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MACC combination

MACC combination: A powerful optimization technique that leverages the strengths of multiple algorithms to enhance research accuracy and reproducibillity.
By combining various Algorithms, MACC optimiation can identify the best protocols and products from the vast literature, pre-prints, and patent databases, guiding researchers towards more effective research outcomes.
This AI-driven approach streamlines the discovery process, empowering scientists to make informed decisions and advance their studies with confidence.

Most cited protocols related to «MACC combination»

All the newly added prediction models on the ProTox-II platform are based on machine learning algorithms. A Random Forest (RF) algorithm (26 ) is used to construct the classification and prediction models for hepatotoxicity, cytotoxicity, mutagenicity, and carcinogenicity. The RF-based models are constructed using 500 decision trees and GINI index criterion. The advantage of using RF-based classifier is that it tends to avoid overfitting.
For the construction of theTox21 based toxicological pathway prediction, an ensemble approach is used including RF and Support Vector Machine (SVM) classifiers. The radial basis function (RBF) is used as kernel function for the SVM algorithm. Immunotoxicity prediction model is based on Bernoulli–Naive Bayes algorithm, as explained in the published work (24 (link)).
Here, two different fingerprints are used: MACCS molecular fingerprints-166 bits and Morgan circular fingerprints-2048 bits (http:/www.rdkit.org/). These two fingerprints have shown an optimal performance for prediction of chemical activity (11 ,24 (link)).
Additionally, a selective oversampling of minority class is introduced in the construction of the models. For each of the prediction end-points, the active (positive) and inactive (negative) data are fragmented using RECAP (27 (link)) and ROTBONDS (28 (link)) fragmentation methods. The propensity score (PS) (12 ) for each of the uniquely occurring fragments in both the sets is computed. Only those molecules having the highest propensity scores for fragments conserved for the active class are oversampled and added into model construction. The same ratio of active and inactive compounds was maintained for all the folds of cross-validation, using the fragment-based similarities between the compounds.
The prediction models are based on python programming language. Machine learning packages like scikit-learn (http:/scikit-learn.org) and cheminformatics package RDKit (http:/www.rdkit.org/) are used for the model implementation. All data are standardized using KNIME (29 ). A template script (Sample API script http://tox.charite.de/protox_II/simple_api.py) has been provided under the description ‘using the API’ on the FAQ section of the ProTox-II webserver.
Publication 2018
Carcinogens Chemical Actions Cytotoxin MACC combination Minority Groups Mutagens Oxidase, Protoporphyrinogen Python
Four classes of 2D fingerprint types can be distinguished: (i) dictionary-based, (ii) topological or path-based, (iii) circular fingerprints and (iv) pharmacophores. In addition, fingerprints can differ in the atom types or feature classes used or the length of the bit string. In this study, 14 fingerprints belonging to three of the four classes were compared.
The public Molecular ACCess System (MACCS) structural keys
[43 ] are 166 predefined substructures defined as SMARTS and belong to the dictionary-based fingerprint class. They were originally designed for substructure search and typically show a low performance level for virtual screening, thus they are often used as baseline fingerprint for benchmarking studies.
Topological or path-based fingerprints describe combinations of atom types and paths between atom types. In atom pair (AP) fingerprints
[44 (link)], pairs of atoms together with the number of bonds separating them are encoded. In topological torsions (TT)
[37 (link)], on the other hand, four atoms forming a torsion are described. In both AP and TT fingerprints the atom type consists of the element, the number of heavy-atom neighbours and the number of π-electrons.
The RDKit fingerprint, a relative of the well-known Daylight fingerprint
[45 ], is another topological descriptor. Atom-types, the atomic number and aromaticity state, are combined with bond types to hash all branched and linear molecular subgraphs up to a particular size
[42 ]. In this study, a maximum path length of five (RDK5) was used.
Similar to the Daylight fingerprints, certain paths and feature classes of the molecular graph are enumerated and hashed in the Avalon fingerprint
[46 (link)]. There are 16 feature classes which were optimized for substructure search. A detailed description of the feature classes is given in Table
1 and the supplementary material of
[46 (link)].
Circular fingerprints were developed more recently
[47 (link)], and encode circular atom environments up to a certain bond radius from the central atom. If atom types consisting of the element, the number of heavy-atom neighbours, the number of hydrogens, the isotope and ring information are used these fingerprints are called extended-connectivity (EC) fingerprints. Alternatively, pharmacophoric features can be used, yielding functional connectivity (FC) fingerprints. We consider two representations of the fingerprints, bit strings (FP) and count vectors (FC). This gives four types of circular fingerprints: extended-connectivity bit string (ECFP), extended-connectivity count vector (ECFC), feature-connectivity bit string (FCFP) and feature-connectivity count vector (FCFC). The maximum bond length or diameter is added at the end to the name. In this study, the four types of circular fingerprints with a diameter 4, i.e. ECFP4, ECFC4, FCFP4 and FCFC4, as well as ECFP6 were compared. In addition, ECFC0, which is a kind of atom count, was used as a second baseline fingerprint.
For all bit-string fingerprints, a size of 1024 bits was used. However, Sastry et al. found that such a small bit space may result in many collisions which can affect VS performance
[21 (link)]. To investigate this effect a larger bit space, 16384 bits, was used for three fingerprints: long ECFP4 (lECFP4), long ECFP6 (lECFP6) and long Avalon (lAvalon).
All fingerprints were calculated using the RDKit.
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Publication 2013
Cloning Vectors Electrons Hydrogen Isotopes MACC combination Radius Scents
The molecular fingerprints used were taken from the benchmarking platform described by Riniker and Landrum [9 (link)] and are listed in Table 1. Although their study focused on results for 14 fingerprints, the associated code [24 ] includes a further 14, mainly additional variants of circular fingerprints but also hashed forms of atom pairs (HashAP) and topological torsions (HashTT). In this study we have used the full set of 28 fingerprints as implemented in the RDKit version 2015.09.2 [25 ].
The fingerprints may be classified as follows. Additional details are in the publication by Riniker and Landrum:

