Discovery of a new family of chromium ethylene polymerisation catalysts using high throughput screening methodologyElectronic supplementary information (ESI) available: experimental section. See http://www.rsc.org/suppdata/cc/b2/b202037h/

2002 ◽  
pp. 1038-1039 ◽  
Author(s):  
David J. Jones ◽  
Vernon C. Gibson ◽  
Simon M. Green ◽  
Peter J. Maddox
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Supplementary Information ◽  
Experimental Section ◽  
New Family ◽  
Ethylene Polymerisation

ChemInform Abstract: Investigation of a New Family of Chiral Ligands for Enantioselective Catalysis via Parallel Synthesis and High-Throughput Screening.

ChemInform ◽  
2010 ◽  
Vol 30 (1) ◽  
pp. no-no
Author(s):  
C. GENNARI ◽  
S. CECCARELLI ◽  
U. PIARULLI ◽  
C. A. G. N. MONTALBETTI ◽  
R. F. W. JACKSON
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Parallel Synthesis ◽  
Chiral Ligands ◽  
Enantioselective Catalysis ◽  
New Family

Issues of Z-factor and an approach to avoid them for quality control in high-throughput screening studies

2020 ◽  
Author(s):  
Xiaohua Douglas Zhang ◽  
Dandan Wang ◽  
Shixue Sun ◽  
Heping Zhang
Keyword(s):  
Quality Control ◽  
High Throughput ◽  
High Throughput Screening ◽  
Theoretical Basis ◽  
Sampling Error ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Automation Technology ◽  
General Public License

Abstract Motivation High-throughput screening (HTS) is a vital automation technology in biomedical research in both industry and academia. The well-known Z-factor has been widely used as a gatekeeper to assure assay quality in an HTS study. However, many researchers and users may not have realized that Z-factor has major issues. Results In this article, the following four major issues are explored and demonstrated so that researchers may use the Z-factor appropriately. First, the Z-factor violates the Pythagorean theorem of statistics. Second, there is no adjustment of sampling error in the application of the Z-factor for quality control (QC) in HTS studies. Third, the expectation of the sample-based Z-factor does not exist. Fourth, the thresholds in the Z-factor-based criterion lack a theoretical basis. Here, an approach to avoid these issues was proposed and new QC criteria under homoscedasticity were constructed so that researchers can choose a statistically grounded criterion for QC in the HTS studies. We implemented this approach in an R package and demonstrated its utility in multiple CRISPR/CAS9 or siRNA HTS studies. Availability and implementation The R package qcSSMDhomo is freely available from GitHub: https://github.com/Karena6688/qcSSMDhomo. The file qcSSMDhomo_1.0.0.tar.gz (for Windows) containing qcSSMDhomo is also available at Bioinformatics online. qcSSMDhomo is distributed under the GNU General Public License. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Breeze: an integrated quality control and data analysis application for high-throughput drug screening

2020 ◽  
Vol 36 (11) ◽  
pp. 3602-3604 ◽  
Author(s):  
Swapnil Potdar ◽  
Aleksandr Ianevski ◽  
John-Patrick Mpindi ◽  
Dmitrii Bychkov ◽  
Clément Fiere ◽  
...  
Keyword(s):  
Quality Control ◽  
Data Analysis ◽  
High Throughput ◽  
High Throughput Screening ◽  
Complete Solution ◽  
Supplementary Information ◽  
Source Analysis ◽  
Technical Documentation ◽  
Resistance Patterns ◽  
Analysis Application

Abstract Summary High-throughput screening (HTS) enables systematic testing of thousands of chemical compounds for potential use as investigational and therapeutic agents. HTS experiments are often conducted in multi-well plates that inherently bear technical and experimental sources of error. Thus, HTS data processing requires the use of robust quality control procedures before analysis and interpretation. Here, we have implemented an open-source analysis application, Breeze, an integrated quality control and data analysis application for HTS data. Furthermore, Breeze enables a reliable way to identify individual drug sensitivity and resistance patterns in cell lines or patient-derived samples for functional precision medicine applications. The Breeze application provides a complete solution for data quality assessment, dose–response curve fitting and quantification of the drug responses along with interactive visualization of the results. Availability and implementation The Breeze application with video tutorial and technical documentation is accessible at https://breeze.fimm.fi; the R source code is publicly available at https://github.com/potdarswapnil/Breeze under GNU General Public License v3.0. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

Investigation of a New Family of Chiral Ligands for Enantioselective Catalysis via Parallel Synthesis and High-Throughput Screening

1998 ◽  
Vol 63 (16) ◽  
pp. 5312-5313 ◽  
Author(s):  
Cesare Gennari ◽  
Simona Ceccarelli ◽  
Umberto Piarulli ◽  
Christian A. G. N. Montalbetti ◽  
Richard F. W. Jackson
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Parallel Synthesis ◽  
Chiral Ligands ◽  
Enantioselective Catalysis ◽  
New Family

scholarly journals CancerDiscover: A configurable pipeline for cancer prediction and biomarker identification using machine learning framework

