On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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7 results for “biopharmaceutical”

Valuing Post-Revenue Biopharmaceutical Assets with Pfizer's Current Portfolio as a Case Studyq-fin.PR

This study improves valuation of post-revenue biopharmaceutical assets using Pfizer's data.

problem Accurate valuation of post-revenue drug assets in biotech and pharma.

method Historical sales data analysis to forecast future sales and calculate Net Present Value.

result Demonstrates a method for more informed investment decisions in biotech and pharma.

biopharmaceutical valuation post-revenue assets Pfizer case study Net Present Value strategic investment

2023-12-04abs ↗pdf ↗

Designing Robust Biotechnological Processes Regarding Variabilities using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Designcs.LG

Optimized biopharmaceutical seed train design reduces variability and saves time.

problem Designing robust biotechnological processes with cell cultures.

method Coupling uncertainty-based upstream simulation and Bayes optimization using Gaussian processes.

result Optimized seed train design results in lower cell density variability and reduced process duration.

biopharmaceutical seed train multi-objective optimization Bayes optimization cell cultures

2022-05-06abs ↗pdf ↗

RAPTOR-GEN: RApid PosTeriOR GENerator for Bayesian Learning in Biomanufacturingstat.ML

RAPTOR-GEN accelerates digital twin development for biopharmaceuticals.

problem Lack of agility in rapid, on-demand production of biotherapeutics due to bioprocess complexity.

method Bayesian learning framework based on a multi-scale probabilistic knowledge graph (pKG) and stochastic differential equations (SDE).

result RAPTOR-GEN accelerates intelligent digital twin development from sparse and heterogeneous experimental data.

Bayesian learning digital twin biopharmaceuticals biomanufacturing sparse data

2025-09-25abs ↗pdf ↗

Biomanufacturing Harvest Optimization with Small Datastat.ML

Optimizes harvesting in biopharmaceutical fermentation with limited data.

problem High variability in fermentation outputs due to model risk with small data.

method Stochastic model, Bayesian approach, Markov decision process.

result Improves fermentation output and reduces variability.

fermentation biopharmaceutical model risk stochastic modeling Markov decision process

2021-01-11abs ↗pdf ↗

Orthogonalization of data via Gromov-Wasserstein type feedback for clustering and visualizationstat.ML

Adaptive orthogonalization of data for clustering and visualization.

problem Clustering and visualization of data with high specificity.

method Adaptive orthogonalization process using Gromov-Wasserstein feedback.

result Method refines orthogonality of data to achieve high specificity clustering.

data clustering orthogonalization Gromov-Wasserstein diffusion map biopharmaceutical manufacturing

2022-07-25abs ↗pdf ↗

Green Simulation Assisted Reinforcement Learning with Model Risk for Biomanufacturing Learning and Controlcs.LG

A green simulation-assisted reinforcement learning method for biomanufacturing.

problem Complexity, high variability, lead time, and limited historical data in biopharmaceutical manufacturing.

method Quantifies model risk, uses posterior distribution, and selectively reuses simulation data.

result Demonstrates promising performance in online learning and decision making.

reinforcement learning biomanufacturing model risk green simulation process learning

2020-06-17abs ↗pdf ↗

Sampling To Improve Predictions For Underrepresented Observations In Imbalanced Datastat.ML

The paper tackles imbalance in production data by proposing sampling methods to improve model performance on underrepresented observations.

problem Imbalance in production data negatively impacts model predictive performance on underrepresented observations.

method Three sampling approaches are investigated to adjust for imbalance in training data and improve model performance.

result Fitting a model using sampled data yields a small reduction in overall predictive performance but a better performance on underrepresented observations.

data imbalance production data model performance sampling underrepresented observations

2021-11-17abs ↗pdf ↗