<div align="center">
<picture>
<source media="(prefers-color-scheme: light)" srcset="https://ibm.github.io/AutoPeptideML/imgs/APML_light.png" height="250x">
<img alt="logo" src="https://ibm.github.io/AutoPeptideML/imgs/APML_dark.png">
</picture>
<h1>AutoPeptideML</h1>
<p>
<strong>AutoML system for building trustworthy peptide bioactivity predictors</strong>
</p>
<p>
<a href="https://ibm.github.io/AutoPeptideML/"><img alt="Tutorials" src="https://img.shields.io/badge/docs-tutorials-green" /></a>
<a href="https://github.com/IBM/AutoPeptideML/blob/main/LICENSE"><img alt="GitHub" src="https://img.shields.io/github/license/IBM/AutoPeptideML" /></a>
<a href="https://pypi.org/project/autopeptideml/"><img src="https://img.shields.io/pypi/v/autopeptideml" /></a>
<a href="https://static.pepy.tech/project/autopeptideml/"><img src="https://static.pepy.tech/badge/autopeptideml" /></a>
<a target="_blank" href="https://colab.research.google.com/github/IBM/AutoPeptideML/blob/main/examples/AutoPeptideML_Collab.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
</p>
</div>
- **Documentation:** <a href="https://ibm.github.io/AutoPeptideML/" target="_blank">https://ibm.github.io/AutoPeptideML</a>
- **Source Code:** <a href="https://github.com/IBM/AutoPeptideML" target="_blank">https://github.com/IBM/AutoPeptideML</a>
- **Webserver:** <a href="http://peptide.ucd.ie/AutoPeptideML" target="_blank">http://peptide.ucd.ie/AutoPeptideML</a>
- **Google Collaboratory Notebook:** <a href="https://colab.research.google.com/github/IBM/AutoPeptideML/blob/main/examples/AutoPeptideML_Collab.ipynb" target="_blank">AutoPeptideML_Collab.ipynb</a>
- **Paper:** <a href="https://doi.org/10.1093/bioinformatics/btae555" target="_blank">https://doi.org/10.1093/bioinformatics/btae555</a>
- **Blog post:** <a href="https://portal.valencelabs.com/blogs/post/autopeptideml-building-peptide-bioactivity-predictors-automatically-IZZKbJ3Un0qjo4i" target="_blank">Portal - AutoPeptideML Tutorial</a>
- **Models and Paper SI**: <a href="https://zenodo.org/records/13363975/files/AutoPeptideML_SI.tar.gz?download=1" target="_blank">Zenodo Repository</a>
AutoPeptideML allows researchers without prior knowledge of machine learning to build models that are:
- **Trustworthy:** Robust evaluation following community guidelines for ML evaluation reporting in life sciences [DOME](https://www.nature.com/articles/s41592-021-01205-4).
- **Interpretable:** Output contains a PDF summary of the model evaluation explaining how to interpret the results to understand how reliable the model is.
- **Reproducible:** Output contains all necessary information for other researchers to reproduce the training and verify the results.
- **State-of-the-art:** Models generated with this system are competitive with state-of-the-art handcrafted approaches.
We recommend the use of the Google Collaboratory Notebook for users that want greater control of the model training process and the webserver for users that prefer ease of use.