2017 ◽  
Author(s):  
Akram Mohammed ◽  
Greyson Biegert ◽  
Jiri Adamec ◽  
Tomáš Helikar
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Open Source ◽  
High Throughput ◽  
High Throughput Screening ◽  
Learning Algorithms ◽  
Supplementary Information ◽  
Supplementary File ◽  
Biomarker Identification ◽  
Selection Algorithms

AbstractMotivationUse of various high-throughput screening techniques has resulted in an abundance of data, whose complete utility is limited by the tools available for processing and analysis. Machine learning holds great potential for deciphering these data in the context of cancer classification and biomarker identification. However, current machine learning tools require manual processing of raw data from various sequencing platforms, which is both tedious and time-consuming. The current classification tools lack flexibility in choosing the best feature selection algorithms from a range of algorithms and most importantly inability to compare various learning algorithms.ResultsWe developed CancerDiscover, an open-source software pipeline that allows users to efficiently and automatically integrate large high-throughput datasets, preprocess, normalize, and selects best performing features from multiple feature selection algorithms. The pipeline lets users apply various learning algorithms and generates multiple classification models and evaluation reports that distinguish cancer from normal samples, as well as different types and subtypes of cancer.Availability and ImplementationThe open source pipeline is freely available for download at https://github.com/HelikarLab/[email protected] InformationPlease refer to the CancerDiscover README (Supplementary File 1) for detailed instructions on installation and operation of the pipeline. For a list of available feature selection methods, see Supplementary File 2.

scholarly journals High-throughput phenotypic screen identifies a new family of potent anti-amoebic compounds

2021 ◽  
Author(s):  
Conall Sauvey ◽  
Gretchen Ehrenkaufer ◽  
Jonathan Blevitt ◽  
Paul Jackson ◽  
Ruben Abagyan
Keyword(s):  
Entamoeba Histolytica ◽  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
Life Stage ◽  
Current Standard ◽  
New Family ◽  
Entamoeba Invadens ◽  
Related Compounds

AbstractEntamoeba histolytica is a disease-causing parasitic amoeba which affects an estimated 50 million people worldwide, particularly in socioeconomically vulnerable populations experiencing water sanitation issues. Infection with E. histolytica is referred to as amoebiasis, and can cause symptoms such as colitis, dysentery, and even death in extreme cases. Drugs exist that are capable of killing this parasite, but they are hampered by downsides such as significant adverse effects at therapeutic concentrations, issues with patient compliance, the need for additional drugs to kill the transmissible cyst stage, and potential development of resistance. Past screens of small and medium sized chemical libraries have yielded anti-amoebic candidates, thus rendering high-throughput screening a promising direction for new drug discovery in this area. In this study, we screened a curated 80,000-compound library from Janssen pharmaceuticals against E. histolytica trophozoites in vitro, and from it identified a highly potent new inhibitor compound. Further experimentation confirmed the activity of this compound, as well as that of several structurally related compounds, originating from both the Janssen Jump-stARter library, and from chemical vendors, thus highlighting a new structure-activity relationship (SAR). In addition, we confirmed that the compound inhibited E. histolytica survival as rapidly as the current standard of care and inhibited transmissible cysts of the related model organism Entamoeba invadens. Together these results constitute the discovery of a novel class of chemicals with favorable in vitro pharmacological properties which may lead to an improved therapy against this parasite and in all of its life stages.Author summaryThe parasite Entamoeba histolytica represents a significant challenge in the field of global health. It currently infects and causes disease among millions of people worldwide, particularly those lacking access to clean water. Drugs exist to treat this disease, but nevertheless it persists as a problem, likely at least partly due to problems and downsides inherent to these drugs. Hence the search for new and better ones is needed. We report here our contribution to this search, consisting of testing a large, carefully-curated collection of tens of thousands of chemicals for their ability to kill E. histolytica. This large-scale test resulted in the identification of one of the compounds as potently anti-amoebic, capable of killing the parasite cells at extremely low concentrations. Further experimentation found several chemically-related compounds to also possess this property, and additionally found the first compound capable of killing the infective life stage of another Entamoeba parasite. These results have revealed an entire new family of chemicals with good potential for development as better drugs against this disease.

scholarly journals Critical Review of Processing and Classification Techniques for Images and Spectra in Microplastic Research

2020 ◽  
Vol 74 (9) ◽  
pp. 989-1010 ◽  
Author(s):  
Win Cowger ◽  
Andrew Gray ◽  
Silke H. Christiansen ◽  
Hannah DeFrond ◽  
Ashok D. Deshpande ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
High Throughput ◽  
High Throughput Screening ◽  
Supplementary Information ◽  
Gas Chromatography Mass Spectrometry ◽  
Spectral Processing ◽  
Double Blind ◽  
Pyrolysis Gas ◽  
Machine Learning Classification

