Browsing by Subject "Algorithms"
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Item 3D Reconstruction of Coronary Artery Vascular Smooth Muscle Cells.(PLOS, 2016) Luo, Tong; Chen, Huan; Kassab, Ghassan S.; Department of Biomedical Engineering, Purdue School of Engineering and Technology, IUPUIAims: The 3D geometry of individual vascular smooth muscle cells (VSMCs), which are essential for understanding the mechanical function of blood vessels, are currently not available. This paper introduces a new 3D segmentation algorithm to determine VSMC morphology and orientation. Methods and Results: A total of 112 VSMCs from six porcine coronary arteries were used in the analysis. A 3D semi-automatic segmentation method was developed to reconstruct individual VSMCs from cell clumps as well as to extract the 3D geometry of VSMCs. A new edge blocking model was introduced to recognize cell boundary while an edge growing was developed for optimal interpolation and edge verification. The proposed methods were designed based on Region of Interest (ROI) selected by user and interactive responses of limited key edges. Enhanced cell boundary features were used to construct the cell’s initial boundary for further edge growing. A unified framework of morphological parameters (dimensions and orientations) was proposed for the 3D volume data. Virtual phantom was designed to validate the tilt angle measurements, while other parameters extracted from 3D segmentations were compared with manual measurements to assess the accuracy of the algorithm. The length, width and thickness of VSMCs were 62.9±14.9μm, 4.6±0.6μm and 6.2±1.8μm (mean±SD). In longitudinal-circumferential plane of blood vessel, VSMCs align off the circumferential direction with two mean angles of -19.4±9.3° and 10.9±4.7°, while an out-of-plane angle (i.e., radial tilt angle) was found to be 8±7.6° with median as 5.7°. Conclusions: A 3D segmentation algorithm was developed to reconstruct individual VSMCs of blood vessel walls based on optical image stacks. The results were validated by a virtual phantom and manual measurement. The obtained 3D geometries can be utilized in mathematical models and leads a better understanding of vascular mechanical properties and function.Item Alt Event Finder: a tool for extracting alternative splicing events from RNA-seq data.(BMC, 2012) Zhou, Ao; Breese, Marcus R.; Hao, Yangyang; Edenberg, Howard J.; Li, Lang; Skaar, Todd C.; Liu, YunlongBACKGROUND: Alternative splicing increases proteome diversity by expressing multiple gene isoforms that often differ in function. Identifying alternative splicing events from RNA-seq experiments is important for understanding the diversity of transcripts and for investigating the regulation of splicing. RESULTS: We developed Alt Event Finder, a tool for identifying novel splicing events by using transcript annotation derived from genome-guided construction tools, such as Cufflinks and Scripture. With a proper combination of alignment and transcript reconstruction tools, Alt Event Finder is capable of identifying novel splicing events in the human genome. We further applied Alt Event Finder on a set of RNA-seq data from rat liver tissues, and identified dozens of novel cassette exon events whose splicing patterns changed after extensive alcohol exposure. CONCLUSIONS: Alt Event Finder is capable of identifying de novo splicing events from data-driven transcript annotation, and is a useful tool for studying splicing regulation.Item Automated pancreatic cyst screening using natural language processing: a new tool in the early detection of pancreatic cancer(Elsevier, 2015-05) Roch, Alexandra M.; Mehrabi, Saeed; Krishnan, Anand; Schmidt, Heidi E.; Kesterson, Joseph; Beesley, Chris; Dexter, Paul R.; Palakal, Matthew; Schmidt, C. Max; Department of Surgery, IU School of MedicineINTRODUCTION: As many as 3% of computed tomography (CT) scans detect pancreatic cysts. Because pancreatic cysts are incidental, ubiquitous and poorly understood, follow-up is often not performed. Pancreatic cysts may have a significant malignant potential and their identification represents a 'window of opportunity' for the early detection of pancreatic cancer. The purpose of this study was to implement an automated Natural Language Processing (NLP)-based pancreatic cyst identification system. METHOD: A multidisciplinary team was assembled. NLP-based identification algorithms were developed based on key words commonly used by physicians to describe pancreatic cysts and programmed for automated search of electronic medical records. A pilot study was conducted prospectively in a single institution. RESULTS: From March to September 2013, 566,233 reports belonging to 50,669 patients were analysed. The mean number of patients reported with a pancreatic cyst was 88/month (range 78-98). The mean