## Contents
<details open markdown="1"><summary><b>Table of Contents</b></summary>
- [Intallation Guide](#installation)
- [Benchmark Data](#benchmark)
- [Documentation](#documentation)
- [Examples](#examples)
- [License](#license)
- [Acknowledgements](#acknowledgements)
</details>
## Installation <a name="installation"></a>
Installing in a conda environment is recommended. For creating the environment, please run:
```bash
conda create -n autopeptideml python
conda activate autopeptideml
```
### 1. Python Package
#### 1.1.From PyPI
```bash
pip install autopeptideml
```
#### 1.2. Directly from source
```bash
pip install git+https://github.com/IBM/AutoPeptideML
```
### 2. Third-party dependencies
- MMSeqs2 [https://github.com/steineggerlab/mmseqs2](https://github.com/steineggerlab/mmseqs2)
```bash
# static build with AVX2 (fastest) (check using: cat /proc/cpuinfo | grep avx2)
wget https://mmseqs.com/latest/mmseqs-linux-avx2.tar.gz; tar xvfz mmseqs-linux-avx2.tar.gz; export PATH=$(pwd)/mmseqs/bin/:$PATH
# static build with SSE4.1 (check using: cat /proc/cpuinfo | grep sse4)
wget https://mmseqs.com/latest/mmseqs-linux-sse41.tar.gz; tar xvfz mmseqs-linux-sse41.tar.gz; export PATH=$(pwd)/mmseqs/bin/:$PATH
# static build with SSE2 (slowest, for very old systems) (check using: cat /proc/cpuinfo | grep sse2)
wget https://mmseqs.com/latest/mmseqs-linux-sse2.tar.gz; tar xvfz mmseqs-linux-sse2.tar.gz; export PATH=$(pwd)/mmseqs/bin/:$PATH
# MacOS
brew install mmseqs2
```
To use Needleman-Wunch, either:
```bash
conda install -c bioconda emboss
```
or
```bash
sudo apt install emboss
```
## Benchmark data <a name="benchmark"></a>
Data used to benchmark our approach has been selected from the benchmarks collected by [Du et al, 2023](https://academic.oup.com/bib/article-abstract/24/3/bbad135/7107929). A new set of benchmarks was constructed from the original set following the new data acquisition and dataset partitioning methods within AutoPeptideML. To download the datasets:
- **Original UniDL4BioPep Benchmarks:** Please check the project [Github Repository](https://github.com/dzjxzyd/UniDL4BioPep/tree/main).
- **⚠️ New AutoPeptideML Benchmarks (Amended version):** Can be downloaded from this [link](https://drive.google.com/u/0/uc?id=1UmDu773CdkBFqkitK550uO6zoxhU1bUB&export=download). Please note that these are not exactly the same benchmarks as used in the paper, those are kept in the next line for reproducibility (see [Issue #24](https://github.com/IBM/AutoPeptideML/issues/24) for more details).
- **⚠️ New AutoPeptideML Benchmarks (Paper version):** Can be downloaded from this [link](https://drive.google.com/u/0/uc?id=1UmDu773CdkBFqkitK550uO6zoxhU1bUB&export=download). This benchmarks have a small number of overlapping sequences between training and testing due to a bug described in [Issue #24](https://github.com/IBM/AutoPeptideML/issues/24). They are kept here for reproducibility.
## Documentation <a name="documentation"></a>
<details markdown="1"><summary><b>1. Model builder options</summary></b><a name="builder"></a>
**Dataset construction**
- `dataset`: File with positive peptides in `FASTA` or `CSV` file. It can also contain negative peptides in which case the files should contain the labels (0: negative or 1: positive) either in the header (`FASTA`) or in column `Y` (`CSV`).
- `--balance`: If `True`, it balances the datasets by oversampling the underrepresented label.
- `--autosearch`: If `True`, it searches for negative peptides.
- `--autosearch_tags`: Comma separated list of tags that may overlap with positive activity that are going to be excluded from the negative peptides.
- `--autosearch_proportion`: Negative:positive ration when automatically drawing negative controls from the bioactive peptides database (Default: 1.0).
**Output**
- `--outputdirdir`: Output directory (Default: `./apml_result/apml_result`).
**Protein Language Model**
- `--plm`: Protein Language Model for computing peptide representations. Available options: `esm2-8m`, `esm2-35m`, `esm2-150m`, `esm2-650m`, `esm2-3b`, `esm2-15b`, `esm1b`, `prot-t5-xxl`, `prot-t5-xl`, `protbert`, `prost-t5`. (Default: `esm2-8m`). Please note: Larger Models might not fit into GPU RAM, if it is necessary for your purposes, please create a new issue.
- `--plm_batch_size`: Number of peptides for which to batch the PLM computation.(Default: 12).
**Dataset Partitioning**
- `--test_partition`: Whether to divide the dataset in train/test splits. (Default: `True`).
- `--test_threshold`: Maximum sequence identity allowed between train and test. (Default: 0.3).
- `--test_size`: Proportion of data to be assigned to evaluation set. (Default: 0.2).
- `--test_alignment`: Alignment algorithm used for computing sequence identities. Available options: `peptides`, `needle`. (Default: `peptides`).
- `--splits`: Path to directory with train and test splits. Expected contents: `train.csv` and `test.csv`.
- `--val_partition`: Whether to divide dataset in train/validation folds.
- `--val_method`: Method to use for creating train/validation folds. Options available: `random`, `graph-part`. (Default: `random`)
- `--val_threshold`: Maximum sequence identity allowed between train and validation. (Default: 0.5).
