What is GeNet?

Microarray is a widely used technology in biomedical research. The proper analysis of microarray gene expression data is used in revealing underlying mechanisms of biological processes. To make this process easier, we present you GeNet, an easy to use python-based web application integrated with a machine learning framework and statistical methods. GeNet is designed carefully targeting a wide range of users. Bio informaticians, researchers, computer scientists, statistical analysts, etc can use the application without prior knowledge in other fields. It provides a hassle-free and interactive user experience that makes the analysis process much simpler and enjoyable. GeNet provides many analysis steps including different techniques for pre-processing, visualization, modeling, prediction, and validation of the results with a user-friendly GUI. Get started and explore more with GeNet.


Features

GeNet provides you with many features that make microarray gene expression analysis simple and hassle-free.


Hassle-free User Experience

GeNet is designed especially for the use of those with no or less programming knowledge or experience. It provides an easy to use graphical user interface that can be easily accessed and used by anyone.

Simple Tips and Guides along the way

Confused about how to use the application? GeNet got you covered! We provide you simple, easy to understand tips that would guide you through each step in the application.

Data Pre-processing

Microarray gene expression data is messy and noisy. GeNet offers you a number of methods to clean and organize your data. Remember it all depends on your data set! The cleaner the data the more productive your results will be.

Visualization

Visualize the behavior and patterns of your data with GeNet for better understanding. Rather than being overwhelmed by the huge number of numerical data why not try distilling it into visual graphics where you can easily grab the complex patterns and relationships.

Classification

Visualize the behavior and patterns of your data with GeNet for better understanding. Rather than being overwhelmed by the huge number of numerical data why not try distilling it into visual graphics where you can easily grab the complex patterns and relationships.

Selecting the best set of genes

Understanding microarray gene expression data can be exhausting due to its high dimensionality. It usually contains data about thousands of genes. How can you select the set of most impactful genes out of those? GeNet is there for the rescue! It provides you with efficient methods and allows you to extract the most reliable set of genes related to your data set.

Analyzing your results

Elaborate the results you get by comparing various models and using standard techniques. GeNet offers you the facility to see in-depth the operation of your process and results. It guides you through step by step detailed analysis of the methodology and results.

Validation

Making sure the results you got are meaningful is one of the main challenges in microarray gene expression analysis. GeNet offers you the opportunity to see through your results and compare them against already established biological data sources. It’s always better to have some validity than blindly obtaining results.

Modeling & Prediction

Modeling is the process of generating predictions based on the patterns extracted from your data. You can explore with new datasets and predict future results. This will easily allow you to classify a new data set.

Download Files and Images

GeNet provides you with the facility to download the result files and images in the most convenient formats. You can download, manipulate, and use them in external applications as you desire.

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See What Our Customer Are Saying?

GeNet team always strives to give the best experience for our customers. It is your feedback and suggestions that make us do better.


"GeNet is very easy to use. Recommend it to anyone without prior experience."

Shehan Shaman - Student

"I have struggled a lot with gene expression data preprocessing and feature selection in the past. GeNet made it so simple and I was amazed to see the accuracy of the outputs."

Kaveesha Dilshani - Student

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