Assembly and binning of metagenomes

☯️🧬 Refined and Overlapped Binning of Metagenomic Contigs Using Assembly Graphs

A fast and space-efficient pre-filter for querying very large collections of nucleotide sequences.

This project focuses on cleaning and analyzing a loan application dataset to gain insights into the factors influencing loan defaults. Through systematic data cleaning, visualization, and merging with previous application data, it provides a robust foundation for further predictive modeling.

Statistical standard error estimation tools for correlated data

Express metagenome binning

✨🧬 Refined binning of metagenomic contigs using assembly graphs

Codes for Practical experiments of Data Warehousing and Mining (Semester V - Computer Engineering - Mumbai University)

metagenomics pipeline supporting The UnO Project for identifying pathogens in common across outbreak samples

🚦🧬 Binning Metagenomic Contigs via Composition, Coverage and Assembly Graphs

Autometa: Automated Extraction of Genomes from Shotgun Metagenomes

Optimal binning: monotonic binning with constraints. Support batch & stream optimal binning. Scorecard modelling and counterfactual explanations.

ImputeCC enhances integrative Hi-C-based metagenomic binning through constrained random-walk-based imputation

Support the NASA / GlobColour / CCI ISIN grid used for MODIS L3BIN satellite products.

A hybrid assembly and MAG recovery pipeline (and more!)

Data auto-binning algorithm- takes x,y,z data and organizes it in x and y user prescribed bins, auto-interpolates data gaps, then plots z

Scorecard Development in R, 评分卡

The feature engineering techniques discussed are - dimensionality reduction(pca), scaling(standard scaler, normalizer, minmaxscaler), categorical encoding(one hot/dummy), binning, clustering, feature selection. These are techniques performed on a dataset consisting of Californian House Prices.

Feature Engineering steps implemented in Google Colab with step-by-step view.