ml4bio / rna-fm Goto Github PK

View Code? Open in Web Editor NEW

187.0 187.0 18.0 2.59 MB

RNA foundation model

Home Page: https://ml4bio.github.io/RNA-FM/

License: MIT License

Python 44.75% Jupyter Notebook 55.25%

rna-fm's People

Contributors

Stargazers

Watchers

Forkers

fbosnic holyseven phosseini ornacomputationteam syntheticbio josephvalencia horikitasaku btparrish erik-whiting modelturnedgeek finitenet tyhouch amelie-iska heany

rna-fm's Issues

Clarification on evaluation

Hi,

Great work!

I am trying to reproduce the SS prediction results (attached image) for ArchiveII600 (3911 sequences) and TS0 (1305 sequences).

While I could exactly reproduce UFold's scores, I could not reproduce RNAFM's scores in the same way. I used the model weights for RNAFM from here.

I got the F1 score 0.666 for TS0, using "RNA-FM-ResNet_bpRNA.pth"; the paper reported 0.704. For ArchiveII600, I got 0.933 using "RNA-FM-ResNet_RNAStralign.pth"; the paper reported 0.941.

I was wondering if the evaluation in your paper was done differently than how UFold did it

I'd really appreciate any help. Thank you!

Code for training from scratch

Is the training code going to be available? Thanks

continue train

Great job！I would like to continue training your model on a new data. I would be grateful if you could provide the training script.

Fine-tuning of ResNet32 for different task with less datapoints

Hi,
I'm reaching out with a question related to the applicability of your models for tasks with a smaller dataset. Specifically, I'm interested in whether there are available scripts for fine-tuning the RNA-FM + ResNet model with few data points, similar to how the RNA-FM (TL) model was adjusted in the paper's RNA 3D closeness prediction task.

Any guidance or resources would be greatly appreciated.

Unable to install

Hi there, I haven't been able to install RNA-FM. I've tried using the recommended conda install from the github repo, pip, on a mac, on a linux cluster, and nothing works. Could you provide a little more detail on the system requirements?

Thank you. Looks like nice work and I'd love to try it out.

Release tutorial and evaluation data?

Would you be able to release mRNA-FM's/RNA-FM's training, tutorial and evaluation data? For instance, as a Drive download link or on HuggingFace? Thanks!

Request an Access to Preprocessed Dataset RNAcentral100

Hi Guys! I am curious whether I can access the preprocessed dataset RNAcentral100 which was used to pre-train the foundation model. If not, should I directly download the data from RNAcentral website? https://ftp.ebi.ac.uk/pub/databases/RNAcentral/releases/19.0/.

Thanks a lot!

The difference between RNA-FM-ResNet_bpRNA. pth and RNA-FM-ResNet-RNAStralign.pth

thank your great job, I want to know what datasets you used for RNA-FM-ResNet_bpRNA.pth and RNA-FM-ResNet_RNAStralign.pth respectively. I guess you use the TR0 training set to obtain RNA-FM-ResNet_bpRNA.pth and the RNAStralign training set to obtain RNA-FM-ResNet_RNAStralign.pth, Is it right? If not, could you tell me which dataset these two files were trained on?

Training data of mRNA-FM

Hi,

Thank you for your work.

Can I ask what's the training data of mRNA-FM?

I met some errors when trying to install RNA-FM. Can you help me out?

Here is part of error logs.

pip install .
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Processing /workspace/work/CLIP/RNA-FM
  Preparing metadata (setup.py) ... done
Collecting numpy==1.22.0 (from rna-fm==0.1.2)
  Downloading numpy-1.22.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.0 kB)
Collecting pandas==1.3.1 (from rna-fm==0.1.2)
  Downloading pandas-1.3.1.tar.gz (4.7 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 4.7/4.7 MB 11.0 MB/s eta 0:00:00
  Installing build dependencies ... error
  error: subprocess-exited-with-error

