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A large number of long non-coding RNAs (lncRNAs) have been identified over the past decades. Accumulating evidence proves that lncRNAs play key roles in various biological processes. However, the majority of the lncRNAs have not been functionally characterized. The annotation of lncRNA functions has become an area of focus in the fields of biology and bioinformatics. In this paper, we develop a global...
Many pathogenic mutations percolate to protein dysfunction by altering dynamics. Reconstructing protein energy landscapes promises to relate dynamics to function but is generally infeasible due to the disparate spatio-temporal scales involved. Recent algorithmic innovation allows reconstructing energy landscapes of medium-size proteins in the presence of sufficient prior wet-laboratory structure data...
Transcription factors (TFs), as the key regulatory elements of gene transcription, can activate or suppress the transcription by binding to specific sets of DNA sequences. In the past, the introduction of ChIP-seq sequencing technologies provides immense opportunities for precise categorization of TF binding sites. In this talk, we will introduce several novel computational models for integrative...
The energy landscape underscores the inherent nature of proteins as dynamic systems interconverting between structures with varying energies. Recently, our laboratory has developed a computational framework that feasibly reconstructs energy landscapes of any forms of a protein of interest, thus allowing investigating in silico the impact of pathogenic mutations on equilibrium structure and dynamics...
The presence of heterogeneity in the data representation of the commercial protein screen makes the analysis of experimental results and the protein crystallization screening process difficult and time-consuming. In order to reduce the human effort required to deal with this problem, we present our application based on schema matching and data integration to automatically find the matching elements...
DNA affinity purification sequencing (DAP-seq) is a recently developed technique for transcription factor (TF) binding site discovery that produces datasets like ChIP-seq. A major advantage of the DAP-seq method is that it uses exogenously expressed TFs to directly interrogate genomic DNA, without the need for tagged transgenic lines or gene-specific antibodies while still capturing TF binding events...
Many bioinformatics studies aim to find features that differentiate between two or more classes. Recent work proposes a Bayesian framework for feature selection that places a prior on the label-conditioned feature distribution. Assuming independent features, the optimal Bayesian filter is obtained and has been solved for Gaussian features. Here we extend the optimal Bayesian filter for categorical...
Observational data resources based on the capture of clinical data in the electronic health record (EHR) have produced significant learning opportunities in many areas of medicine. These large data resources can span multiple hospital systems and employ common semantics, ontologies, and data models. They have uncovered critical safety issues for patients, and spurred observational research and clinical...
The large availability of biomedical data brings opportunities and challenges to health care. Representation of medical concepts has been well studied in many applications, such as medical informatics, cohort selection, risk prediction, and health care quality measurement. In this paper, we propose an efficient multichannel convolutional neural network (CNN) model based on multi-granularity embeddings...
Due to the high variability in tumor morphology and the low signal-to-noise ratio inherent to mammography, manual classification of mammogram yields a significant number of patients being called back, and subsequent large number of biopsies performed to reduce the risk of missing cancer. The convolutional neural network (CNN) is a popular deep-learning construct used in image classification. This...
Left ventricle(LV) segmentation is a prerequisite step of evaluation of LV structure and function, which plays an important role in the diagnosis and treatment of cardiovascular diseases. In this paper, we propose a method to segment endocardium and epicardium of LV using convolution neural network combined with active contour model and tensor voting. A fully convolution neural network (FCN) named...
Drowsy driving is the main reason for sleep-related crashes. We have observed that an alpha wave attenuation-disappearance phenomenon and a typical alpha blocking phenomenon commonly exist in the eye closure events during daytime simulated driving experiments. These two alpha-related phenomena prove to respectively represent two different sleepiness levels: the sleep onset and the relaxed wakefulness...
This study explored the hidden biomedical information from knee MR images for osteoarthritis prediction. We have computed the Cartilage Damage Index (CDI) information from 36 informative locations on tibiofemoral compartment from 3D MR imaging reconstruction and used PCA analysis to process the feature set. The processed feature set and original raw feature set were severed as input to four machine...
As high-throughput sequencing technologies are generating vast amounts of data, there is urgent need to develop efficient algorithms for sequencing data compression. Existing methods usually dispatch the similar sequences into the same bucket based on their same minimizer, that is the lexicographical smallest k-mer within the sequence, for data compression. However, when the sequencing error existed...
Data collection of cellular processes (such as Microtubules (MTs) Dynamic Instability) using optical microscopes, are often threatened by either destruction of the specimen or the probe; thereby limiting the extensive period of time that the data can be collected. This leads to scarcity of data. Due to this, we encounter non-uniform sampling of the MT dynamic instability phenomenon relative to the...
Chronic Kidney Disease (CKD) is an increasingly prevalent condition affecting 13% of the US population. The disease is often a silent condition, making its diagnosis challenging. Identifying CKD stages from standard office visit records can help in early detection of the disease and lead to timely intervention. The dataset we use is highly imbalanced. We propose a hierarchical meta-classification...
Many rare and common genetic variants, including SNPs and CNVs, are reported to be associated with mental disorders, yet more remain to be discovered. However, despite the large amount of high-throughput genomics data, there is a lack of integrative methods to systematically prioritize variants that confer susceptibility to mental disorders in personal genomes. Here, we developed a computational tool:...
The collaborations of the diseases might be the key to understand the mechanism of the diseases since it is difficult to detect the role of complex genes and micro RNA in diseases. With the rapid development of technology, several metabolites of many kinds of diseases could be obtained by the advanced machines. Some diseases are related to several metabolites, and some metabolites have strong relationship...
The increasing adoption of health information technologies in the United States accelerates their potential to facilitate beneficial studies that combine large, complex data sets from multiple sources. The process of de-identification, by which identifiers are removed from the health information, mitigates privacy risks to individuals and thereby supports the secondary use of data for comparative...
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