- `--val_alignment`: Alignment algorithm used for computing sequence identities. Available options: `peptides` `needle`. (Default: `peptides`).
- `--val_n_folds`: Number of folds (Default: 10).
- `--folds`: Path to directory with train/validation folds. Expected contents: `train_{fold}.csv` and `valid_{fold}.csv`.
**Model Selection and Hyperparameter Optimisation**
- `--config`: Name of one of the pre-defined configuration files (see `autopeptideml/data/configs`) or path to a custom configuration file (see next section).
**Other**
- `--verbose`: Whether to display information about runtime (Default: True).
- `--threads`: Number of threads to use for parallelization. (Default: Number of cores in the machine).
- `--seed`: Seed for pseudorandom number generators. Controls stochastic processes. (Default: 42)
</details>
<details markdown="1"><summary><b>2. Predict</summary></b><a name="predict"></a>
- `dataset`: File with problem peptides in `FASTA` or `CSV` file.
- `--ensemble`: Path to the a file containing a previous AutoPeptideML result.
- `--outputdir`: Output directory (Default: `./apml_predictions`).
- `--verbose`: Whether to display information about runtime (Default: True).
- `--threads`: Number of threads to use for parallelization. (Default: Number of cores in the machine).
- `--plm`: Protein Language Model for computing peptide representations. Must be the same as used to train the model. Available options: `esm2-8m`, `esm2-35m`, `esm2-150m`, `esm2-650m`, `esm2-3b`, `esm2-15b`, `esm1b`, `prot-t5-xxl`, `prot-t5-xl`, `protbert`, `prost-t5`. (Default: `esm2-8m`). Please note: Larger Models might not fit into GPU RAM, if it is necessary for your purposes, please create a new issue.
- `--plm_batch_size`: Number of peptides for which to batch the PLM computation.(Default: 12).
</details>
<details markdown="1"><summary><b>3. Hyperparameter Optimisation and model selection</summary></b><a name="hpo"></a>
The experiment configuration is a file in `JSON` format describing the hyperparameter optimisation search space and the composition of the final ensemble. The first level of the file is a dictionary with a single key (`ensemble` or `model_selection` or `model_selection`) and a list of search spaces for the hyperparameter optimisation. For each model within the `ensemble` list, `n` different models will be trained one per cross-validation fold; in the case of `model_selection`, only one of the algorithms will comprise the final ensemble; in the case of `model_selection`, only one of the algorithms will comprise the final ensemble.
Each experiment requires the following fields:
- `model`: Defines the ML algorithm. Options: `KNearestNeighbours`, `SVM`, `RFC`, `XGBoost`, `LGBM`, `MLP`, and `UniDL4BioPep`. More options will be added in subsequent releases and they can be implemented upon request.
- `trials`: Defines the number of iterations for the hyperparameter optimisation search.
- `optimization_metric`: Defines the metric that should be used for directing the optimisation search. Always, the metric will be calculated as the average across the `n` cross-validation folds. For the metrics available all of the binary classification within the list in the [scikit-learn documentation](https://scikit-learn.org/stable/modules/model_evaluation.html#classification-metrics) are supported (Default: Matthew's correlation coefficient, MCC).
- `hyperparameter-space`: List of dictionaries that defines the hyperparameter search space proper. Each of the dictionaries within correspond to a different hyperparameter and may have the following fields:
- `name`: It has to correspond with the corresponding hyperparameter in the model implementation. Most of the simpler ML models use the `scikit-learn` implementation, `LGBM` uses the Microsoft implementation (More information on [LGBM Repository](https://github.com/microsoft/LightGBM)) and `UniDL4BioPep` uses the PyTorch implementation (More information on [UniDL4BioPep PyTorch Repository](https://github.com/David-Dingle/UniDL4BioPep_ASL_PyTorch)), though for this model hyperparameter optimisation is not recommended.
- `type`: Defines the type of hyperparameter. Options: `int`, `float`, or `categorical`.
- `min` and `max`: Defines the lower and upper bounds of the search space for types `int` and `float`.
- `log`: Boolean value that defines whether the search should be done in logarithmic space or not. Accelerates searches through vast spaces for example for learning rates (1e-7 to 1). It is not optional.
- `value`: Defines the list of options available for a hyperparameter of type `categorical` for example types of kernel (`linear`, `rbf`, `sigmoid`) for a Support Vector Machine.