Path-based fingerprints RDKx where x is 5, 6, 7 (hashed branched and linear subgraphs up to size x), TT (topological torsion [26 (link)], a count vector) and a binary vector form HashTT, AP [27 (link)] (atom pair, a count vector) and a binary vector form HashAP.

Substructure keys Avalon [28 (link)], MACCS.

Circular fingerprints The extended-connectivity fingerprints [29 (link)] ECFPx where x is 0, 2, 4, 6, and the corresponding count vectors denoted as ECFCx. Also the feature-class fingerprints FCFPx and corresponding count vectors FCFCx where x is 2, 4, 6.

A length of 1024 bits was used for all binary fingerprints listed above, but for comparison a longer length of 16384 bits was used for a number of fingerprints (as in the original study). This longer version is indicated by the prefix “L”: LAvalon, LECFP6, LECFP4, LFCFP6 and LFCFP4. The Tanimoto coefficient was used to measure similarity for all binary fingerprints, while the Dice coefficient was used for count vectors.
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Publication 2016
Cloning Vectors MACC combination
To facilitate comparison with MACC profiles, the signals for all external markers were re-computed for the same 300-bp bins as those used for MACC computations. The profiles around specified sets of sites were computed by using linear interpolation of MACC values associated with 300-bp bins and the resulting average profiles were additionally smoothed in the 40-bp running window. Two-state HMM and Viterbi algorithm were used to map chromatin accessibility states based on MACC profiles. The computations were performed using R-package RHmm (https://r-forge.r-project.org/projects/rhmm/). For generation of HMM based on randomized data, shuffling of MACC profile within each chromosome was used. Hierarchical clustering was performed using unweighted pair group method with the distance between profiles computed as (1—Pearson's correlation coefficients). To ensure that our findings are not biased by variability in the fragment size distributions, we reproduced crucial observations with MNase and MACC profiles computed only with fragments of mono-, di- and tri-nucleosomal lengths (Supplementary Figs 21–25).
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Publication 2016
Chromatin Chromosomes Figs MACC combination Nucleosomes

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Publication 2014
1-(trimethylsilyl)-1H-imidazole Action Potentials Hemoglobin A Ligands MACC combination Pharmaceutical Preparations Zinc

Most recents protocols related to «MACC combination»

