We provide end-to-end machine learning services tailored to research and development needs, encompassing the full spectrum of techniques, from supervised, unsupervised, and semi-supervised learning algorithms to advanced reinforcement learning (RL) strategies. Our approach integrates robust statistical methodologies to ensure analytical rigor and maximize the impact of data-driven innovation. Our services include:
Healthcare and Cost Studies
- Non-Normal Data Analysis
- Statistical methods to assess costs related to specific health conditions, treatments, and interventions
Design of Experiments statistical input and recommendations
- Define Your Variables and Hypotheses
- Manipulate Independent Variables
- Randomization
- Dependent Variable Measurement
- Sample Size Calculation
- Full Factorial Design/ Fractional Factorial Design/ Central Composite Design
- Statistical Tests/ Effect Size and Significance
Stability testing
- Gather stability data from physical, chemical, biological, and microbiological tests
- Present the data appropriately, considering the dosage form attributes
- Assess whether there’s a significant association between the response (e.g., product attribute) and relevant terms (e.g., time, storage conditions).
- Determine the shelf life of the product based on stability data
- Model Assessment
- Extrapolate stability data beyond the observed time points and estimate long-term behavior
Dissolution testing
- Profile Comparison
- Similarity Factor (f2)
- ANOVA
- Regression Models
Bayesian Analysis
- Setting up prior belief, based on historical data, expert opinions, or similar analysis
- Data Collection
- Bayesian model
- Posterior Calculation
- Decision Making
Machine learning Algorithms
- Supervised Learning Algorithms
Unsupervised Learning Algorithms
Semi-Supervised Learning Algorithms
Reinforcement Learning (RL)
Regression & Classification Problems