sensitivity and specificity were 99.9% and 98.8%, respectively. CONCLUSION: NLP is an effective tool to automatically identify patients with pancreatic cysts based on electronic medical records (EMR). This highly accurate system can help capture patients 'at-risk' of pancreatic cancer in a registry.Item Candidate Genes from an FDA-Approved Algorithm Fail to Predict Opioid Use Disorder Risk in Over 450,000 Veterans(medRxiv, 2024-05-16) Davis, Christal N.; Jinwala, Zeal; Hatoum, Alexander S.; Toikumo, Sylvanus; Agrawal, Arpana; Rentsch, Christopher T.; Edenberg, Howard J.; Baurley, James W.; Hartwell, Emily E.; Crist, Richard C.; Gray, Joshua C.; Justice, Amy C.; Gelernter, Joel; Kember, Rachel L.; Kranzler, Henry R.; Biochemistry and Molecular Biology, School of MedicineImportance: Recently, the Food and Drug Administration gave pre-marketing approval to algorithm based on its purported ability to identify genetic risk for opioid use disorder. However, the clinical utility of the candidate genes comprising the algorithm has not been independently demonstrated. Objective: To assess the utility of 15 variants in candidate genes from an algorithm intended to predict opioid use disorder risk. Design: This case-control study examined the association of 15 candidate genetic variants with risk of opioid use disorder using available electronic health record data from December 20, 1992 to September 30, 2022. Setting: Electronic health record data, including pharmacy records, from Million Veteran Program participants across the United States. Participants: Participants were opioid-exposed individuals enrolled in the Million Veteran Program (n = 452,664). Opioid use disorder cases were identified using International Classification of Disease diagnostic codes, and controls were individuals with no opioid use disorder diagnosis. Exposures: Number of risk alleles present across 15 candidate genetic variants. Main outcome and measures: Predictive performance of 15 genetic variants for opioid use disorder risk assessed via logistic regression and machine learning models. Results: Opioid exposed individuals (n=33,669 cases) were on average 61.15 (SD = 13.37) years old, 90.46% male, and had varied genetic similarity to global reference panels. Collectively, the 15 candidate genetic variants accounted for 0.4% of variation in opioid use disorder risk. The accuracy of the ensemble machine learning model using the 15 genes as predictors was 52.8% (95% CI = 52.1 - 53.6%) in an independent testing sample. Conclusions and relevance: Candidate genes that comprise the approved algorithm do not meet reasonable standards of efficacy in predicting opioid use disorder risk. Given the algorithm's limited predictive accuracy, its use in clinical care would lead to high rates of false positive and negative findings. More clinically useful models are needed to identify individuals at risk of developing opioid use disorder.Item CGPE: an integrated online server for Cancer Gene and Pathway Exploration(Oxford University Press, 2021) Liu, Jiannan; Dong, Chuanpeng; Liu, Yunlong; Wu, Huanmei; BioHealth Informatics, School of Informatics and ComputingSummary: Cancer Gene and Pathway Explorer (CGPE) is developed to guide biological and clinical researchers, especially those with limited informatics and programming skills, performing preliminary cancer-related biomedical research using transcriptional data and publications. CGPE enables three user-friendly online analytical and visualization modules without requiring any local deployment. The GenePub HotIndex applies natural language processing, statistics and association discovery to provide analytical results on gene-specific PubMed publications, including gene-specific research trends, cancer types correlations, top-related genes and the WordCloud of publication profiles. The OnlineGSEA enables Gene Set Enrichment Analysis (GSEA) and results visualizations through an easy-to-follow interface for public or in-house transcriptional datasets, integrating the GSEA algorithm and preprocessed public TCGA and GEO datasets. The preprocessed datasets ensure gene sets analysis with appropriate pathway alternation and gene signatures. The CellLine Search presents evidence-based guidance for cell line selections with combined information on cell line dependency, gene expressions and pathway activity maps, which are valuable knowledge to have before conducting gene-related experiments. In a nutshell, the CGPE webserver provides a user-friendly, visual, intuitive and informative bioinformatics tool that allows biomedical researchers to perform efficient analyses and preliminary studies on in-house and publicly available bioinformatics data.Item Clinical Pharmacology of Antihypertensive Therapy for the Treatment of Hypertension in CKD(American Society of Nephrology, 2019-05-07) Sinha, Arjun