3 research outputs found
Evolution-aware Protein Structure Comparison and Applications in Protein-Protein Interaction Prediction
Comparison of protein structures provide insights into the function and interactions of proteins and enhance our understanding of biomolecular mechanisms driving life and disease. Available protein structure comparison methods are based solely on the 3D geometric similarity, limiting their ability to detect functionally relevant correspondences between the residues of the proteins, especially for distantly related homologous proteins. However, non-geometric features contained in primary sequence and evolutionary history of proteins contain valuable information that can enhance detection of such similarities. In this study, we introduced a new method to incorporate additional biochemical and evolutionary features of the proteins being compared. We proposed UniScore as a new structure similarity score, which integrates geometric similarity, sequence similarity, and evolutionary profiles of the proteins. We further developed a corresponding Unialign algorithm for finding structural alignment of proteins with near-optimal UniScore. We evaluated Unialign in terms of the consistency between the alignments it produces with human-curated alignments, calculated by the fraction of correctly aligned residues. Experimental results show that UniAlign outperforms other structural programs in aligning proteins from the NCBI's human-curated Conserved Domain Database. Unialign's ability in detecting functionally important structural similarities is utilized in an application to discover interactions between HIV-1 ENV protein (gp41 and gp120) and human proteins. Structural compatibility of an HIV-human interaction pairs are evaluated via geometric, biochemical, and evolutionary features and a prediction model is developed using a Support Vector Machine. This provides the first model for prediction of interactions that can also generate a protein-protein 3D complex. The results of the HIV-human interaction study have discovered novel virus-host interactions as well as potential clinical targets for therapeutic intervention.Ph.D., Biomedical Engineering -- Drexel University, 201
Motion Artifact Processing Techniques for Physiological Signals
The combination of reducing birth rate and increasing life expectancy continues to drive
the demographic shift toward an ageing population and this is placing an ever-increasing
burden on our healthcare systems. The urgent need to address this so called healthcare
\time bomb" has led to a rapid growth in research into ubiquitous, pervasive and
distributed healthcare technologies where recent advances in signal acquisition, data
storage and communication are helping such systems become a reality. However, similar
to recordings performed in the hospital environment, artifacts continue to be a major
issue for these systems. The magnitude and frequency of artifacts can vary signicantly
depending on the recording environment with one of the major contributions due to
the motion of the subject or the recording transducer. As such, this thesis addresses
the challenges of the removal of this motion artifact removal from various physiological
signals.
The preliminary investigations focus on artifact identication and the tagging of physiological
signals streams with measures of signal quality. A new method for quantifying
signal quality is developed based on the use of inexpensive accelerometers which facilitates
the appropriate use of artifact processing methods as needed. These artifact
processing methods are thoroughly examined as part of a comprehensive review of the
most commonly applicable methods. This review forms the basis for the comparative
studies subsequently presented. Then, a simple but novel experimental methodology
for the comparison of artifact processing techniques is proposed, designed and tested
for algorithm evaluation. The method is demonstrated to be highly eective for the
type of artifact challenges common in a connected health setting, particularly those concerned
with brain activity monitoring. This research primarily focuses on applying the
techniques to functional near infrared spectroscopy (fNIRS) and electroencephalography
(EEG) data due to their high susceptibility to contamination by subject motion related
artifact.
Using the novel experimental methodology, complemented with simulated data, a comprehensive
comparison of a range of artifact processing methods is conducted, allowing
the identication of the set of the best performing methods. A novel artifact removal
technique is also developed, namely ensemble empirical mode decomposition with canonical
correlation analysis (EEMD-CCA), which provides the best results when applied on
fNIRS data under particular conditions. Four of the best performing techniques were
then tested on real ambulatory EEG data contaminated with movement artifacts comparable
to those observed during in-home monitoring.
It was determined that when analysing EEG data, the Wiener lter is consistently
the best performing artifact removal technique. However, when employing the fNIRS
data, the best technique depends on a number of factors including: 1) the availability
of a reference signal and 2) whether or not the form of the artifact is known. It is
envisaged that the use of physiological signal monitoring for patient healthcare will grow
signicantly over the next number of decades and it is hoped that this thesis will aid in
the progression and development of artifact removal techniques capable of supporting
this growth