Akshay Akshay
SEEK ID: https://workflowhub.eu/people/560
Location: Not specified
ORCID: Not specified
Joined: 15th Sep 2023
Expertise: Not specified
Tools: Not specified
Roles
Team Administrator
Their tagsRelated items
A space managed by WorkflowHub administrators for teams that don't want/need to manage their own space.
Teams: IBISBA Workflows, NMR Workflow, UNLOCK, NanoGalaxy, Galaxy Climate, PNDB, IMBforge, COVID-19 PubSeq: Public SARS-CoV-2 Sequence Resource, LBI-RUD, Nick-test-team, usegalaxy-eu, Italy-Covid-data-Portal, UX trial team, Integrated and Urban Plant Pathology Laboratory, SARS-CoV-2 Data Hubs, lmjxteam2, virAnnot pipeline, Ay Lab, iPC: individualizedPaediatricCure, Harkany Lab, MOLGENIS, EJPRD WP13 case-studies workflows, Common Workflow Language (CWL) community, Testing, SeBiMER, IAA-CSIC, MAB - ATGC, Probabilistic graphical models, GenX, Snakemake-Workflows, ODA, IPK BIT, CO2MICS Lab, FAME, CHU Limoges - UF9481 Bioinformatique / CNR Herpesvirus, Quadram Institute Bioscience - Bioinformatics, HecatombDevelopment, Institute of Human Genetics, Testing RO Crates, Test Team, Applied Computational Biology at IEG/HMGU, INFRAFRONTIER workflows, OME, TransBioNet, OpenEBench, Bioinformatics and Biostatistics (BIO2 ) Core, VIB Bioinformatics Core, CRC Cohort, ICAN, MustafaVoh, Single Cell Unit, CO-Graph, emo-bon, TestEMBL-EBIOntology, CINECA, Toxicology community, Pitagora-Network, Workflows Australia, Medizinisches Proteom-Center, Medical Bioinformatics, AGRF BIO, EU-Openscreen, X-omics, ELIXIR Belgium, URGI, Size Inc, GA-VirReport Team, The Boucher Lab, Air Quality Prediction, pyiron, CAPSID, Edinburgh Genomics, Defragmentation TS, NBIS, Phytoplankton Analysis, Seq4AMR, Workflow registry test, Read2Map, SKM3, ParslRNA-Seq: an efficient and scalable RNAseq analysis workflow for studies of differentiated gene expression, de.NBI Cloud, Meta-NanoSim, ILVO Plant Health, EMERGEN-BIOINFO, KircherLab, Apis-wings, BCCM_ULC, Dessimoz Lab, TRON gGmbH, GEMS at MLZ, Computational Science at HZDR, Big data in biomedicine, TRE-FX, MISTIC, Guigó lab, Statistical genetics, Delineating Regions-of-interest for Mass Spectrometry Imaging by Multimodally Corroborated Spatial Segmentation, OLCF-WES, Bioinformatics Unit @ CRG, Bioinformatics Innovation Lab, BSC-CES, ELIXIR Proteomics, Black Ochre Data Labs, Zavolan Lab, Metabolomics-Reproducibility, Team Cardio, NGFF Tools, Bioinformatics workflows for life science, Workflows for geographic science, Pacific-deep-sea-sponges-microbiome, CSFG, SNAKE, Katdetectr, INFRAFRONTIER GmbH, PerMedCoE, Euro-BioImaging, EOSC-Life WP3 OC Team, cross RI project, ANSES-Ploufragan, SANBI Pathogen Bioinformatics, Biodata Analysis Group, DeSci Labs, Erasmus MC - Viroscience Bioinformatics, ARA-dev, Mendel Centre for Plant Genomics and Proteomics, Metagenomic tools, WorkflowEng, Polygenic Score Catalog, bpm, scNTImpute, Systems Biotechnology laboratory, Cimorgh IT solutions, MLme: Machine Learning Made Easy, Hurwitz Lab, Dioscuri TDA, Scipion CNB, System Biotechnology laboratory, yPublish - Bioinfo tools, NIH CFDE Playbook Workflow Partnership, MMV-Lab, EMBL-CBA, EBP-Nor, Evaluation of Swin Transformer and knowledge transfer for denoising of super-resolution structured illumination microscopy data, Bioinformatics Laboratory for Genomics and Biodiversity (LBGB), multi-analysis dFC, CholGen, RNA group, Plant Genomes Pipelines in