  × pip subprocess to install build dependencies did not run successfully.
  │ exit code: 1
  ╰─> [824 lines of output]
      Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com, https://pypi.ngc.nvidia.com
      Ignoring numpy: markers 'python_version == "3.7" and (platform_machine != "arm64" or platform_system != "Darwin") and platform_machine != "aarch64"' don't match your environment
      Ignoring numpy: markers 'python_version == "3.8" and (platform_machine != "arm64" or platform_system != "Darwin") and platform_machine != "aarch64"' don't match your environment
      Ignoring numpy: markers 'python_version == "3.7" and platform_machine == "aarch64"' don't match your environment
      Ignoring numpy: markers 'python_version == "3.8" and platform_machine == "aarch64"' don't match your environment
      Ignoring numpy: markers 'python_version == "3.8" and platform_machine == "arm64" and platform_system == "Darwin"' don't match your environment
      Ignoring numpy: markers 'python_version == "3.9" and platform_machine == "arm64" and platform_system == "Darwin"' don't match your environment
      Collecting setuptools>=38.6.0
        Downloading setuptools-69.1.1-py3-none-any.whl.metadata (6.2 kB)
      Collecting wheel
        Downloading wheel-0.43.0-py3-none-any.whl.metadata (2.2 kB)
      Collecting Cython<3,>=0.29.21
        Downloading Cython-0.29.37-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl.metadata (3.1 kB)
      Collecting numpy==1.19.3
        Downloading numpy-1.19.3.zip (7.3 MB)
           ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 7.3/7.3 MB 11.3 MB/s eta 0:00:00
        Installing build dependencies: started
        Installing build dependencies: finished with status 'done'
        Getting requirements to build wheel: started
        Getting requirements to build wheel: finished with status 'done'
        Preparing metadata (pyproject.toml): started
        Preparing metadata (pyproject.toml): finished with status 'done'
      Downloading setuptools-69.1.1-py3-none-any.whl (819 kB)
         ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 819.3/819.3 kB 11.8 MB/s eta 0:00:00
      Downloading wheel-0.43.0-py3-none-any.whl (65 kB)
         ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 65.8/65.8 kB 13.1 MB/s eta 0:00:00
      Downloading Cython-0.29.37-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (1.9 MB)
         ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.9/1.9 MB 11.8 MB/s eta 0:00:00
      Building wheels for collected packages: numpy
        Building wheel for numpy (pyproject.toml): started
        Building wheel for numpy (pyproject.toml): finished with status 'error'
        error: subprocess-exited-with-error

        × Building wheel for numpy (pyproject.toml) did not run successfully.
        │ exit code: 1
        ╰─> [782 lines of output]
            setup.py:67: RuntimeWarning: NumPy 1.19.3 may not yet support Python 3.10.
              warnings.warn(
            Running from numpy source directory.
            /tmp/pip-install-9x5z1ps4/numpy_11ee67fb2b2142c4bcbc63b744069658/tools/cythonize.py:67: DeprecationWarning: The distutils package is deprecated and slated for removal in Python 3.12. Use setuptools or check PEP 632 for potential alternatives
              from distutils.version import LooseVersion
            numpy/random/_bounded_integers.pxd.in has not changed
            numpy/random/_pcg64.pyx has not changed
            numpy/random/_philox.pyx has not changed
            numpy/random/bit_generator.pyx has not changed
            numpy/random/_common.pyx has not changed
            numpy/random/_bounded_integers.pyx.in has not changed
            numpy/random/mtrand.pyx has not changed
            numpy/random/_mt19937.pyx has not changed
            numpy/random/_sfc64.pyx has not changed
            numpy/random/_generator.pyx has not changed
            Processing numpy/random/_bounded_integers.pyx
            Cythonizing sources
            blas_opt_info:
            blas_mkl_info:
            customize UnixCCompiler
              FOUND:
                libraries = ['mkl_rt', 'pthread']
                library_dirs = ['/usr/local/lib']
                define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
                include_dirs = ['/usr/local/include', '/usr/include']

