Comprehensive automatic differentiation in C++
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Updated
Jul 17, 2024 - C++
Comprehensive automatic differentiation in C++
Protein order and disorder data for Keras, Tensor Flow and Edward frameworks with automated update cycle made for continuous learning applications.
The software behind Chai's open-source Real-Time PCR instrument
A Quantum Computing and Machine Learning Model that accelerates the Drug Research and Development process
Fast and Accurate CE-, GC- and LC-MS(/MS) Data Processing
Binomica Public Repository for Biological Parts
A free and collaborative space for Machine Learning 🤖 applied to Biology 🧬
Explore biomolecular pathways in Reactome from the command-line or a Python script
This repo is dedicated to make bioinformatics resources available for anyone who wish to enter this field. (You may find it useful or not useful based on your level). I am still embarking my path in the field, just posting things based on my knowledge and the things which worked for me personally, they may/may not work with you, totally fine.
TISIGNER: Unleash the power of synthetic biology
This project is done for the Biomedicine, Biotechnology and Public Healthcare department of the University of Cádiz. They idea is to automatize the processing of the microscopic images of their experiments related to angiogenesis.
This pipeline provides a way to perform pharmaceutical compounds virtual screening using similarity-based analysis, ligand-based and structure-based techniques. The pipeline contains a collections of modules to perform a variety of analysis.
genomics, biochemistry, biotechnology, cell biology, biophysics, ecology
Resources for learning Biotechnology, Biology, Synthetic Biology, Genomic and Bioinformatic
Wetlab Calculator
Icahn Graduate School of Medicine: Systems Biology & Biotechnology Specialization
A Python molecular biology CAD tool with a dead-simple user interface
I developed Machine Learning Software with multiple models that predict and classify AID362 biology lab data. Accuracy values are 99% and above, and F1, Recall and Precision scores are average (average of 3) 78.33%. The purpose of this study is to prove that we can establish an artificial intelligence (machine learning) system in health. With my…
FLYCOP (FLexible sYnthetic Consortium OPtimization)
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