Author: gene_x

Abstract: -- 0, generate the ref.gtf and ref.bed from GenBank file: HD21-2_new.gtf and HD21-2_new.bed -- #[PREPARE gtf from gff] https://github.com/gpertea/gffread/issues/3 #DEL gb2

Author: gene_x

Abstract: Biopython is a useful library for working with biological data in Python. You can use it to download sequences from GenBank by specifying a taxonomy ID. Here's a code example to download sequences usi

Author: gene_x

Abstract: GPT (short for "Generative Pre-trained Transformer") is a type of transformer model, which is an advanced deep learning architecture. It is based on the Transformer architecture introduced by Vaswan

Author: gene_x

Abstract: import plotly.graph_objects as go import pandas as pd from sklearn.decomposition import PCA import numpy as np from scipy.linalg import eigh, sqrtm # Read i

Author: gene_x

Abstract: There are many command-line tools and utilities that can be useful in bioinformatics for quick data processing, analysis, and manipulation. Some of these oneliner tools include: - awk: A versatile te

Author: gene_x

Abstract: Biopython is a powerful library for bioinformatics that provides tools for manipulating biological sequences, working with 3D structures, performing genome analysis, and more. Among its many features,

Author: gene_x

Abstract: #!/usr/bin/env python3 import os import sys import logging import tempfile import shutil import datetime from pathlib import Path import psycopg2 from

Author: gene_x

Abstract: > A simple machine learning example using Python and the scikit-learn library for the classification of the Iris dataset. The Iris dataset is a classic dataset containing measurements of iris flowers

Author: gene_x

Abstract: Abstract: Next-generation sequencing (NGS) has revolutionized the field of genomics, allowing for rapid detection and analysis of infectious agents. However, the complexity of NGS data and the lack o

Author: gene_x

Abstract: import plotly.graph_objects as go import pandas as pd from sklearn.decomposition import PCA import numpy as np # Read in data as a pandas dataframe #df = pd.DataFrame({