There is an example available in the [default configuration file](https://github.ibm.com/raulfd/AutoPeptideML/blob/main/autopeptideml/data/configs/default_config.json).
</details>
<details markdown="1"><summary><b>4. API</summary></b><a name="api"></a>
Please check the [Code reference documentation](https://ibm.github.io/AutoPeptideML/autopeptideml/)
</details>
## Examples <a name="examples"></a>
```bash
autopeptideml dataset.csv
```
```bash
autopeptideml dataset.csv --val_method graph-part --val_threshold 0.3 --val_alignment needle
autopeptideml dataset.csv
```
```bash
autopeptideml dataset.csv --val_method graph-part --val_threshold 0.3 --val_alignment needle
```
For running predictions
```bash
autopeptideml-predict peptides.csv --ensemble AB_1/ensemble
```
License <a name="license"></a>
-------
AutoPeptideML is an open-source software licensed under the MIT Clause License. Check the details in the [LICENSE](https://github.com/IBM/AutoPeptideML/blob/master/LICENSE) file.
Credits <a name="acknowledgements"></a>
-------
Special thanks to [Silvia González López](https://www.linkedin.com/in/silvia-gonz%C3%A1lez-l%C3%B3pez-717558221/) for designing the AutoPeptideML logo and to [Marcos Martínez Galindo](https://www.linkedin.com/in/marcosmartinezgalindo) for his aid in setting up the AutoPeptideML webserver.
Raw data
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"author_email": "raulfd@ibm.com",
"download_url": "https://files.pythonhosted.org/packages/50/f5/eb05c64e5b0567e830bcacab4639d6e9d0acb3a0516b3dc0534451d49eb0/autopeptideml-1.0.4.tar.gz",
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"description": "<div align=\"center\">\n\n <picture>\n <source media=\"(prefers-color-scheme: light)\" srcset=\"https://ibm.github.io/AutoPeptideML/imgs/APML_light.png\" height=\"250x\">\n <img alt=\"logo\" src=\"https://ibm.github.io/AutoPeptideML/imgs/APML_dark.png\">\n </picture>\n\n <h1>AutoPeptideML</h1>\n\n <p>\n <strong>AutoML system for building trustworthy peptide bioactivity predictors</strong>\n </p>\n\n <p> \n \n<a href=\"https://ibm.github.io/AutoPeptideML/\"><img alt=\"Tutorials\" src=\"https://img.shields.io/badge/docs-tutorials-green\" /></a>\n<a href=\"https://github.com/IBM/AutoPeptideML/blob/main/LICENSE\"><img alt=\"GitHub\" src=\"https://img.shields.io/github/license/IBM/AutoPeptideML\" /></a>\n<a href=\"https://pypi.org/project/autopeptideml/\"><img src=\"https://img.shields.io/pypi/v/autopeptideml\" /></a>\n<a href=\"https://static.pepy.tech/project/autopeptideml/\"><img src=\"https://static.pepy.tech/badge/autopeptideml\" /></a>\n<a target=\"_blank\" href=\"https://colab.research.google.com/github/IBM/AutoPeptideML/blob/main/examples/AutoPeptideML_Collab.ipynb\">\n <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n</a>\n\n\n\n </p>\n</div>\n\n- **Documentation:** <a href=\"https://ibm.github.io/AutoPeptideML/\" target=\"_blank\">https://ibm.github.io/AutoPeptideML</a>\n- **Source Code:** <a href=\"https://github.com/IBM/AutoPeptideML\" target=\"_blank\">https://github.com/IBM/AutoPeptideML</a>\n- **Webserver:** <a href=\"http://peptide.ucd.ie/AutoPeptideML\" target=\"_blank\">http://peptide.ucd.ie/AutoPeptideML</a>\n- **Google Collaboratory Notebook:** <a href=\"https://colab.research.google.com/github/IBM/AutoPeptideML/blob/main/examples/AutoPeptideML_Collab.ipynb\" target=\"_blank\">AutoPeptideML_Collab.ipynb</a>\n- **Paper:** <a href=\"https://doi.org/10.1093/bioinformatics/btae555\" target=\"_blank\">https://doi.org/10.1093/bioinformatics/btae555</a>\n- **Blog post:** <a href=\"https://portal.valencelabs.com/blogs/post/autopeptideml-building-peptide-bioactivity-predictors-automatically-IZZKbJ3Un0qjo4i\" target=\"_blank\">Portal - AutoPeptideML Tutorial</a>\n- **Models and Paper SI**: <a href=\"https://zenodo.org/records/13363975/files/AutoPeptideML_SI.tar.gz?download=1\" target=\"_blank\">Zenodo Repository</a>\n\nAutoPeptideML allows researchers without prior knowledge of machine learning to build models that are:\n\n- **Trustworthy:** Robust evaluation following community guidelines for ML evaluation reporting in life sciences [DOME](https://www.nature.com/articles/s41592-021-01205-4).