2206A Student Center
Computationally-Determined FliC Linked Recognition Epitope Micelles for Burkholderia Vaccination
Human disease caused by Burkholderia spp. is a serious problem in many parts of the world including infection of immunocompromised patients and those with cystic fibrosis. Traditional antimicrobial therapy is protracted and problematic. The goal of this cooperated structural vaccinology project is to identify novel immune dominant and cryptic linked B and T cell epitopes for the development of efficacious vaccines against Burkholderia mallei and B. pseudomallei flagellar protein FliC. For our in silico part works, we will screen the greater than 30 known genomic/proteomic data sets of Burkholderia spp. including both pathogenic and non-pathogenic strains. Identified sequences of interest will be scored for inclusion of both a putative T cell (linear) epitope and a B cell (linear or conformational) epitope while eliminating potentially self-reactive mimetic epitopes. Then candidate FliC peptides will be synthesized and tested by cooperated labs on campus.
S110 Memorial Union
Dynamically Predict Risk of 30-day Hospital Readmission for Diabetic Patients Using a Data Stream Mining Approach
Early hospital readmissions can negatively impact patients’ quality of life and hospitals’ income. Intensive efforts have been made to develop 30-day hospital readmission risk prediction models. Most of the reported statistical and machine learning models were built with static datasets. The reality, however, is that data arrives sequentially and the trend may evolve over time. As a result, many static models failed in practice. To make the models up-to-date, the traditional approach is re-training them from scratch periodically, which can be expensive. Most importantly, it is difficult to determine and discard outdated knowledge from the models using this approach. In this work, we aim to develop a self-adaptive 30-day hospital readmission prediction model using a data stream mining approach. We used a diabetic inpatient encounter data stream to dynamically train and evaluate models based on incremental learning algorithms. This approach and preliminary results will be presented in the seminar.
2206A Student Center
Informatics Approaches to Uncover the Molecular Mechanisms of Endometriosis in Clinical Patients
Endometriosis, a complex and common gynecological disorder affecting 5–10% of reproductive-age women, is characterized by the growth of endometrial tissue outside of the uterine cavity. Accumulating evidence indicates that various epigenetic aberrations are associated with endometriosis. In our study, we have methylation data and clinical information on 80 patients (36 controls and 44 cases) from a clinical study. Our objectives are to identify the genomic regions associated with endometriosis and identify the specific genes associated with endometriosis after adjusting for potential confounding variables. We have developed a bioinformatics methylation data analysis pipeline in-house using several open source tools including FastQC, Bowtie2 and R packages. In this seminar, the informatics approach for the data analysis will be presented.
2206A Student Center
A Deep Natural Network Method for Predicting Mitochondrially Localized Proteins in Plants
Targeting and translocation of proteins to the appropriate subcellular compartments is crucial for cell organization and function. Some newly synthesized proteins are transported to mitochondria with the assistance of complex targeting sequences containing either an N-terminal pre-sequence or a multitude of internal signals. Compared with experimental approaches, computational predictions provide an efficient way to infer subcellular localization for any given protein. However, it is still challenging to predict plant mitochondrially localized proteins accurately due to various limitations. Consequently, the performance of current tools is unsatisfactory. We present a novel computational approach for large-scale prediction of plant mitochondrial proteins. We collected subcellular localization data for plant proteins from databases and the literature, and extracted different types of features from the training data, including amino acid composition, protein sequence profile, and gene co-expression information. We then trained deep neural networks for predicting plant mitochondrial proteins. Benchmarked on an independent dataset, our method achieves considerable improvements over existing tools in predicting mitochondria-localized proteins in plants. We improved the true positive rate by 10-30% over three of the state-of-the-art tools under similar specificity levels. We also applied our method to predict candidate mitochondrial proteins on the whole proteome of Arabidopsis and potato.
2206A Student Center
Geonmic Selection using Deep Learning method
Genomic selection is an approach to enhance the quantitative traits in plant and animal breeding program at early stage using whole genome molecular markers, especially for long life-cycle species. It’s based on the assumption that all quantitative trait loci (QTL) tend to be in linkage disequilibrium with at least on marker. Statistical methods, such as ridge regression, best linear unbiased prediction (RR-BLUP), Bayes A, Bayesian LASSO are widely used for genomic selection problem works SNP matrix. Other machine learning methods (random forrest, support vector machine and neural network) are also been applied for this study. In this work, we are developing a deep learning method using long short term memory (LSTM) recurrent network on a public standard dataset of Pinus taeda (loblolly pine) . The stem height trait (HT, cm) was measured across 861 individuals genotyped with 4,853 SNPs derived from 32 parents. The genomic estimated breeding values (GEBV) was calculated using 10-fold cross-validation method and accuracy was measured using Pearson correlation coefficient between GEBV and observed values.
 Hoerl, Arthur E., and Robert W. Kennard. “Ridge regression: biased estimation for nonorthogonal problems.” Technometrics 42.1 (2000): 80-86.
 Meuwissen, T.H.E., B.J. Hayes, and M.E. Goddard. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829.
 Park, Trevor, and George Casella. “The bayesian lasso.” Journal of the American Statistical Association 103.482 (2008): 681-686.
 Heslot, Nicolas, et al. “Genomic selection in plant breeding: a comparison of models.” Crop Science 52.1 (2012): 146-160.
 Resende, Márcio FR, et al. “Accuracy of genomic selection methods in a standard data set of loblolly pine (Pinus taeda L.).” Genetics 190.4 (2012): 1503-1510.
« Previous 1 2 3 4 Next »