D.; Agarwal, Rajiv; Medicine, School of MedicineCKD is common and frequently complicated with hypertension both predialysis and in ESKD. As a major modifiable risk factor for cardiovascular disease in this high-risk population, treatment of hypertension in CKD is important. We review the mechanisms and indications for the major classes of antihypertensive drugs, including angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, β-adrenergic blocking agents, dihydropyridine calcium channel blockers, thiazide diuretics, loop diuretics, mineralocorticoid receptor blockers, direct vasodilators, and centrally acting α-agonists. Recent evidence suggests that β-adrenergic blocking agents may have a greater role in patients on dialysis and that thiazide diuretics may have a greater role in patients with advanced CKD. We conclude with sharing our general prescribing algorithm for both patients with predialysis CKD and patients with ESKD on dialysis.Item Comparison of Automated Posttonsillectomy Bleed Capture With Self-report(American Medical Association, 2017-08-01) Phillips, D. Ryan; Ellsperman, Susan E.; Matt, Bruce H.; Zarzaur, Ben L.; Otolaryngology -- Head and Neck Surgery, School of MedicineImportance: Tonsillectomy is one of the most common procedures performed by otolaryngologists and is associated with postoperative bleeding. Bleed rates are usually monitored by self-report. Objective: To evaluate whether using automated capture and reporting of pediatric posttonsillectomy bleeding is feasible and accurate compared with traditional self-reporting by the surgical team. Design, Setting, and Participants: An automated complication-reporting algorithm was designed to query the local health information exchange and then tested against self-reported tonsillectomy complication data collected from January 1, 2014, through December 31, 2015, at a tertiary pediatric hospital. The algorithm identified patients undergoing tonsillectomy and searched their postoperative encounters for a hand-selected set of diagnosis codes from the International Classification of Diseases, Ninth Revision and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision and free-text words to identify complication events. Five months of the 2014-2015 data set were used to help design the algorithm. Data from the remaining 19 months were compared with self-reported complications. Main Outcomes and Measures: Automated system findings compared with self-reported bleeding events. Results: During the 19-month period, 1017 tonsillectomies were performed. We compared the algorithm's effectiveness in finding tonsillectomy and adenotonsillectomy procedures for the evaluated surgeons with the hand-reviewed master tonsillectomy list. The algorithm reported 51 false-positive (5.01% missed) and 74 false-negative (7.28% misidentified) procedures. The algorithm agreed with self-report for 986 tonsillectomies and disagreed on 31 cases (3.05%) (κ = 0.69; 95% CI, 0.66-0.73). The algorithm was found to be sensitive to correctly identifying 60.53% (95% CI, 48.63%-71.34%) of tonsillectomies as having bleeding complications, with a specificity of 98.30% (95% CI, 97.19%-98.99%). Conclusions and Relevance: Capture of posttonsillectomy bleeding is possible through an automatic search of the medical record, although the algorithm will require continued refinement. Leveraging health information exchange data increases the possibilities of capturing complications at hospitals outside the local health system. Use of these algorithms will allow repeatable automated feedback to be provided to surgeons on a cyclical basis.Item Complex Proteoform Identification Using Top-Down Mass Spectrometry(2018-12) Kou, Qiang; Wu, Huanmei; Liu, Xiaowen; Liu, Yunlong; Al Hasan, MohammadProteoforms are distinct protein molecule forms created by variations in genes, gene expression, and other biological processes. Many proteoforms contain multiple primary structural alterations, including amino acid substitutions, terminal truncations, and posttranslational modifications. These primary structural alterations play a crucial role in determining protein functions: proteoforms from the same protein with different alterations may exhibit different functional behaviors. Because top-down mass spectrometry directly analyzes intact proteoforms and provides complete sequence information of proteoforms, it has become the method of choice for the identification of complex proteoforms. Although instruments and experimental protocols for top-down mass spectrometry have been advancing rapidly in the past several years, many computational problems in this area remain unsolved, and the development of software tools for analyzing such data is still at its very early stage. In this dissertation, we propose several novel algorithms for challenging computational problems