\n- **Interpretable:** Output contains a PDF summary of the model evaluation explaining how to interpret the results to understand how reliable the model is.\n- **Reproducible:** Output contains all necessary information for other researchers to reproduce the training and verify the results.\n- **State-of-the-art:** Models generated with this system are competitive with state-of-the-art handcrafted approaches.\n\nWe recommend the use of the Google Collaboratory Notebook for users that want greater control of the model training process and the webserver for users that prefer ease of use.\n\n## Contents\n\n<details open markdown=\"1\"><summary><b>Table of Contents</b></summary>\n\n- [Intallation Guide](#installation)\n- [Benchmark Data](#benchmark)\n- [Documentation](#documentation)\n- [Examples](#examples)\n- [License](#license)\n- [Acknowledgements](#acknowledgements)\n </details>\n\n## Installation <a name=\"installation\"></a>\n\nInstalling in a conda environment is recommended. For creating the environment, please run:\n\n```bash\nconda create -n autopeptideml python\nconda activate autopeptideml\n```\n\n### 1. Python Package\n\n#### 1.1.From PyPI\n\n\n```bash\npip install autopeptideml\n```\n\n#### 1.2. Directly from source\n\n```bash\npip install git+https://github.com/IBM/AutoPeptideML\n```\n\n### 2. Third-party dependencies\n\n - MMSeqs2 [https://github.com/steineggerlab/mmseqs2](https://github.com/steineggerlab/mmseqs2)\n\n ```bash\n # static build with AVX2 (fastest) (check using: cat /proc/cpuinfo | grep avx2)\n wget https://mmseqs.com/latest/mmseqs-linux-avx2.tar.gz; tar xvfz mmseqs-linux-avx2.tar.gz; export PATH=$(pwd)/mmseqs/bin/:$PATH\n\n # static build with SSE4.1 (check using: cat /proc/cpuinfo | grep sse4)\n wget https://mmseqs.com/latest/mmseqs-linux-sse41.tar.gz; tar xvfz mmseqs-linux-sse41.tar.gz; export PATH=$(pwd)/mmseqs/bin/:$PATH\n\n # static build with SSE2 (slowest, for very old systems) (check using: cat /proc/cpuinfo | grep sse2)\n wget https://mmseqs.com/latest/mmseqs-linux-sse2.tar.gz; tar xvfz mmseqs-linux-sse2.tar.gz; export PATH=$(pwd)/mmseqs/bin/:$PATH\n\n # MacOS\n brew install mmseqs2 \n ```\n\n To use Needleman-Wunch, either:\n\n ```bash\n conda install -c bioconda emboss\n ```\n or\n\n ```bash\n sudo apt install emboss\n ```\n\n## Benchmark data <a name=\"benchmark\"></a>\n\nData used to benchmark our approach has been selected from the benchmarks collected by [Du et al, 2023](https://academic.oup.com/bib/article-abstract/24/3/bbad135/7107929). A new set of benchmarks was constructed from the original set following the new data acquisition and dataset partitioning methods within AutoPeptideML. To download the datasets:\n\n- **Original UniDL4BioPep Benchmarks:** Please check the project [Github Repository](https://github.com/dzjxzyd/UniDL4BioPep/tree/main).\n- **\u26a0\ufe0f New AutoPeptideML Benchmarks (Amended version):** Can be downloaded from this [link](https://drive.google.com/u/0/uc?id=1UmDu773CdkBFqkitK550uO6zoxhU1bUB&export=download). Please note that these are not exactly the same benchmarks as used in the paper, those are kept in the next line for reproducibility (see [Issue #24](https://github.com/IBM/AutoPeptideML/issues/24) for more details).\n- **\u26a0\ufe0f New AutoPeptideML Benchmarks (Paper version):** Can be downloaded from this [link](https://drive.google.com/u/0/uc?id=1UmDu773CdkBFqkitK550uO6zoxhU1bUB&export=download). This benchmarks have a small number of overlapping sequences between training and testing due to a bug described in [Issue #24](https://github.com/IBM/AutoPeptideML/issues/24). They are kept here for reproducibility.\n\n## Documentation <a name=\"documentation\"></a>\n\n<details markdown=\"1\"><summary><b>1. Model builder options</summary></b><a name=\"builder\"></a>\n\n**Dataset construction**\n\n- `dataset`: File with positive peptides in `FASTA` or `CSV` file. It can also contain negative peptides in which case the files should contain the labels (0: negative or 1: positive) either in the header (`FASTA`) or in column `Y` (`CSV`).