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Human action recognition is the process of labeling image sequences with action labels. Robust solutions to this problem have applications in domains such as medical care, human-computer interaction and virtual training. The task is challenging for feature extraction due to variations in motion performance, recording settings and inter-personal differences. To meet these challenges, we propose two...
Merging gene expression datasets is a simple way to increase the number of samples in an analysis. However experimental and data processing conditions, which are proper to each dataset or batch, generally influence the expression values and can hide the biological effect of interest. It is then important to normalize the bigger merged dataset, as failing to adjust for those batch effects may adversely...
With the rapidly increasing reliance of life sciences research on digital data and sophisticated computational analysis techniques, the ability to reproduce findings generated by in-silico data analysis workflows is of increasing importance to the scientific community. However, challenges to reproducibility arise from the heterogeneity of approaches available to workflow definition and enactment....
This paper presents a new method of segmenting and classifying protein crystallization trial images that were collected using trace fluorescent labeling. Trace fluorescent labeling typically involves fluorescence dye that can re-emit the illumination light at other wavelengths around the principal wavelength. The captured image has a primary color channel with respect to illumination light and fluorescence...
When microscopy image data are stored and processed, the corresponding workflow is typically carried out by treating storing and processing separately. In many laboratories, it is common to store data on one computing system and process data on another system. This separation of storing and processing data has a negative impact on the traceability of results, and thus on reproducibility, as information...
Next Generation Sequencing has introduced novel means of sequencing millions of DNA molecules simultaneously and has opened up new avenues in the field of bioinformatics that requires high performance computing technologies. Bioinformatics pipelines are constructed to carry out bioinformatics analyses in a fast and efficient manner. Workflow systems are developed to simplify the construction of pipelines...
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...
In order to facilitate better estimations on coronary artery disease conditions of a patient, we aim to predict the number of Angioplasty (a coronary artery procedure) by taking into account all the information from his/her Electronic Health Record (EHR) data. For this purpose, two exponential family members—multinomial distribution and Poisson distribution models—are considered, which treat the target...
Non-invasive blood glucose measurement is a crucial challenge in both academic and industry communities. Currently, most of non-invasive solutions are developed based on optical signals. However, their accuracy is still far from clinical requirements if these measured optical signals directly used to estimate corresponding glucose levels. To solve this challenge, a novel Back-propagation Monte Carlo...
Trigger detection plays a key role in the extraction of biomedical events, so it will influence the results of biomedical events extraction directly. The traditional biomedical event trigger recognition method is based on artificial design features and construct feature vectors; Not only does it consume great amounts of manpower, it also lacks system generalization ability. Most of methods of trigger...
There are intensive computational efforts to discover large-scale microbial interactions from metagenomic abundance data, however, it is often difficult to validate such inferred interactions without a manually curated dataset. There are also a number of small-scale microbial interactions reported in massive literature with experimental confidence. Text mining can be employed to extract such microbial...
We present a novel computational method for Multiple Sequence Alignment (MSA), a fundamental problem in computational biology. In contrast to other known approaches, our method searches for an optimal alignment — structurally and evolutionarily — by inserting or deleting gaps from a set of initial candidates in an efficient manner. Our method called a Universal Partitioning Search (UPS) approach for...
Representation learning algorithm in medical area maps high dimensional real world medical concepts to low dimensional vector space, encodes rich medical knowledge, and has brought improvement to various machine learning applications in medical area. However, previous representation learning models in medical area failed to consider the multi-sense characteristic of medical concept. Moreover, the...
Clustering cancer patients into subgroups and identifying cancer subtypes is an important task in cancer genomics. Clustering based on comprehensive multi-omic molecular profiling can often achieve better results than those using a single data type, since each omic data type may contain complementary information. However, it is challenging to integrate heterogeneous omic data directly. Based on one...
Estimating model parameters is a crucial step to understand the behavior of biological systems. To perform parameter estimation, a commonly used formulation is the least square method that minimizes the mean squared error. This method finds the model parameters that minimize the sum of the squared error between experimental data and model predictions. However, such a formulation can misguide parameter...
Data in large-scale genetic studies of complex human diseases, such as substance use disorders, are often incomplete. Despite great progress in genotype imputation, e.g., the IMPUTE2 method, considerably less progress has been made in inferring phenotypes. We designed a novel approach to integrate individuals' comorbid conditions with their genotype data to infer missing (unreported) diagnostic criteria...
Data mapping among different data standards in health institutes is often a necessity when data exchanges occur among different institutes. However, no matter rule-based approaches or traditional machine learning methods, none of these methods have achieved satisfactory results yet. In this work, we propose a deep learning method, mixture feature embedding convolutional neural network (MfeCNN), to...
Biological network alignment benefits the evolutionary and comparative biology by providing regions of topological and functional similarity between different species. However, most existing network aligners follow heuristic methods and only capture the static information that based purely on the original isolated networks, while there also exists valuable interactive information hidden in the resulted...
We introduce a method to quantify visual diagnostic clues that are considered during simulated pathology diagnosis. We extend Apriori method of association rule mining with custom Perl extensions to collect sets of diagnostic clues that show an improvement in diagnostic accuracy. We developed an information gain measure based on Kullback-Leibler divergence in an attempt to gauge the impact of additional...
Ligand binding site prediction from protein structure plays an important role in various complex rational drug design efforts. Its applications include drug side effects prediction, docking prioritization in inverse virtual screening and elucidation of protein function in genome wide structural studies. Currently available tools have limitations that disqualify them from many possible use cases. In...
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