A Computational Framework for Scientifically Guided Machine Learning Workflow Recommendation in Experimental Biology
Biological Tabular Data Advisor is a biologically informed machine learning workflow recommendation system designed for experimental biological datasets.
Unlike generic AutoML systems, this framework evaluates:
- experimental structure
- repeated measurements
- longitudinal organization
- fluorescence workflows
- imaging-derived features
- dose-response experiments
- batch effects
- interpretability requirements
- biological reproducibility risks
before recommending computational workflows.
The goal is not simply to maximize predictive performance, but to provide scientifically defensible and biologically appropriate analysis guidance.
Modern biological datasets often violate standard machine learning assumptions.
Many experiments contain:
- repeated measurements
- time-series trajectories
- fluorescence drift
- longitudinal structure
- plate/well effects
- non-independent observations
- high-dimensional feature spaces
- small sample sizes
Generic AutoML platforms frequently ignore these biological realities, leading to:
- data leakage
- inflated model performance
- incorrect train/test splitting
- poor reproducibility
- biologically misleading conclusions
This project introduces a computational reasoning layer specifically designed for experimental biology workflows.
Automatically detects:
- wide vs long format datasets
- time-series structure
- repeated measurements
- fluorescence-related columns
- morphology features
- dose/concentration columns
- batch/plate metadata
- longitudinal experimental organization
Provides biologically informed warnings for:
- data leakage
- repeated measures
- temporal autocorrelation
- fluorescence artifacts
- photobleaching
- inappropriate train/test splitting
- batch effects
- small sample size risks
Suggests biologically appropriate methods including:
- Logistic Regression
- Random Forest
- Gradient Boosting / XGBoost
- Linear Mixed-Effects Models
- Functional Data Analysis
- Dynamic Time Warping
- Cross-correlation analysis
- Longitudinal trajectory analysis
Specialized support for:
- fluorescence trajectories
- calcium imaging
- repeated stimulation paradigms
- longitudinal cell-line experiments
- time-course biological assays
Recommendations include:
- baseline correction
- ΔF/F normalization
- ON/OFF phase analysis
- lag-response analysis
- grouped validation
- temporal preservation strategies
The framework is designed for:
- fluorescence microscopy datasets
- cell-line assays
- imaging-derived morphology features
- dose-response experiments
- longitudinal biological experiments
- electrophysiology measurements
- behavioral datasets
- experimental tabular biology data
Dataset Upload
↓
Biological Structure Detection
↓
Scientific Reasoning Engine
↓
Workflow Recommendation
↓
Validation Guidance
↓
Visualization Suggestions
The advisor can automatically identify risks such as:
- repeated measurements
- wide-format trajectory data
- temporal leakage
- inappropriate random splitting
- fluorescence preprocessing requirements
- batch effects
- missing-value structure
- class imbalance
- baseline correction
- ΔF/F normalization
- photobleaching inspection
- batch correction
- feature scaling
- biological outlier inspection
- grouped cross-validation
- time-aware splitting
- replicate-level validation
- mixed-effects modeling
- PCA
- correlation heatmaps
- fluorescence trajectories
- ON/OFF phase plots
- lag analysis plots
- morphology embeddings
- React
- Vite
- Tailwind CSS
- PapaParse
- JavaScript
- Lucide React
This project is not intended to replace scientists or statisticians.
Instead, it aims to function as:
- a computational biology assistant
- a workflow reasoning engine
- a methodological reviewer
- a biological ML guidance system
The emphasis is on:
- scientific rigor
- interpretability
- reproducibility
- biologically defensible workflows
rather than purely predictive optimization.
Planned future expansions include:
- microscopy-specific workflow reasoning
- omics-aware workflow modules
- neuroscience analysis modules
- reviewer-risk detection
- publication-readiness assessment
- literature-aware recommendation systems
- RAG-enhanced scientific guidance
- integration with LLM-based reasoning engines
Current status:
- MVP / research prototype
- active development
- conceptual framework + experimental implementation
https://biological-ml-advisor.netlify.app/
Taufia Hussain
Computational Biology • Experimental Biology • Scientific ML
CEO & Co-Founder — DataLens.Tools
MIT License