semvec/pypi-packages huggingface dataset:
eugene-tools 0.1.2
Elucidating the Utility of Genomic Elements with Neural Nets
Requires Python: >=3.9,<3.11

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<img src="docs/_static/logos/eugene_logo.png" alt="EUGENe Logo" width=350>

# **E**lucidating the **U**tility of **G**enomic **E**lements with **Ne**ural Nets

EUGENe is a Python toolkit for building and evaluating sequence-based deep learning models in genomics. It provides a unified workflow for managing data, training models, and interpreting predictions on biological sequences.

You can find the [current documentation](https://eugene-tools.readthedocs.io/en/latest/index.html) here for getting started.

If you use EUGENe for your research, please cite our preprint: [Klie *et al.* bioRxiv 2022](https://www.biorxiv.org/content/10.1101/2022.10.24.513593v1)
seqexplainer 0.1.2
Interpreting sequence-to-function machine learning models
Requires Python: >=3.9,<3.11

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# SeqExplainer -- Interpreting sequence-to-function machine learning models

A huge goal of applying machine learniing to genomics data is to [obtain novel geneetic insights [1]](https://www.nature.com/articles/s41576-022-00532-2). This can be challenging when models are complex (such as with neural networks). There are [many interpretability methods](https://github.com/ML4GLand/awesome-dl4g/blob/main/README.md#interpretability) specifically designed for such complex sequence-to-function preditctors, but they can be difficult to use and often are not interoperable.

The goal of SeqExplainer is to bring all these methods under one roof. We have designed a workflow that can take in any PyTorch model trained to predict labels from DNA sequence and expose it to many of the most popular explainability methods available in the field. We also offer some wrappers for explaining "shallow" sklearn models.

## What is the scope of SeqExplainer?

Most of the core functionality is for post-hoc analysis of a trained model. 

## Common workflows

### Feature attribution analysis (coming soon)

### Identifying motifs in attributions (coming soon)

### Testing feature dependencies (coming soon)

## Tutorials

###  Extracting motif syntax rules from a CNN (coming soon)

### Explaining predictions for shallow models on synthetic MPRAs using SHAP (coming soon)

## Requirements

The main dependencies of SeqExplainer are:

```bash
python
torch
captum
numpy
matplotlib
logomaker
sklearn
shap
```

## References
1. Novakovsky, G., Dexter, N., Libbrecht, M. W., Wasserman, W. W. & Mostafavi, S. Obtaining genetics insights from deep learning via explainable artificial intelligence. Nat. Rev. Genet. 1–13 (2022) doi:10.1038/s41576-022-00532-2
motifdata 0.1.2
Motif representation and analysis toolkit
Requires Python: >=3.9,<3.11

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<img src="docs/_static/MotifData_logo.png" alt="MotifData Logo" width=350>

# MotifData - Motif analysis toolkit in Python

```python
import motifdata as motif
```

### Motif analysis with [MotifData](https://github.com/ML4GLand/MotifData)

  - Perform HOMER motif analysis
  - Perform MEME motif analysis
  - Perform DEM (Cluster-buster based) motif analysis
  - Perform cisTarget
syncticketstoclickup 1.0.3
A package that allows to sync tickets from Zendesk to ClickUp.
Keywords: python,zendesk,clickup,ticket,task,sync,requests

# zendesk-clickup
Project that syncs all tickets from Zendesk to ClickUp. This is useful for teams that use them for managing customer support tickets and want to keep the ticket numbers consistent across both platforms.

## Installation
This module is available on PyPI and can be installed using pip.
```bash
$ python -m pip install syncticketstoclickup
```
This module requires Python 3.10 or higher.

## Configuration
1. Duplicate the `.env.example` file and rename it to `.env`.
2. Replace the placeholder values in the `.env` file with your actual configuration.

## Usage
This is the basic usage of the module to sync all tickets from Zendesk to ClickUp.
```python
from syncticketstoclickup.zendesk_to_clickup import ZendeskToClickUp

ZendeskToClickUp().sync_zendesk_to_clickup()
```
imgopt 0.3
Compress & Optimize Images



# ImgOpt - Compress & Optimize Images

ImgOpt is a simple and very easy to use package to Compress and Optimize images.



### Features:
+ Easy to Use
+ Standalone Package
+ Image Compression Options
+ No Need to use External APIs
+ No Need for Any API Key

### Installation
**Pip Command**

```
pip install imgopt
```

### How to use?

**Import imgopt Package**

```
import imgopt
```

### InBuilt Functions:
#### OptmizeImage Fuction:
This function will compress and optimize the input image and return the output as an image. You can select your desired filename in the second parameter.

```
imgopt.OptmizeImage(image_path,output_path_with_extension)
```

```
imgopt.OptmizeImage('pro.jpg','vinay.jpg')
```
If you don't pass any output filename, it will automatically generate a random filename with an extension.
```
imgopt.OptmizeImage('pro.jpg')
```

#### DynamicCompress Fuction:
This function will compress and optimize the input image. It requires one more parameter, representing the percentage of compression. The function will return the output as an image. You can select your desired filename in the second parameter. All three parameters are mandatory when using this function.
```
imgopt.OptmizeImage(image_path,output_path_with_extension,compress_percent)
```
The value of Compress Percent must be between 10 and 100 (Type: int). A lower percentage will reduce the file size more.

```
imgopt.OptmizeImage('pro.jpg','vinay.jpg',75)
```
**Note:** Default Compress Percent is set to 80




---

### Code Examples
```
import imgopt

imgopt.OptmizeImage('pro.jpg')

imgopt.DynamicCompress('pro.jpg', 'hello.jpg',20)
```

### Support This Project
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### Author
The ImgOpt Package is created by [Vinay Chaudhary](https://buymeacoffee.com/vinay_dev). This Project is Powered by [Regem Enterprises](https://regem.in)