scholarly journals Flexible Fitting of PROTAC Concentration–Response Curves with Changepoint Gaussian Processes

2021 ◽  
Vol 26 (9) ◽  
pp. 1212-1224
Author(s):  
Elizaveta Semenova ◽  
Maria Luisa Guerriero ◽  
Bairu Zhang ◽  
Andreas Hock ◽  
Philip Hopcroft ◽  
...  
Keyword(s):  
Gaussian Processes ◽  
New Technology ◽  
Maximum Effect ◽  
Data Sets ◽  
Response Curves ◽  
User Input ◽  
Common Solution ◽  
Screening Experiments ◽  
Point Of Departure ◽  
Reduced Data

A proteolysis-targeting chimera (PROTAC) is a new technology that marks proteins for degradation in a highly specific manner. During screening, PROTAC compounds are tested in concentration–response (CR) assays to determine their potency, and parameters such as the half-maximal degradation concentration (DC50) are estimated from the fitted CR curves. These parameters are used to rank compounds, with lower DC50 values indicating greater potency. However, PROTAC data often exhibit biphasic and polyphasic relationships, making standard sigmoidal CR models inappropriate. A common solution includes manual omitting of points (the so-called masking step), allowing standard models to be used on the reduced data sets. Due to its manual and subjective nature, masking becomes a costly and nonreproducible procedure. We therefore used a Bayesian changepoint Gaussian processes model that can flexibly fit both nonsigmoidal and sigmoidal CR curves without user input. Parameters such as the DC50, maximum effect Dmax, and point of departure (PoD) are estimated from the fitted curves. We then rank compounds based on one or more parameters and propagate the parameter uncertainty into the rankings, enabling us to confidently state if one compound is better than another. Hence, we used a flexible and automated procedure for PROTAC screening experiments. By minimizing subjective decisions, our approach reduces time and cost and ensures reproducibility of the compound-ranking procedure. The code and data are provided on GitHub ( https://github.com/elizavetasemenova/gp_concentration_response ).

scholarly journals Flexible fitting of PROTAC concentration-response curves with Gaussian Processes

2020 ◽  
Author(s):  
Elizaveta Semenova ◽  
Maria-Luisa Guerriero ◽  
Bairu Zhang ◽  
Andreas Hock ◽  
Philip Hopcroft ◽  
...  
Keyword(s):  
Gaussian Processes ◽  
New Technology ◽  
Maximum Effect ◽  
Response Curves ◽  
User Input ◽  
Screening Experiments ◽  
Specific Manner ◽  
Gp Model ◽  
Point Of Departure ◽  
Better Than

AbstractA proteolysis targeting chimera (PROTAC) is a new technology that marks proteins for degradation in a highly specific manner. During screening, PROTAC compounds are tested in concentration-response (CR) assays to determine their potency, and parameters such as the half-maximal degradation concentration (DC50) are estimated from the fitted CR curves. These parameters are used to rank compounds, with lower DC50 values indicating greater potency. However, PROTAC data often exhibit bi-phasic and poly-phasic relationships, making standard sigmoidal CR models inappropriate. We therefore used a Bayesian Gaussian Processes (GP) model that can flexibly fit both non-sigmoidal and sigmoidal CR curves without user input. Parameters, such as the DC50, the maximum effect Dmax, and the point of departure (PoD) are estimated from the fitted curves. We then rank compounds based on one or more parameters, and propagate the parameter uncertainty into the rankings, enabling us to confidently state if one compound is better than another. Hence, we used a flexible and automated procedure for PROTAC screening experiments. The code and data are provided on Github (https://github.com/elizavetasemenova/gp_concentration_response).

Bayesian Trigonometric Support Vector Classifier

2003 ◽  
Vol 15 (9) ◽  
pp. 2227-2254 ◽  
Author(s):  
Wei Chu ◽  
S. Sathiya Keerthi ◽  
Chong Jin Ong
Keyword(s):  
Loss Function ◽  
Gaussian Processes ◽  
Likelihood Function ◽  
Support Vector ◽  
Data Sets ◽  
Model Adaptation ◽  
Bayesian Techniques ◽  
Benchmark Data ◽  
Support Vector Classifier ◽  
Set Up

This letter describes Bayesian techniques for support vector classification. In particular, we propose a novel differentiable loss function, called the trigonometric loss function, which has the desirable characteristic of natural normalization in the likelihood function, and then follow standard gaussian processes techniques to set up a Bayesian framework. In this framework, Bayesian inference is used to implement model adaptation, while keeping the merits of support vector classifier, such as sparseness and convex programming. This differs from standard gaussian processes for classification. Moreover, we put forward class probability in making predictions. Experimental results on benchmark data sets indicate the usefulness of this approach.

