💻 Software & Environment Setup
Bioinformatics & Computational Biology
The computational toolchain and environment used throughout Clinical & Omics AI: Multimodal EHR-Genomics Analysis Using Transfer Learning.
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# Recommended conda environmentconda create -n dstc_env python=3.11 -yconda activate dstc_envpip install biopython pandas pysam scikit-bioBiopython
Sequence I/O, BLAST parsing, phylogenetics
Sequence I/O, BLAST parsing, phylogenetics
SAMtools / BCFtools
Alignment & variant file manipulation
Alignment & variant file manipulation
Bioconductor (R)
DESeq2, edgeR & limma for expression analysis
DESeq2, edgeR & limma for expression analysis
Nextflow
Reproducible, portable NGS pipelines
Reproducible, portable NGS pipelines
Supported OS: Linux (Ubuntu 22.04+), macOS, or Windows via WSL2.
Hardware: 16 GB RAM minimum; a CUDA GPU helps for deep-learning modules.
Earn government-registered certification in Clinical & Omics AI: Multimodal EHR-Genomics Analysis Using Transfer Learning
e-Certificate and e-Marksheet issued on successful completion.
