Expertise: Bioinformatics, Genomics, Machine Learning
Tools: Python, R, Machine Learning
I am a Ph.D. student in Gong lab. I am interested in cancer genomics, including the mining of genetic risk determinants in cancer, functional prediction of genetic variants, tumor-associated molecular epidemiology, large-scale data integration, analysis, and mining, as well as the construction of bioinformatical data platforms.
Teams: EU-Openscreen
Organizations: Fraunhofer Institute for Translational Medicine and Pharmacology ITMP

Expertise: Bioinformatics, Cheminformatics, Machine Learning
Tools: Workflows
Teams: Applied Computational Biology at IEG/HMGU
Organizations: Helmholtz Zentrum München

Expertise: Software Engineering, Machine Learning, AI
Tools: Java, Jupyter notebook, Web services, Python
Expertise: Bioinformatics, Computer Science, Data Management, Genetics, Genomics, Machine Learning, Metagenomics, NGS, Scientific workflow developement, Software Engineering
Tools: Databases, Galaxy, Genomics, Jupyter notebook, Machine Learning, Nextflow, nf-core, PCR, Perl, Python, R, rtPCR, Snakemake, Transcriptomics, Virology, Web, Web services, Workflows
Dad, husband and PhD. Scientist, technologist and engineer. Bibliophile. Philomath. Passionate about science, medicine, research, computing and all things geeky!
Teams: Bioinformatics Innovation Lab
Organizations: Pondicherry University

Expertise: Bioinformatics, Systems Biology, Machine Learning
Tools: Galaxy, Cytoscape, Databases, Jupyter notebook, R, Python
Ph.D. Student at Department of Bioinformatics, Pondicherry University
Teams: MAB - ATGC
Organizations: Centre National de la Recherche Scientifique (CNRS)

Expertise: Bioinformatics, Genomics, algorithm, Machine Learning, Metagenomics, NGS, Computer Science
Tools: Transcriptomics, Genomics, Python, C/C++, Web services, Workflows
Teams: GalaxyProject SARS-CoV-2, nf-core viralrecon, EOSC-Life - Demonstrator 7: Rare Diseases, iPC: individualizedPaediatricCure, EJPRD WP13 case-studies workflows, TransBioNet, OpenEBench, ELIXIR Proteomics
Organizations: Barcelona Supercomputing Center (BSC-CNS), ELIXIR

Expertise: Bioinformatics, Computer Science, AI, Machine Learning
Computer Engineer in Barcelona Supercomputing Center (BSC)
Teams: Harkany Lab
Organizations: Medical University of Vienna

Expertise: Systems Biology, Bioengineering, Bioinformatics, Neuroscience
Tools: Workflows, Machine Learning, Transcriptomics
Research Director @ INRAe
Abstract (Expand)
Authors: Anna-Lena Lamprecht, Magnus Palmblad, Jon Ison, Veit Schwämmle, Mohammad Sadnan Al Manir, Ilkay Altintas, Christopher J. O. Baker, Ammar Ben Hadj Amor, Salvador Capella-Gutierrez, Paulos Charonyktakis, Michael R. Crusoe, Yolanda Gil, Carole Goble, Timothy J. Griffin, Paul Groth, Hans Ienasescu, Pratik Jagtap, Matúš Kalaš, Vedran Kasalica, Alireza Khanteymoori, Tobias Kuhn, Hailiang Mei, Hervé Ménager, Steffen Möller, Robin A. Richardson, Vincent Robert, Stian Soiland-Reyes, Robert Stevens, Szoke Szaniszlo, Suzan Verberne, Aswin Verhoeven, Katherine Wolstencroft
Date Published: 2021
Publication Type: Journal
DOI: 10.12688/f1000research.54159.1
Citation: F1000Res 10:897
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 ...
IDR is based on OMERO and thus all what we show in this notebook can be easily adjusted for use against another OMERO server, e.g. your institutional OMERO server instance.
The main objective of this notebook is to demonstrate how public resources such as the IDR can be used to train your neural network or validate software tools.
The authors of the PLOS Biology paper, "Nessys: A new set of tools for the automated detection of nuclei within intact tissues and dense 3D cultures" published in August ...
Learning objectives
- Read data to analyse from an object store.
- Analyse data in parallel using Dask.
- Show how to use public resources to train neural network.
- Load labels associated to the original data
- Compare results with ground truth.
The authors of the PLOS Biology paper, "Nessys: A new set of tools for the automated detection of nuclei within intact tissues and dense 3D cultures" published in August 2019: https://doi.org/10.1371/journal.pbio.3000388, considered several image ...
Type: Unrecognized workflow type
Creators: Jean-Marie Burel, Petr Walczysko
Submitter: Jean-Marie Burel
The image is referenced in the paper "NesSys: a novel method for accurate nuclear segmentation in 3D" published August 2019 in PLOS Biology: https://doi.org/10.1371/journal.pbio.3000388 and can be viewed online in the Image Data Resource.
This original image was converted into the Zarr format. The analysis results produced by the authors of the paper were converted into labels and linked to the Zarr file which was placed into a public ...
Type: Unrecognized workflow type
Creators: Jean-Marie Burel, Petr Walczysko
Submitter: Jean-Marie Burel
ABR_Threshold_Detection
What is this?
This code can be used to automatically determine hearing thresholds from ABR hearing curves.
One of the following methods can be used for this purpose:
- neural network (NN) training,
- calibration of a self-supervised sound level regression (SLR) method
on given data sets with manually determined hearing thresholds.
Installation:
Run inside the src directory:
Installation as python package
pip install -e ./src (Installation as python
...