scholarly journals Physiologically based kinetic modelling based prediction of in vivo rat and human acetylcholinesterase (AChE) inhibition upon exposure to diazinon

2021 ◽  
Author(s):  
Shensheng Zhao ◽  
Sebastiaan Wesseling ◽  
Bert Spenkelink ◽  
Ivonne M. C. M. Rietjens
Keyword(s):  
Dose Response ◽  
Kinetic Modelling ◽  
Ache Inhibition ◽  
Response Curves ◽  
Alternative Testing ◽  
Dose Response Curves ◽  
Physiologically Based Kinetic Modelling ◽  
Point Of Departure

AbstractThe present study predicts in vivo human and rat red blood cell (RBC) acetylcholinesterase (AChE) inhibition upon diazinon (DZN) exposure using physiological based kinetic (PBK) modelling-facilitated reverse dosimetry. Due to the fact that both DZN and its oxon metabolite diazoxon (DZO) can inhibit AChE, a toxic equivalency factor (TEF) was included in the PBK model to combine the effect of DZN and DZO when predicting in vivo AChE inhibition. The PBK models were defined based on kinetic constants derived from in vitro incubations with liver fractions or plasma of rat and human, and were used to translate in vitro concentration–response curves for AChE inhibition obtained in the current study to predicted in vivo dose–response curves. The predicted dose–response curves for rat matched available in vivo data on AChE inhibition, and the benchmark dose lower confidence limits for 10% inhibition (BMDL10 values) were in line with the reported BMDL10 values. Humans were predicted to be 6-fold more sensitive than rats in terms of AChE inhibition, mainly because of inter-species differences in toxicokinetics. It is concluded that the TEF-coded DZN PBK model combined with quantitative in vitro to in vivo extrapolation (QIVIVE) provides an adequate approach to predict RBC AChE inhibition upon acute oral DZN exposure, and can provide an alternative testing strategy for derivation of a point of departure (POD) in risk assessment.

Exploring Imputation Techniques for Missing Data in Transportation Management Systems

2003 ◽  
Vol 1836 (1) ◽  
pp. 132-142 ◽  
Author(s):  
Brian L. Smith ◽  
William T. Scherer ◽  
James H. Conklin
Keyword(s):  
Missing Data ◽  
Urban Areas ◽  
Large Scale ◽  
Management Systems ◽  
Data Sets ◽  
Transportation Industry ◽  
Transportation Management ◽  
Reduced Data ◽  
Natural Characteristics

Many states have implemented large-scale transportation management systems to improve mobility in urban areas. These systems are highly prone to missing and erroneous data, which results in drastically reduced data sets for analysis and real-time operations. Imputation is the practice of filling in missing data with estimated values. Currently, the transportation industry generally does not use imputation as a means for handling missing data. Other disciplines have recognized the importance of addressing missing data and, as a result, methods and software for imputing missing data are becoming widely available. The feasibility and applicability of imputing missing traffic data are addressed, and a preliminary analysis of several heuristic and statistical imputation techniques is performed. Preliminary results produced excellent performance in the case study and indicate that the statistical techniques are more accurate while maintaining the natural characteristics of the data.

Folksonomy

2011 ◽  
pp. 877-891
Author(s):  
Katrin Weller ◽  
Isabella Peters ◽  
Wolfgang G. Stock
Keyword(s):  
Large Data ◽  
Document Retrieval ◽  
Point Of View ◽  
Knowledge Organization ◽  
Data Sets ◽  
Document Collections ◽  
User Input ◽  
Indexing Method ◽  
Indexing And Retrieval ◽  
Locating Information

This chapter discusses folksonomies as a novel way of indexing documents and locating information based on user generated keywords. Folksonomies are considered from the point of view of knowledge organization and representation in the context of user collaboration within the Web 2.0 environments. Folksonomies provide multiple benefits which make them a useful indexing method in various contexts; however, they also have a number of shortcomings that may hamper precise or exhaustive document retrieval. The position maintained is that folksonomies are a valuable addition to the traditional spectrum of knowledge organization methods since they facilitate user input, stimulate active language use and timeliness, create opportunities for processing large data sets, and allow new ways of social navigation within document collections. Applications of folksonomies as well as recommendations for effective information indexing and retrieval are discussed.