in proteoform identification by top-down mass spectrometry. First, we present two approximate spectrum-based protein sequence filtering algorithms that quickly find a small number of candidate proteins from a large proteome database for a query mass spectrum. Second, we describe mass graph-based alignment algorithms that efficiently identify proteoforms with variable post-translational modifications and/or terminal truncations. Third, we propose a Markov chain Monte Carlo method for estimating the statistical signi ficance of identified proteoform spectrum matches. They are the first efficient algorithms that take into account three types of alterations: variable post-translational modifications, unexpected alterations, and terminal truncations in proteoform identification. As a result, they are more sensitive and powerful than other existing methods that consider only one or two of the three types of alterations. All the proposed algorithms have been incorporated into TopMG, a complete software pipeline for complex proteoform identification. Experimental results showed that TopMG significantly increases the number of identifications than other existing methods in proteome-level top-down mass spectrometry studies. TopMG will facilitate the applications of top-down mass spectrometry in many areas, such as the identification and quantification of clinically relevant proteoforms and the discovery of new proteoform biomarkers.Item Connecting Rodent and Human Pharmacokinetic Models for the Design and Translation of Glucose-Responsive Insulin(American Diabetes Association, 2020-08) Yang, Jing Fan; Gong, Xun; Bakh, Naveed A.; Carr, Kelley; Phillips, Nelson F.B.; Ismail-Beigi, Faramarz; Weiss, Michael A.; Strano, Michael S.; Biochemistry and Molecular Biology, School of MedicineDespite considerable progress, development of glucose-responsive insulins (GRIs) still largely depends on empirical knowledge and tedious experimentation-especially on rodents. To assist the rational design and clinical translation of the therapeutic, we present a Pharmacokinetic Algorithm Mapping GRI Efficacies in Rodents and Humans (PAMERAH) built upon our previous human model. PAMERAH constitutes a framework for predicting the therapeutic efficacy of a GRI candidate from its user-specified mechanism of action, kinetics, and dosage, which we show is accurate when checked against data from experiments and literature. Results from simulated glucose clamps also agree quantitatively with recent GRI publications. We demonstrate that the model can be used to explore the vast number of permutations constituting the GRI parameter space and thereby identify the optimal design ranges that yield desired performance. A design guide aside, PAMERAH more importantly can facilitate GRI's clinical translation by connecting each candidate's efficacies in rats, mice, and humans. The resultant mapping helps to find GRIs that appear promising in rodents but underperform in humans (i.e., false positives). Conversely, it also allows for the discovery of optimal human GRI dynamics not captured by experiments on a rodent population (false negatives). We condense such information onto a "translatability grid" as a straightforward, visual guide for GRI development.Item Conventional, Bayesian, and Modified Prony's methods for characterizing fast and slow waves in equine cancellous bone(AIP Publishing, 2015-08) Groopman, Amber M.; Katz, Jonathan I.; Fujita, Fuminori; Matsukawa, Mami; Mizuno, Katsunori; Wear, Keith A.; Miller, James G.; Department of Radiology and Imaging Sciences, IU School of MedicineConventional, Bayesian, and the modified least-squares Prony's plus curve-fitting (MLSP + CF) methods were applied to data acquired using 1 MHz center frequency, broadband transducers on a single equine cancellous bone specimen that was systematically shortened from 11.8 mm down to 0.5 mm for a total of 24 sample thicknesses. Due to overlapping fast and slow waves, conventional analysis methods were restricted to data from sample thicknesses ranging from 11.8 mm to 6.0 mm. In contrast, Bayesian and MLSP + CF methods successfully separated fast and slow waves and provided reliable estimates of the ultrasonic properties of fast and slow waves for sample thicknesses ranging from 11.8 mm down to 3.5 mm. Comparisons of the three methods were carried out for phase velocity at the center frequency and the slope of the attenuation coefficient for the fast and slow waves. Good agreement among the three methods was also observed for average signal loss at the center frequency. The Bayesian and MLSP + CF approaches were able to separate the fast and slow waves and provide good estimates of the fast and slow wave properties even when the two wave modes overlapped in both time and frequency domains making conventional analysis methods unreliable.