bioconvert VS DNABERT

Compare bioconvert vs DNABERT and see what are their differences.

bioconvert

Bioconvert is a collaborative project to facilitate the interconversion of life science data from one format to another. (by bioconvert)
ngs bam2sam Conversion phylogeny Sequence sam2bam bam2cram bcf2vcf bigbed2bed cram2bam cram2sam embl2ena embl2fasta embl2genbank fasta2fastq genbank2embl genbank2fasta Sniffer Bioinformatics convertor
Source Code
bioconvert.readthedocs.io
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DNABERT

DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome (by jerryji1993)
kmer dnabert-model kmer-format GPU Sequence Genome Machine Learning Deep Learning Natural Language Processing NLP
Source Code
doi.org
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bioconvert DNABERT
1 1
351 543
1.1% -
6.1 3.1
5 months ago about 2 months ago
Python Python
GNU General Public License v3.0 only Apache License 2.0
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bioconvert

Posts with mentions or reviews of bioconvert. We have used some of these posts to build our list of alternatives and similar projects.

DNABERT

Posts with mentions or reviews of DNABERT. We have used some of these posts to build our list of alternatives and similar projects.
  • [D] New to DNABERT
    1 project | /r/MachineLearning | 3 Nov 2023
    If I want to get started, they said it's optional to pre-train (so you can skip to step 3). This is where I got tripped up: "Note that the sequences are in kmer format, so you will need to convert your sequences into that." From what I understand, you need to do this so that all of the sequences are the same length? So kmer=6 means all of the sequences are length 6? Someone suggested that I take the first nucleotide in the promoter and grab 3 nucleotides before and 3 nucleotides after (+/-3 bases). I don't think that's how the kmer thing works though? I tried replicating how I think it works down below (I got confused on the last row of the 'after' df). Please correct me if I'm wrong!

What are some alternatives?

When comparing bioconvert and DNABERT you can also consider the following projects:

malware-phylogeny - malware phylogeny for WSO web shell, Shellbot IRC bot and algorithm

courses - This repository is a curated collection of links to various courses and resources about Artificial Intelligence (AI)

Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages

datasets - 🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools

stanford-tensorflow-tutorials - This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.

spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python

nlp-recipes - Natural Language Processing Best Practices & Examples

transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

OOK_Audio - De Bruijn Sequence WAV File Generator for the HackRF

bioconvert vs malware-phylogeny DNABERT vs courses DNABERT vs Stanza DNABERT vs datasets DNABERT vs stanford-tensorflow-tutorials DNABERT vs spaCy DNABERT vs nlp-recipes DNABERT vs transformers DNABERT vs OOK_Audio