Scikit-learn

Varoquaux, Gaël; Buitinck, Lars; Louppe, Gilles; Grisel, Olivier; Pedregosa, Fabián; Mueller, Andreas

doi:10.1145/2786984.2786995

Cited by 583 publications

(283 citation statements)

References 15 publications

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“…To select statistically significant motifs, we first assessed the motifs by a random forest classifier using scikit-learn (Varoquaux et al 2015). The random forest algorithm uses bootstrap sampling and constructs a decision tree for each sub-sample.…”

Section: Resultsmentioning

confidence: 99%

Prediction pipeline for discovery of regulatory motifs associated with Brugia malayi molting

Grote

Liu

et al. 2019

Preprint

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25Filarial nematodes can cause debilitating diseases in humans. They have complicated 26 life cycles involving an insect vector and mammalian hosts, and they go through a 27 number of developmental molts. While whole genome sequences of parasitic worms are 28 now available, very little is known about transcription factor (TF) binding sites and their 29 cognate transcription factors that play a role in regulating development. To address this 30 gap, we developed a novel motif prediction pipeline, Emotif Alpha, that integrates ten 31 different motif discovery algorithms, multiple statistical tests, and a comparative analysis 32 of conserved elements between the filarial worms Brugia malayi and Onchocerca 33 volvulus, and the free-living nematode Caenorhabditis elegans. We identified stage-34 specific TF binding motifs in B. malayi, with a particular focus on those potentially 35 involved in the L3-L4 molt, a stage important for the establishment of infection in the 36 mammalian host. Using an in vitro molting system, we tested and validated three of 37

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Section: Resultsmentioning

confidence: 99%

Prediction pipeline for discovery of regulatory motifs associated with Brugia malayi molting

Grote

Liu

et al. 2019

Preprint

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“…On DS1 this required extensive manual proofreading. This segmentation served as ground truth to train a support vector classifier in scikit-learn [37] (v0.19.1) based on the features given in Table 2. This more automated segmentation pipeline was then applied to dataset DS2.…”

Section: Methodsmentioning

confidence: 99%

A semi-automated approach to dense segmentation of 3D white matter electron microscopy

Kleinnijenhuis

Johnson

Mollink

et al. 2020

Preprint

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PurposeNeuroscience methods working on widely different scales can complement and inform each other. At the macroscopic scale, magnetic resonance imaging methods that estimate microstructural measures have much to gain from ground truth validation and models based on accurate measurement of that microstructure. We present an approach to generate rich and accurate geometric models of white matter microstructure through dense segmentation of 3D electron microscopy (EM).MethodsVolumetric data of the white matter of the genu of the corpus callosum of the adult mouse brain were acquired using serial blockface scanning electron microscopy (SBF-SEM). A segmentation pipeline was developed to separate the 3D EM data into compartments and individual cellular and subcellular constituents, making use of established tools as well as newly developed algorithms to achieve accurate segmentation of various compartments.ResultsThe volume was segmented into six compartments comprising myelinated axons (axon, myelin sheath, nodes of Ranvier), oligodendrocytes, blood vessels, mitochondria, and unmyelinated axons. The myelinated axons had an average inner diameter of 0.56 μm and an average outer diameter of 0.87 μm. The diameter of unmyelinated axons was 0.43 μm. A mean g-ratio of 0.61 was found for myelinated axons, but the g-ratio was highly variable between as well as within axons.ConclusionThe approach for segmentation of 3D EM data yielded a dense annotation of a range of white matter compartments that can be interrogated for their properties and used for in silico experiments of brain structure. We provide the resulting dense annotation as a resource to the neuroscience community.

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“…Later the coordinates of the center of mass of docking poses were subjugated to Principal component [36] and cluster analysis. They were performed using the scikit-learn library [37] for python programming language. The first two principal components were used for cluster analysis, which was performed using the DBSCAN algorithm [38].…”

Section: Methodsmentioning

confidence: 99%

Mechanistic insights of the attenuation of quorum-sensing-dependent virulence factors of Pseudomonas aeruginosa: Molecular modeling of the interaction of taxifolin with transcriptional regulator LasR

Grabski

Tiratsuyan

2018

Preprint

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Pseudomonas aeruginosa is one of the most dangerous superbugs and is responsible for both acute and chronic infection. Current therapies are not effective because of biofilms that increase antibiotic resistance. Bacterial virulence and biofilm formation are regulated through a system called quorum sensing, which includes transcriptional regulators LasR and RhIR. These regulators are activated by their own natural autoinducers. Targeting this system is a promising strategy to combat bacterial pathogenicity. Flavonoids are very well known for their antimicrobial activity and taxifolin is one of them. It is also known that flavonoids inhibit Pseudomonas aeruginosa biofilm formation, but the mechanism of action is unknown. In the present study, we tried to analyse the mode of interactions of LasR with taxifolin. We used a combination of molecular docking, molecular dynamics simulations and machine learning techniques, which includes principal component and cluster analysis to study the interaction of the LasR protein with taxifolin. We show that taxifolin has two binding modes. One binding mode is the interaction with ligand binding domain. The second mode is the interaction with the "bridge", which is a cryptic binding site. It involves conserved amino acid interactions from multiple domains. Biochemical studies show hydroxyl group of ring A in flavonoids is necessary for inhibition. In our model the hydroxyl group ensures the formation of many hydrogen bonds during the second binding mode. Microsecond simulations also show the stability of the formed complex. This study may offer insights on how taxifolin inhibits LasR and the quorum sensing circuitry.

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Scikit-learn

Cited by 583 publications

References 15 publications

Prediction pipeline for discovery of regulatory motifs associated with Brugia malayi molting

Prediction pipeline for discovery of regulatory motifs associated with Brugia malayi molting

A semi-automated approach to dense segmentation of 3D white matter electron microscopy

Mechanistic insights of the attenuation of quorum-sensing-dependent virulence factors of Pseudomonas aeruginosa: Molecular modeling of the interaction of taxifolin with transcriptional regulator LasR

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