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alan-turing-institute

eider:Declarative Feature Extraction from Tabular Data Records

Extract features from tabular data in a declarative fashion, with a focus on processing medical records. Features are specified as JSON and are independently processed before being joined. Input data can be provided as CSV files or as data frames. This setup ensures that data is transformed in a modular and reproducible manner, and allows the same pipeline to be easily applied to new data.

Maintained by Camila Rangel Smith. Last updated 11 months ago.

hut23hut23-1515

3 stars 6.26 score 10 scripts

mohmedsoudy

DFD:Extract Drugs from Differential Expression Data

Extract Drugs from Differential Expression Data using the Connectivity Map (CMAP) Approach and Library of Integrated Network-Based Cellular Signatures (LINCS) Database.

Maintained by Mohamed Soudy. Last updated 1 years ago.

1 stars 2.70 score 1 scripts

mohmedsoudy

ScRNAIMM:Performing Single-Cell RNA-Seq Imputation by Using Mean/Median Imputation

Performing single-cell imputation in a way that preserves the biological variations in the data. The package clusters the input data to do imputation for each cluster, and do a distribution check using the Anderson-Darling normality test to impute dropouts using mean or median (Yazici, B., & Yolacan, S. (2007) <DOI:10.1080/10629360600678310>).

Maintained by Mohamed Soudy. Last updated 1 years ago.

2.70 score

kathbaum

DrDimont:Drug Response Prediction from Differential Multi-Omics Networks

While it has been well established that drugs affect and help patients differently, personalized drug response predictions remain challenging. Solutions based on single omics measurements have been proposed, and networks provide means to incorporate molecular interactions into reasoning. However, how to integrate the wealth of information contained in multiple omics layers still poses a complex problem. We present a novel network analysis pipeline, DrDimont, Drug response prediction from Differential analysis of multi-omics networks. It allows for comparative conclusions between two conditions and translates them into differential drug response predictions. DrDimont focuses on molecular interactions. It establishes condition-specific networks from correlation within an omics layer that are then reduced and combined into heterogeneous, multi-omics molecular networks. A novel semi-local, path-based integration step ensures integrative conclusions. Differential predictions are derived from comparing the condition-specific integrated networks. DrDimont's predictions are explainable, i.e., molecular differences that are the source of high differential drug scores can be retrieved. Our proposed pipeline leverages multi-omics data for differential predictions, e.g. on drug response, and includes prior information on interactions. The case study presented in the vignette uses data published by Krug (2020) <doi:10.1016/j.cell.2020.10.036>. The package license applies only to the software and explicitly not to the included data.

Maintained by Katharina Baum. Last updated 3 years ago.

2.00 score 2 scripts

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