usually associated with a group of serious diseases, including intellectual disabilities, cardiac diseases, physical abnormalities, and other abnormalities. Currently, since there is no cure for human DS, screening and early detection have become the most efficient way for DS...
Thanks to rapidly evolving sequencing techniques, the amount of genomic data at our disposal is growing increasingly large. Determining the gene structure is a fundamental requirement to effectively interpret gene function and regulation. An important part in that determination process is the identification of translation initiation sites. In this paper, we propose a novel approach for automatic prediction...
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