about
Unexplored diversity and strain-level structure of the skin microbiome associated with psoriasis.Maternal inheritance of bifidobacterial communities and bifidophages in infants through vertical transmissionStrain-level microbial epidemiology and population genomics from shotgun metagenomics.Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights.Microbial strain-level population structure and genetic diversity from metagenomes.Large-scale comparative metagenomics of Blastocystis, a common member of the human gut microbiome.Large-Scale Phylogenomics of the Lactobacillus casei Group Highlights Taxonomic Inconsistencies and Reveals Novel Clade-Associated Features.Draft Genome Sequence of the Planktic Cyanobacterium Tychonema bourrellyi, Isolated from Alpine Lentic Freshwater.Accessible, curated metagenomic data through ExperimentHub.Draft Genome Sequences of Novel Pseudomonas, Flavobacterium, and Sediminibacterium Species Strains from a Freshwater Ecosystem.Active learning methods for electrocardiographic signal classification.Correction for Pinto et al., "Draft Genome Sequences of Novel Pseudomonas, Flavobacterium, and Sediminibacterium Strains from a Freshwater Ecosystem".Profiling microbial strains in urban environments using metagenomic sequencing data.Erratum: MetaPhlAn2 for enhanced metagenomic taxonomic profilingMetaPhlAn2 for enhanced metagenomic taxonomic profilingLocal SVM approaches for fast and accurate classification of remote-sensing imagesCombining active and metric learning for hyperspectral image classificationActive-Metric Learning for Classification of Remotely Sensed Hyperspectral ImagesAn Active Learning Framework for Hyperspectral Image Classification Using Hierarchical SegmentationMultimetric Active Learning for Classification of Remote Sensing DataEnsemble Multiple Kernel Active Learning For Classification of Multisource Remote Sensing DataGenetic algorithm-based method for mitigating label noise issue in ECG signal classificationA Framework for Land Cover Classification Using Discrete Return LiDAR Data: Adopting Pseudo-Waveform and Hierarchical SegmentationIncorporating edge information into best merge region-growing segmentationLarge-Scale Image Classification Using Active LearningSVM Active Learning Approach for Image Classification Using Spatial InformationMultiobjective PSO for Hyperspectral Image ClusteringOptical Image Classification: A Ground-Truth Design FrameworkActive learning for spectroscopic data regressionActive Learning Methods for Biophysical Parameter EstimationAn approach for classifying large scale imagesSVR active learning for product quality controlDataset shift adaptation with active queriesGaussian process regression within an active learning schemeGround-truth assisted design for remote sensing image classificationImproving active learning methods using spatial informationSupport Vector Machine Active Learning Through Significance Space ConstructionUsing active learning to adapt remote sensing image classifiersGaussian Process Approach to Buried Object Size Estimation in GPR ImagesModel-based active learning for SVM classification of remote sensing images
P50
Q33826257-DCDB1A95-C653-4347-BB45-246B42D18E16Q33837496-AC79E135-635A-431F-8F42-6C05DA10946CQ35964692-3ED05D66-CE01-47DA-BB61-3591AE240DCCQ36072739-E3A1BF3A-D59F-425C-A51D-9F059EF339F9Q37735523-DD5C5C96-16DB-4B27-AC62-A226957E04BFQ38610350-29C8C14C-C456-43CB-B5FE-8265575BB6E6Q41486444-CDCE3588-91D8-421E-AFD4-CD7521B419FEQ47139714-60764F76-4B41-47F8-8C0B-A8780106544CQ47632693-BABA83B9-71F7-4987-96F4-6809805EFFB1Q49420426-D7639B72-6791-4F20-9B57-69E34F9F0AFCQ51639877-B8EA2471-6B3D-41C3-A08B-AD7DCF80C5C0Q52665822-B9E13D58-E67D-4385-A7F3-D3119F8B43C3Q55081977-F4841FA0-35DA-4EB6-9215-DFDEE38C49FCQ57255761-6EA869E4-6FC7-4558-AD80-F90089D06DDBQ57255766-CC466CEE-CE2B-426B-880A-F32190CDC473Q57255792-25498E3C-3A98-4A03-9E11-E9C33CC81D7AQ58066913-7B540C98-06CD-4555-B9CD-6824F7E94D17Q58066914-FCF96C42-E465-435A-94F8-1754AA8388E0Q58066916-0AB199BA-9328-4284-8A76-7E096161560CQ58066917-2424CEBD-FBE6-4345-8BD3-345C95D08BCEQ58066919-4FF591AA-54AC-490F-866B-78112C15A040Q58066920-842942B4-2DC6-4BCB-9B3C-6BEEEB89C6E3Q58066922-5AFA8AFA-1BD4-4C09-8B64-6F58B0D4262FQ58066923-26409A5C-1585-4309-B0A3-5CC84B166FA9Q58066925-A8B87B09-7109-4BEC-B526-B82C152DA1EAQ58066927-92AE82BD-E68A-4BF3-9449-75AD39E42F56Q58066929-4C625759-D6B0-49AC-B47E-E4615FE6E2F8Q58066931-69FF1056-8928-4D03-A923-D44DF720B317Q58066933-F6E18A4F-5B41-4BDE-B2B7-0049C28CABF0Q58066934-7A484024-E0CB-47B5-A64A-A07BAE520BE1Q58066935-F1140A70-389A-4F36-B07E-1E5937FCBAC6Q58066936-1501CAB9-4B48-4781-95CF-C8939CFF2883Q58066938-6597A581-D778-4652-8178-1EA160B23F96Q58066941-8F383750-050E-495C-9163-353A08CDCD3FQ58066942-8D78E4F3-53D7-4F80-B1FE-EC764891459CQ58066945-99409FF8-6176-4EAC-9CAC-A9BBE9F2045DQ58066947-70761FAC-A54E-447F-BAF2-0878F65A3819Q58066949-BA428FF7-0AAD-4CB7-9DE3-A9C90145E888Q58066950-B62ECD7F-3568-4CF6-B7D1-88A7F80AD4B0Q58066951-D01DDC8B-1A65-4A6D-B108-13DF4107B987
P50
description
hulumtues
@sq
onderzoeker
@nl
researcher
@en
հետազոտող
@hy
name
Edoardo Pasolli
@ast
Edoardo Pasolli
@en
Edoardo Pasolli
@es
Edoardo Pasolli
@nl
Edoardo Pasolli
@sl
type
label
Edoardo Pasolli
@ast
Edoardo Pasolli
@en
Edoardo Pasolli
@es
Edoardo Pasolli
@nl
Edoardo Pasolli
@sl
prefLabel
Edoardo Pasolli
@ast
Edoardo Pasolli
@en
Edoardo Pasolli
@es
Edoardo Pasolli
@nl
Edoardo Pasolli
@sl
P1053
K-5156-2016
P106
P1153
26423907800
P21
P2456
P31
P3829
P496
0000-0003-0799-3490