\n- `--balance`: If `True`, it balances the datasets by oversampling the underrepresented label.\n- `--autosearch`: If `True`, it searches for negative peptides.\n- `--autosearch_tags`: Comma separated list of tags that may overlap with positive activity that are going to be excluded from the negative peptides.\n- `--autosearch_proportion`: Negative:positive ration when automatically drawing negative controls from the bioactive peptides database (Default: 1.0).\n\n\n**Output**\n\n- `--outputdirdir`: Output directory (Default: `./apml_result/apml_result`).\n\n**Protein Language Model**\n\n- `--plm`: Protein Language Model for computing peptide representations. Available options: `esm2-8m`, `esm2-35m`, `esm2-150m`, `esm2-650m`, `esm2-3b`, `esm2-15b`, `esm1b`, `prot-t5-xxl`, `prot-t5-xl`, `protbert`, `prost-t5`. (Default: `esm2-8m`). Please note: Larger Models might not fit into GPU RAM, if it is necessary for your purposes, please create a new issue.\n- `--plm_batch_size`: Number of peptides for which to batch the PLM computation.(Default: 12).\n\n**Dataset Partitioning**\n\n- `--test_partition`: Whether to divide the dataset in train/test splits. (Default: `True`).\n- `--test_threshold`: Maximum sequence identity allowed between train and test. (Default: 0.3).\n- `--test_size`: Proportion of data to be assigned to evaluation set. (Default: 0.2).\n- `--test_alignment`: Alignment algorithm used for computing sequence identities. Available options: `peptides`, `needle`. (Default: `peptides`).\n- `--splits`: Path to directory with train and test splits. Expected contents: `train.csv` and `test.csv`.\n- `--val_partition`: Whether to divide dataset in train/validation folds.\n- `--val_method`: Method to use for creating train/validation folds. Options available: `random`, `graph-part`. (Default: `random`)\n- `--val_threshold`: Maximum sequence identity allowed between train and validation. (Default: 0.5).\n- `--val_alignment`: Alignment algorithm used for computing sequence identities. Available options: `peptides` `needle`. (Default: `peptides`).\n- `--val_n_folds`: Number of folds (Default: 10).\n- `--folds`: Path to directory with train/validation folds. Expected contents: `train_{fold}.csv` and `valid_{fold}.csv`.\n\n**Model Selection and Hyperparameter Optimisation**\n\n- `--config`: Name of one of the pre-defined configuration files (see `autopeptideml/data/configs`) or path to a custom configuration file (see next section).\n\n**Other**\n\n- `--verbose`: Whether to display information about runtime (Default: True).\n- `--threads`: Number of threads to use for parallelization. (Default: Number of cores in the machine).\n- `--seed`: Seed for pseudorandom number generators. Controls stochastic processes. (Default: 42)\n\n</details>\n\n<details markdown=\"1\"><summary><b>2. Predict</summary></b><a name=\"predict\"></a>\n\n- `dataset`: File with problem peptides in `FASTA` or `CSV` file.\n- `--ensemble`: Path to the a file containing a previous AutoPeptideML result.\n- `--outputdir`: Output directory (Default: `./apml_predictions`).\n- `--verbose`: Whether to display information about runtime (Default: True).\n- `--threads`: Number of threads to use for parallelization. (Default: Number of cores in the machine).\n- `--plm`: Protein Language Model for computing peptide representations. Must be the same as used to train the model. Available options: `esm2-8m`, `esm2-35m`, `esm2-150m`, `esm2-650m`, `esm2-3b`, `esm2-15b`, `esm1b`, `prot-t5-xxl`, `prot-t5-xl`, `protbert`, `prost-t5`. (Default: `esm2-8m`). Please note: Larger Models might not fit into GPU RAM, if it is necessary for your purposes, please create a new issue.\n- `--plm_batch_size`: Number of peptides for which to batch the PLM computation.(Default: 12).