              FOUND:
                libraries = ['mkl_rt', 'pthread']
                library_dirs = ['/usr/local/lib']
                define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
                include_dirs = ['/usr/local/include', '/usr/include']

            non-existing path in 'numpy/distutils': 'site.cfg'
            lapack_opt_info:
            lapack_mkl_info:
              FOUND:
                libraries = ['mkl_rt', 'pthread']
                library_dirs = ['/usr/local/lib']
                define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
                include_dirs = ['/usr/local/include', '/usr/include']

              FOUND:
                libraries = ['mkl_rt', 'pthread']
                library_dirs = ['/usr/local/lib']
                define_macros = [('SCIPY_MKL_H', None), ('HAVE_CBLAS', None)]
                include_dirs = ['/usr/local/include', '/usr/include']

            /usr/lib/python3.10/distutils/dist.py:274: UserWarning: Unknown distribution option: 'define_macros'
              warnings.warn(msg)
            running bdist_wheel
            running build
            running config_cc
            unifing config_cc, config, build_clib, build_ext, build commands --compiler options
            running config_fc
            unifing config_fc, config, build_clib, build_ext, build commands --fcompiler options
            running build_src
            build_src
            building py_modules sources
            building library "npymath" sources
              adding 'build/src.linux-x86_64-3.10/numpy/core/src/npymath' to include_dirs.
            None - nothing done with h_files = ['build/src.linux-x86_64-3.10/numpy/core/src/npymath/npy_math_internal.h']
            building library "npysort" sources
              adding 'build/src.linux-x86_64-3.10/numpy/core/src/common' to include_dirs.
            None - nothing done with h_files = ['build/src.linux-x86_64-3.10/numpy/core/src/common/npy_sort.h', 'build/src.linux-x86_64-3.10/numpy/core/src/common/npy_partition.h', 'build/src.linux-x86_64-3.10/numpy/core/src/common/npy_binsearch.h']
            building library "npyrandom" sources
            building extension "numpy.core._multiarray_tests" sources
            building extension "numpy.core._multiarray_umath" sources
              adding 'build/src.linux-x86_64-3.10/numpy/core/src/umath' to include_dirs.
              adding 'build/src.linux-x86_64-3.10/numpy/core/src/npymath' to include_dirs.
              adding 'build/src.linux-x86_64-3.10/numpy/core/src/common' to include_dirs.
            numpy.core - nothing done with h_files = ['build/src.linux-x86_64-3.10/numpy/core/src/umath/funcs.inc', 'build/src.linux-x86_64-3.10/numpy/core/src/umath/simd.inc', 'build/src.linux-x86_64-3.10/numpy/core/src/umath/loops.h', 'build/src.linux-x86_64-3.10/numpy/core/src/umath/matmul.h', 'build/src.linux-x86_64-3.10/numpy/core/src/umath/clip.h', 'build/src.linux-x86_64-3.10/numpy/core/src/npymath/npy_math_internal.h', 'build/src.linux-x86_64-3.10/numpy/core/src/common/templ_common.h', 'build/src.linux-x86_64-3.10/numpy/core/include/numpy/config.h', 'build/src.linux-x86_64-3.10/numpy/core/include/numpy/_numpyconfig.h', 'build/src.linux-x86_64-3.10/numpy/core/include/numpy/__multiarray_api.h', 'build/src.linux-x86_64-3.10/numpy/core/include/numpy/__ufunc_api.h']

Recommend Projects

React

A declarative, efficient, and flexible JavaScript library for building user interfaces.
Vue.js

🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
Typescript

TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
TensorFlow

An Open Source Machine Learning Framework for Everyone
Django

The Web framework for perfectionists with deadlines.
Laravel

A PHP framework for web artisans
D3

Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

javascript

JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
web

Some thing interesting about web. New door for the world.
server

A server is a program made to process requests and deliver data to clients.
Machine learning

Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Visualization

Some thing interesting about visualization, use data art
Game

Some thing interesting about game, make everyone happy.

Recommend Org

Facebook

We are working to build community through open source technology. NB: members must have two-factor auth.
Microsoft

Open source projects and samples from Microsoft.
Google

Google ❤️ Open Source for everyone.
Alibaba

Alibaba Open Source for everyone
D3

Data-Driven Documents codes.
Tencent

China tencent open source team.