Galaxy, Pathogen Genomic Laboratory, Chemical Data Lab, JiangLab, Pangenome database project, HP2NET - Framework for construction of phylogenetic networks on High Performance Computing (HPC) environment, Center for Open Bioimage Analysis, Generalized Open-Source Workflows for Atomistic Molecular Dynamics Simulations of Viral Helicases, Historical DNA genome skimming, QCDIS, Peter Menzel's Team, NHM Clark group, ESRF Workflow System (Ewoks), Kalbe Bioinformatics, Nextflow4Metabolomics, GBCS, CEMCOF, Jackson Laboratory NGS-Ops, Schwartz Lab, BRAIN - Biomedical Research on Adult Intracranial Neoplasms, Cancer Therapeutics and Drug Safety, Deepdefense, Mid-Ohio Regional Planning Commission, MGSSB, Institute for Human Genetics and Genomic Medicine Aachen, FengTaoSMU, EGA, Plant-Food-Research-Open, KrauthammerLab, Geo Workflows, grassland pDT, FunGIALab, CRIM - Computer Research Institute of Montréal, Medvedeva Lab, Metagenlab, FAIR-EASE, Protein-protein and protein-nucleic acid binding site prediction research, Culhane Lab, IDUN - Drug Delivery and Sensing, Edge Computing DAG Task Scheduling Research Group, Stratum corneum nanotexture feature detection using deep learning and spatial analysis: a non-invasive tool for skin barrier assessment, COPO, Taudière group, ErasmusMC Clinical Bioinformatics, interTwin, fluid flow modeling, EnrichDO, WorkflowResearch, Application Security - Test Crypt4GH solutions, RenLabBioinformatics, Yongxin's team, PiFlow, HLee_SeoGroup, UFZ - Image Data Management and Processing Workflows, Korean Bioinformaticians, Into the deep, XChem, CPM, SocialGene, Research Data Management ICE-2, ObjectRecognition, LiDAR, FONDA II C2, Astroparticle Lab, FAIRagro M4.4, Kgerring, QuackenbushLab, Virus sequencing team, SOS, BioImage Informatics and Analysis Workflows, BoostNano, simblockflow, CSSB, Research on Workflow scheduling, Research Data1, CSUbioinformatics, CDPP, Mr., ASD-HRS, data management, FAIR_thesis: Marine acoustic data, CellBinDB, DEEP Lab, University of Amsterdam, SIMEXP, nf-pediatric Team, Kasmanas, Structural Variation Analysis, fuzzyworkflow, CausalCoxMGM Team, Tufts University Center for Cellular Agriculture (TUCCA), Test, CrustyBase, Applied Computational Cancer Research
Web page: Not specified
Machine Learning Made Easy (MLme) is a novel tool that simplifies machine learning (ML) for researchers. By integrating four essential functionalities, namely data exploration, AutoML, CustomML, and visualization, MLme fulfills the diverse requirements of researchers while eliminating the need for extensive coding efforts. MLme serves as a valuable resource that empowers researchers of all technical levels to leverage ML for insightful data analysis and enhance research outcomes. By simplifying ...
Space: Independent Teams
Public web page: https://github.com/FunctionalUrology/MLme
Country: Not specified
City: Not specified
Web page: Not specified
This workflow represents the Default ML Pipeline for AutoML feature from MLme. Machine Learning Made Easy (MLme) is a novel tool that simplifies machine learning (ML) for researchers. By integrating four essential functionalities, namely data exploration, AutoML, CustomML, and visualization, MLme fulfills the diverse requirements of researchers while eliminating the need for extensive coding efforts. MLme serves as a valuable resource that empowers researchers of all technical levels to leverage ...
