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Akademický článek

In silico cancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome

Autor: Jeroen H. A. Creemers, Ankur Ankan, Kit C. B. Roes, Gijs Schröder, Niven Mehra, Carl G. Figdor, I. Jolanda M. de Vries, Johannes Textor

Publikováno v: Nature Communications, Vol 14, Iss 1, Pp 1-14 (2023)

Abstract Late-stage cancer immunotherapy trials often lead to unusual survival curve shapes, like delayed curve separation or a plateauing curve in the treatment arm. It is critical for trial success to anticipate such effects in advance and adjust t

Externí odkaz: https://doaj.org/article/e3acd50fd4b54c859df232a022fd9f30

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Elektronická kniha

Hands-On Markov Models with Python : Implement Probabilistic Models for Learning Complex Data Sequences Using the Python Ecosystem

Autor: Ankur Ankan, Abinash Panda

Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearnKey FeaturesBuild a variety of Hidden Markov Models (HMM)Create and apply models to any sequence of data to analyze, predict, and extract

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In silicocancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome

Autor: Jeroen Creemers, Ankur Ankan, Kit C.B. Roes, Gijs Schröder, Niven Mehra, Carl G. Figdor, I. Jolanda M. de Vries, Johannes Textor

Background Late-stage cancer immunotherapy trials strive to demonstrate the clinical efficacy of novel immunotherapies, which is leading to exceptional responses and long-term survival in subsets of patients. To establish the clinical efficacy of an

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c70d4ab3b7209693cb91b4b8077b5d04
https://doi.org/10.1101/2021.09.09.21263319

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Testing Graphical Causal Models Using the R Package 'dagitty'

Autor: Johannes Textor, Inge M. N. Wortel, Ankur Ankan

Publikováno v: Current Protocols, 1, 2
Current Protocols, 1

Contains fulltext : 244706.pdf (Publisher’s version ) (Open Access) Causal diagrams such as directed acyclic graphs (DAGs) are used in several scientific fields to help design and analyze studies that aim to infer causal effects from observational

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f237d44cfff04e1b4c8bcff45d07756

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Elektronická kniha

Mastering Probabilistic Graphical Models Using Python : Master Probabilistic Graphical Models by Learning Through Real-world Problems and Illustrative Code Examples in Python

Autor: Ankur Ankan

Key FeaturesBook DescriptionWhat you will learnGet to know the basics of probability theory and graph theoryWork with Markov networksImplement Bayesian networksExact inference techniques in graphical models such as the variable elimination algorithmU

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pgmpy: Probabilistic Graphical Models using Python

Autor: Ankur Ankan, Abinash Panda

Publikováno v: Proceedings of the 14th Python in Science Conference.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::84558343c764f8b320aae19cd64a86e1
https://doi.org/10.25080/majora-7b98e3ed-001

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