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I will deliver custom machine learning solutions for biology research data problems
Acerca de este Servicio
I develop custom machine learning pipelines for biological research: scientifically
rigorous, fully documented, and built to survive peer review:
- Image based analysis automated quantification, classification, and morphometric profiling from fluorescence, brightfield, confocal and other images.
- Molecular & omics data predictive models, biomarker discovery, and dimensionality reduction on genomic, proteomic, or epigenetic datasets.
- Reproducible pipelines every project includes a commented script, a README, and a demo notebook (Google Colab-ready) so you or your lab can re-run it
- Research-grade output publication-quality figures, statistical validation, and methodology sections you can use in your manuscript
Stack
Python · scikit-learn · PyTorch · OpenCV · scikit-image · pandas · matplotlib · ImageJ
Lenguaje de programación:
Python
•
Java
Marcos:
Scikit-learn
•
keras
•
PyTorch
•
Panda
Herramientas:
Jupyter Notebook
•
opencv
•
TensorFlow
•
Excel
•
Otros
Mi porfolio
FAQ
What types of biological data can you work with?
Fluorescence microscopy images (multichannel, z-stack), DNA methylation arrays (Illumina 450K/EPIC), flow cytometry data, and general tabular omics datasets. If you're unsure whether your data fits, send me a message before ordering.
Do I need to share raw data or just processed files?
Either works. Raw images or .idat/.csv files are ideal, but preprocessed matrices are fine too. I'll sign an NDA if your data is sensitive or unpublished.
Will I be able to re-run the pipeline myself?
Yes, all deliverables include a commented script and a README with step-by-step instructions. Standard and Premium orders include a Colab notebook you can run without any local installation.
Can I use the results in a publication?
Absolutely. The Standard and Premium packages include methodology documentation written to journal standards. I ask only to be acknowledged in the Acknowledgements section (not authorship).
My dataset is small... is ML still applicable?
Often yes. For small n (< 100 samples), I use regularized models (ElasticNet, Ridge, SVM) and cross-validation strategies appropriate for your sample size, I'll tell you upfront if ML is genuinely not the right tool.
What if my project is more complex than the packages?
Send me a message. I accept custom orders and can scope a milestone-based project for multi-task pipelines.
