Biology subjects

Estiri, H.

Publications and source records attributed to Estiri, H..

4 recordsLinked to original sources

bioRxiv2024

Development of a highly active engineered PETase enzyme for polyester degradation

Polyethylene terephthalate (PET) accounts for {approx}6% of global plastic production, contributing considerably to the global solid waste stream and environmental plastic pollution. Since the discovery of PET-depolymerizing enzymes, enzymatic PET recycling has been regarded as a promising method for plastic disposal, particularly in the context of a circular economy strategy. However, as the PET degrading enzymes developed so far suffer from relatively limited thermostability, low catalytic efficiency, as well as degradation intermediate-induced inhibition, their large scale industrial applications are still largely hampered. To overcome these limitations, we used in silico protein design methods to develop an engineered Leaf-branch Compost Cutinase (LCC), named DRK3, that features enhanced thermal stability and PETase activity relative to the current gold standard LCC enzyme (LCC-ICCG). DRK3 features a 4.1{degrees}C increase in melting temperature relative to the LCC-ICCG enzyme. Under optimal reaction conditions (68{degrees}C), the DRK3 enzyme hydrolyzes amorphous PET material into TPA with a 2-fold higher efficiency compared to LCC-ICCG. Owing to its enhanced properties, DRK3 may be a promising candidate for future applications in industrial PET recycling processes. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=138 SRC="FIGDIR/small/602061v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@125688eorg.highwire.dtl.DTLVardef@f360f8org.highwire.dtl.DTLVardef@634486org.highwire.dtl.DTLVardef@127f622_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2019

High-throughput Phenotyping with Temporal Sequences

ObjectiveHigh-throughput electronic phenotyping algorithms can accelerate translational research using data from electronic health record (EHR) systems. The temporal information buried in EHRs are often underutilized in developing computational phenotypic definitions. The objective of this study is to develop a high-throughput phenotyping method, leveraging temporal sequential patterns of discrete events from electronic health records. Materials and MethodsWe develop a representation mining algorithm to extract five classes of representations from EHR diagnosis and medication records: the aggregated vector of the records (AVR), the traditional immediate sequential patterns (SPM), the transitive sequential patterns (tSPM), as well as two hybrid classes of SPM+AVR and tSPM+AVR. A final small set of representations were selected from each class using the MSMR dimensionality reduction algorithm. Using EHR data on 10 phenotypes from Mass General Brigham Biobank, we trained regularized logistic regression algorithms, which we validated using labeled data. ResultsPhenotyping with temporal sequences resulted in a superior classification performance across all 10 phenotypes compared with the AVR representations that are conventionally used in electronic phenotyping. Although this study only utilizes the diagnosis and medication records, the high-throughput algorithms classification performance was superior or similar to the performance of previously published electronic phenotyping algorithms. We characterize and evaluate the top transitive sequences of diagnosis records paired with the records of risk factors, symptoms, complications, medications, or vaccinations. DiscussionThe proposed high-throughput phenotyping approach enables seamless discovery of sequential record combinations that may be difficult to assume from raw EHR data. A transitive sequence can offer a more accurate characterization of the phenotype, compared with its individual components. Additionally, the identified transitive sequences of a given phenotype reflect the actual lived experiences of the patients with that particular disease. ConclusionSequential data representations provide a precise mechanism for incorporating raw EHR records into downstream Machine Learning.

bioinformatics↗

bioRxiv2019

A Clustering Approach for Detecting Implausible Observation Values in Electronic Health Records Data

BackgroundIdentifying implausible clinical observations (e.g., laboratory test and vital sign values) in Electronic Health Record (EHR) data using rule-based procedures is challenging. Anomaly/outlier detection methods can be applied as an alternative algorithmic approach to flagging such implausible values in EHRs.\n\nObjectiveThe primary objectives of this research were to develop and test an unsupervised clustering-based anomaly/outlier detection approach for detecting implausible observations in EHR data as an alternative algorithmic solution to the existing procedures.\n\nMethodsOur approach is built upon two underlying hypotheses that, (i) when there are large number of observations, implausible records should be sparse, and therefore (ii) if these data are clustered properly, clusters with sparse populations should represent implausible observations. To test these hypotheses, we applied an unsupervised clustering algorithm to EHR observation data on 50 laboratory tests. We tested different specifications of the clustering approach and computed confusion matrix indices against a set of silver-standard plausibility thresholds. We compared the results from the proposed approach with conventional anomaly detection (CAD) approachs, including standard deviation and Mahalanobis distance.\n\nResultsWe found that the clustering approach produced results with exceptional specificity and high sensitivity. Compared with the conventional anomaly detection approaches, our proposed clustering approach resulted in significantly smaller number of false positive cases.\n\nConclusionOur contributions include (i) a clustering approach for identifying implausible EHR observations, (ii) evidence that implausible observations are sparse in EHR laboratory test results, (iii) a parallel implementation of the clustering approach on i2b2 star schema, and (3) a set of silver-standard plausibility thresholds for 50 laboratory tests that can be used in other studies for validation. The proposed algorithmic solution can augment human decisions to improve data quality. Therefore, a workflow is needed to complement the algorithms job and initiate necessary actions that need to be taken in order to improve the quality of data.

bioinformatics↗

bioRxiv2018

Semi-supervised Encoding for Outlier Detection in Clinical Observation Data

Background and ObjectiveTo evaluate the utility of encoding for outlier detection in clinical observation data from Electronic Health Records (EHR).\n\nMethodsThis article presents a semi-supervise encoding approach (super-encoding) for constructing a non-linear exemplar data distribution from EHR data and detecting non-conforming observations as outliers. Two hypotheses are tested using experimental design and non-parametric hypothesis testing procedures to evaluate the outlier detection performance of the semi-supervised encoding approach and increasing demographic precision in encoding.\n\nResultsThe experiments involved applying 492 encoders to 30 laboratory tests extracted from the Research Patient Data Registry (RPDR) from Partners HealthCare. We report results obtained from 14,760 encoders. The semi-supervised encoders (super-encoders) outperformed conventional autoencoders in outlier detection. Adding age at observation to the baseline encoder (that only included observation value as the feature) slightly improved outlier detection. Top-nine performing encoders are introduced. The best outlier detection performance was from a semi-supervised encoder, with observation value as the single feature and a single hidden layer, built on one percent of the data and one percent reconstruction error. At least one encoder had a Youdens J index higher than 0.9999 for all 30 observations.\n\nConclusionGiven the multiplicity of distributions for a single observation in EHR data (i.e., same observation represented with different names or units), as well as non-linearity of human observations, encoding offers huge promises for outlier detection in large-scale data repositories.

bioinformatics↗