\n\n</details>\n\n<details markdown=\"1\"><summary><b>3. Hyperparameter Optimisation and model selection</summary></b><a name=\"hpo\"></a>\n\nThe experiment configuration is a file in `JSON` format describing the hyperparameter optimisation search space and the composition of the final ensemble. The first level of the file is a dictionary with a single key (`ensemble` or `model_selection` or `model_selection`) and a list of search spaces for the hyperparameter optimisation. For each model within the `ensemble` list, `n` different models will be trained one per cross-validation fold; in the case of `model_selection`, only one of the algorithms will comprise the final ensemble; in the case of `model_selection`, only one of the algorithms will comprise the final ensemble.\n\nEach experiment requires the following fields:\n\n- `model`: Defines the ML algorithm. Options: `KNearestNeighbours`, `SVM`, `RFC`, `XGBoost`, `LGBM`, `MLP`, and `UniDL4BioPep`. More options will be added in subsequent releases and they can be implemented upon request.\n- `trials`: Defines the number of iterations for the hyperparameter optimisation search.\n- `optimization_metric`: Defines the metric that should be used for directing the optimisation search. Always, the metric will be calculated as the average across the `n` cross-validation folds. For the metrics available all of the binary classification within the list in the [scikit-learn documentation](https://scikit-learn.org/stable/modules/model_evaluation.html#classification-metrics) are supported (Default: Matthew's correlation coefficient, MCC).\n- `hyperparameter-space`: List of dictionaries that defines the hyperparameter search space proper. Each of the dictionaries within correspond to a different hyperparameter and may have the following fields:\n - `name`: It has to correspond with the corresponding hyperparameter in the model implementation. Most of the simpler ML models use the `scikit-learn` implementation, `LGBM` uses the Microsoft implementation (More information on [LGBM Repository](https://github.com/microsoft/LightGBM)) and `UniDL4BioPep` uses the PyTorch implementation (More information on [UniDL4BioPep PyTorch Repository](https://github.com/David-Dingle/UniDL4BioPep_ASL_PyTorch)), though for this model hyperparameter optimisation is not recommended.\n - `type`: Defines the type of hyperparameter. Options: `int`, `float`, or `categorical`. \n - `min` and `max`: Defines the lower and upper bounds of the search space for types `int` and `float`.\n - `log`: Boolean value that defines whether the search should be done in logarithmic space or not. Accelerates searches through vast spaces for example for learning rates (1e-7 to 1). It is not optional.\n - `value`: Defines the list of options available for a hyperparameter of type `categorical` for example types of kernel (`linear`, `rbf`, `sigmoid`) for a Support Vector Machine.\n\nThere is an example available in the [default configuration file](https://github.ibm.com/raulfd/AutoPeptideML/blob/main/autopeptideml/data/configs/default_config.json).\n\n</details>\n\n<details markdown=\"1\"><summary><b>4. API</summary></b><a name=\"api\"></a>\n\nPlease check the [Code reference documentation](https://ibm.github.io/AutoPeptideML/autopeptideml/)\n\n</details>\n\n\n## Examples <a name=\"examples\"></a>\n\n```bash\nautopeptideml dataset.csv\n```\n\n```bash\nautopeptideml dataset.csv --val_method graph-part --val_threshold 0.3 --val_alignment needle\nautopeptideml dataset.csv\n```\n\n```bash\nautopeptideml dataset.csv --val_method graph-part --val_threshold 0.3 --val_alignment needle\n```\n\nFor running predictions\n\n```bash\n\nautopeptideml-predict peptides.csv --ensemble AB_1/ensemble\n```\n\n\n\nLicense <a name=\"license\"></a>\n-------\nAutoPeptideML is an open-source software licensed under the MIT Clause License. Check the details in the [LICENSE](https://github.com/IBM/AutoPeptideML/blob/master/LICENSE) file.\n\nCredits <a name=\"acknowledgements\"></a>\n-------\n\nSpecial thanks to [Silvia Gonz\u00e1lez L\u00f3pez](https://www.linkedin.com/in/silvia-gonz%C3%A1lez-l%C3%B3pez-717558221/) for designing the AutoPeptideML logo and to [Marcos Mart\u00ednez Galindo](https://www.linkedin.com/in/marcosmartinezgalindo) for his aid in setting up the AutoPeptideML webserver.\n",
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