Synergistic effects of oPL and insulin on glycogen metabolism in fetal rat hepatocytes

1984 ◽  
Vol 247 (6) ◽  
pp. E714-E718
Author(s):  
M. Freemark ◽  
S. Handwerger
Keyword(s):  
Synergistic Effects ◽  
Fetal Liver ◽  
Glycogen Synthesis ◽  
Rat Hepatocytes ◽  
Glycogen Metabolism ◽  
Placental Lactogen ◽  
Maximum Effect ◽  
Hepatic Glycogen ◽  
Response Curves ◽  
Glucose Incorporation

The interactions between ovine placental lactogen (oPL) and insulin in the regulation of fetal liver glycogen metabolism have been studied in cultured hepatocytes from fetal rats on day 20 of gestation. Both oPL (0.75–22.5 micrograms/ml) and insulin (0.01–1 microM) stimulated dose-dependent increases in [14C]glucose incorporation into glycogen. However, the dose-response curves for the two hormones were not parallel and the maximum effect of oPL was 3.4 times greater than that of insulin (P less than 0.001). The two hormones had synergistic effects on [14C]glucose incorporation at low concentrations and additive effects at maximum concentrations. Ovine growth hormone (oGH) also stimulated [14C]glucose incorporation into glycogen but with a potency only 12.3% that of oPL. Cycloheximide (20 microM) abolished the stimulation of [14C]glucose incorporation by insulin (1 microM), oPL (5 micrograms/ml), and oGH (100 micrograms/ml). Although the glycogenic actions of oPL and insulin may depend on new protein synthesis, the results of these studies suggest that these hormones stimulate glycogen synthesis in fetal liver by different mechanisms. Because the glycogenic actions of oPL are potentiated by insulin, these hormones may act in concert to promote hepatic glycogen storage in the fetus.

scholarly journals Using reduced data sets ISCCP-B2 from the Meteosat satellites to assess surface solar irradiance

Solar Energy ◽  
2007 ◽  
Vol 81 (2) ◽  
pp. 240-253 ◽  
Author(s):  
M. Lefèvre ◽  
L. Wald ◽  
L. Diabaté
Keyword(s):  
Solar Irradiance ◽  
Data Sets ◽  
Reduced Data

scholarly journals Protein crystallography with a novel large-area pixel detector

2006 ◽  
Vol 39 (4) ◽  
pp. 550-557 ◽  
Author(s):  
Gregor Hülsen ◽  
Christian Broennimann ◽  
Eric F. Eikenberry ◽  
Armin Wagner
Keyword(s):  
Single Photon ◽  
New Technology ◽  
Photon Counting ◽  
Data Sets ◽  
Pixel Detector ◽  
Large Area ◽  
Single Photon Counting ◽  
Continuous Rotation ◽  
Under Construction ◽  
Paul Scherrer

The PILATUS 1M detector, developed at the Paul Scherrer Institut, is a single-photon-counting hybrid pixel detector designed for macromolecular crystallography. With more than 1 million pixels covering an area of 243 × 210 mm, it is the largest such device constructed to date. The detector features a narrow point spread function, very fast readout and a complete absence of electronic noise. Unfortunately, this prototype detector has numerous defective pixels and sporadic errors in counting that complicate its operation. With appropriate experimental design, it was largely possible to work around these problems and successfully demonstrate the application of this technology to structure determination. Conventional coarse ϕ-sliced data were collected on thaumatin and a refined electron density map was produced that showed the features expected of a map at 1.6 Å resolution. The results were compared with the performance of a reference charge-coupled device detector: the pixel detector is superior in speed, but showed higherR-factors because of the counting errors. Complete fine ϕ-sliced data sets recorded in the continuous-rotation mode showed the predicted advantages of this data collection strategy and demonstrated the expected reduction ofR-factors at high resolution. A new readout chip has been tested and shown to be free from the defects of its predecessor; a PILATUS 6M detector incorporating this new technology is under construction.

Reduction and analysis of SANS and USANS data using IGOR Pro

2006 ◽  
Vol 39 (6) ◽  
pp. 895-900 ◽  
Author(s):  
Steven R. Kline
Keyword(s):  
Neutron Scattering ◽  
Small Angle ◽  
Software Package ◽  
Small Angle Neutron Scattering ◽  
Data Sets ◽  
Graphical Interface ◽  
Model Independent ◽  
Analysis Models ◽  
Reduced Data ◽  
Modular Format

A software package is presented for performing reduction and analysis of small-angle neutron scattering (SANS) and ultra-small-angle neutron scattering (USANS) data. A graphical interface has been developed to visualize and quickly reduce raw SANS and USANS data into one- or two-dimensional formats for interpretation. The resulting reduced data can then be analyzed using model-independent methods or non-linear fitting to one of a large and growing catalog of included structural models. The different instrumental smearing effects for slit-smeared USANS and pinhole-smeared SANS data are handled automatically during analysis. In addition, any number of SANS and USANS data sets can be analyzed simultaneously. The reduction operations and analysis models are written in a modular format for extensibility, allowing users to contribute code and models for distribution to all users. The software package is based on Igor Pro, providing freely distributable and modifiable code that runs on Macintosh and Windows operating systems.

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