Microplastic research is a rapidly developing field, with urgent needs for high throughput and automated analysis techniques. We conducted a review covering image analysis from optical microscopy, scanning electron microscopy, fluorescence microscopy, and spectral analysis from Fourier transform infrared (FT-IR) spectroscopy, Raman spectroscopy, pyrolysis gas–chromatography mass–spectrometry, and energy dispersive X-ray spectroscopy. These techniques were commonly used to collect, process, and interpret data from microplastic samples. This review outlined and critiques current approaches for analysis steps in image processing (color, thresholding, particle quantification), spectral processing (background and baseline subtraction, smoothing and noise reduction, data transformation), image classification (reference libraries, morphology, color, and fluorescence intensity), and spectral classification (reference libraries, matching procedures, and best practices for developing in-house reference tools). We highlighted opportunities to advance microplastic data analysis and interpretation by (i) quantifying colors, shapes, sizes, and surface topologies with image analysis software, (ii) identifying threshold values of particle characteristics in images that distinguish plastic particles from other particles, (iii) advancing spectral processing and classification routines, (iv) creating and sharing robust spectral libraries, (v) conducting double blind and negative controls, (vi) sharing raw data and analysis code, and (vii) leveraging readily available data to develop machine learning classification models. We identified analytical needs that we could fill and developed supplementary information for a reference library of plastic images and spectra, a tutorial for basic image analysis, and a code to download images from peer reviewed literature. Our major findings were that research on microplastics was progressing toward the use of multiple analytical methods and increasingly incorporating chemical classification. We suggest that new and repurposed methods need to be developed for high throughput screening using a diversity of approaches and highlight machine learning as one potential avenue toward this capability.

TISIDB: an integrated repository portal for tumor–immune system interactions

2019 ◽  
Vol 35 (20) ◽  
pp. 4200-4202 ◽  
Author(s):  
Beibei Ru ◽  
Ching Ngar Wong ◽  
Yin Tong ◽  
Jia Yi Zhong ◽  
Sophia Shek Wa Zhong ◽  
...  
Keyword(s):  
Immune System ◽  
High Throughput ◽  
High Throughput Screening ◽  
Supplementary Information ◽  
Literature Mining ◽  
Comprehensive Investigation ◽  
High Throughput Data ◽  
Cancer Types ◽  
User Friendly ◽  
High Throughput Data Analysis

Abstract Summary The interaction between tumor and immune system plays a crucial role in both cancer development and treatment response. To facilitate comprehensive investigation of tumor–immune interactions, we have designed a user-friendly web portal TISIDB, which integrated multiple types of data resources in oncoimmunology. First, we manually curated 4176 records from 2530 publications, which reported 988 genes related to anti-tumor immunity. Second, genes associated with the resistance or sensitivity of tumor cells to T cell-mediated killing and immunotherapy were identified by analyzing high-throughput screening and genomic profiling data. Third, associations between any gene and immune features, such as lymphocytes, immunomodulators and chemokines, were pre-calculated for 30 TCGA cancer types. In TISIDB, biologists can cross-check a gene of interest about its role in tumor–immune interactions through literature mining and high-throughput data analysis, and generate testable hypotheses and high quality figures for publication. Availability and implementation http://cis.hku.hk/TISIDB Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MultiPaths: a Python framework for analyzing multi-layer biological networks using diffusion algorithms

2020 ◽  
Author(s):  
Josep Marín-Llaó ◽  
Sarah Mubeen ◽  
Alexandre Perera-Lluna ◽  
Martin Hofmann-Apitius ◽  
Sergio Picart-Armada ◽  
...  
Keyword(s):  
High Throughput ◽  
Biological Networks ◽  
High Throughput Screening ◽  
Predictive Performance ◽  
Biological Data ◽  
Supplementary Information ◽  
Command Line Interface ◽  
Multiple Modalities ◽  
High Throughput Experiments ◽  
Pathway Databases

Abstract Summary High-throughput screening yields vast amounts of biological data which can be highly challenging to interpret. In response, knowledge-driven approaches emerged as possible solutions to analyze large datasets by leveraging prior knowledge of biomolecular interactions represented in the form of biological networks. Nonetheless, given their size and complexity, their manual investigation quickly becomes impractical. Thus, computational approaches, such as diffusion algorithms, are often employed to interpret and contextualize the results of high-throughput experiments. Here, we present MultiPaths, a framework consisting of two independent Python packages for network analysis. While the first package, DiffuPy, comprises numerous commonly used diffusion algorithms applicable to any generic network, the second, DiffuPath, enables the application of these algorithms on multi-layer biological networks. To facilitate its usability, the framework includes a command line interface, reproducible examples and documentation. To demonstrate the framework, we conducted several diffusion experiments on three independent multi-omics datasets over disparate networks generated from pathway databases, thus, highlighting the ability of multi-layer networks to integrate multiple modalities. Finally, the results of these experiments demonstrate how the generation of harmonized networks from disparate databases can improve predictive performance with respect to individual resources. Availability and implementation DiffuPy and DiffuPath are publicly available under the Apache License 2.0 at https://github.com/multipaths. Supplementary information Supplementary data are available at Bioinformatics online.

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