MACC scores were computed as described previously (37 (link)). Briefly, read frequencies were computed in non-overlapping bins of selected size (300 bp) for each titration point independently, normalized to library sizes, and fit with a linear regression. The estimated regression coefficients were corrected to remove dependence on GC content. The corrected values were used as MACC scores.
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Publication 2023
DNA Library MACC combination Titrimetry
The similarity degree of the ligands reported in the curated ChEMBL and DrugBank datasets were evaluated as follows. First, an all-against-all 2D-fingerprint-based similarity assessment was performed by using an in-house-developed Python script. The degree of 2D similarity was calculated according to the MACCS and circular (i.e., ECFP4) type of fingerprints available on OpenEye Python toolkits [194 ], and by using the Tanimoto coefficient (Tc) as an index for ligands similarity measurement. Then, the results of the 2D similarity calculations were filtered to retain only those with MACCS and ECFP4 Tanimoto coefficients equal to or higher than 0.8 and 0.3, respectively, in agreement with previously reported studies [196 (link)]. Afterward, extensive 3D shape and atom type similarity calculations of compounds resulting as significantly similar in the 2D estimations were performed by using the ROCS software [117 ]. In this case, the similarity degree between ligands was assessed according to the Tanimoto Combo (TCc) coefficient [117 ]. Finally, the results of the 3D-shape-based similarity calculations were filtered to retain only similarity records with a TCc ≥ 1.5, which is a commonly accepted threshold of similarity [196 (link)].
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Publication 2023
Ligands MACC combination Python
The benthic diatom N. shiloi was previously identified, and the details are given by Demirel, et al. [74 (link)]. In that study, benthic diatoms were separated from the Aegean Sea in Türkiye and identified according to their morphological characteristics on the basis of observations in bright field and scanning electron microscopy. For DNA extraction, the cultured strain was harvested by centrifugation, and the cell pellet was used in the DNA Kits (Zymo Research) and stored at −20 °C. PCR primers targeted to link 18S rDNA (F, 5′-YACCTGGTTGATCCTGCCAGTAG-3′ and R, 5′-GCTTGATCCTTCTGCAGGTTCACC-3′). PCR protocol was applied, and products were viewed in an agarose gel stained by Jel Safe Dye and checked the gel under a UV light. In the DNA sequencing step, dye-terminator sequencing was performed using the primers, and the nucleotide chromatograms were determined by DNA sequences. The sequences obtained in the mentioned study were deposited in the NCBI GenBank, and the accession numbers of KR149459. Nanofrustulum shiloi (EGEMACC 47) was deposited in Ege University, Microalgae Culture Collection, Izmir, Turkey (EGEMACC—https://ege-macc.ege.edu.tr/ (accessed on 15 February 2023)).
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Publication 2023
Cells Centrifugation Diatoms DNA, Ribosomal MACC combination Microalgae Nucleotides Oligonucleotide Primers Scanning Electron Microscopy Sepharose Strains Ultraviolet Rays

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Publication 2023
167-A Cloning Vectors Drug Combinations MACC combination Scents
In this study, the SAS map and SALI value are used to visualize
the structure–activity landscape and identify activity cliffs
(AC)s. In this study, Activity Landscape Plotter V.1, a webserver
to generate SAS maps, is used.17 (link) The threshold
of structure and activity similarity are set to 0.9 and 2, respectively,
which indicates that the activity cliff quadrant is defined as X > 0.9 and Y > 2. And the molecular
fingerprints
used for generating the SAS map consist of ECFP4, PubChem, and MACCS.
Each set of fingerprints can generate the corresponding SAS maps and
ACs, and the overlapping ACs are defined as consensus ACs.
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Publication 2023
MACC combination Microtubule-Associated Proteins

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More about "MACC combination"

MACC (Multi-Algorithm Combination) is a powerful optimization technique that leverages the strengths of multiple algorithms to enhance research accuracy and reproducibility.
By combining various algorithms, MACC optimization can identify the best protocols and products from the vast literature, pre-prints, and patent databases, guiding researchers towards more effective research outcomes.
This AI-driven approach streamlines the discovery process, empowering scientists to make informed decisions and advance their studies with confidence.
The MACC combination technique can be particularly useful when working with a variety of laboratory equipment and tools, such as Microplate readers, ROCS 3.2.0.4 software, Hairpin-it TM miRNAs qPCR kits, PrimeScript RT Master Mix Perfect Real Time Kits, Autolab PGSTAT302N potentiostats, Multi-Cultivator MC 1000-OD systems, and Dual-Glo Luciferase Assay Systems.
These tools can generate vast amounts of data, and MACC can help researchers navigate this complexity, identify the most effective protocols and products, and streamline the discovery process.
MACC optimization can also be leveraged in conjunction with various chemical compounds and reagents, such as Potassium persulfate and SYBR Green Master Mix systems, to enhance the accuracy and reproducibility of experiments involving 2-hydroxyethyl methacrylate and other materials.
By using MACC combination, researchers can improve their overall research outcomes, making more informed decisions and advancing their studies with greater confidence and efficiency.