Bert_protein_classifier Open-source Model - Free Deployment, Predict Protein Functions Based on Amino Acid Sequences

Bert Protein Classifier

Developed by oohtmeel

This model is fine-tuned based on Bert-Base-Uncased and is used for the multi-label classification task of predicting protein functions based on protein amino acid sequences.

Protein Model

Transformers

#Protein sequence analysis #Multi-label classification #GO term prediction

Downloads 1,772

Release Time : 6/19/2024

Model Overview

This is a multi-label classification model specifically designed for protein function prediction, which can predict multiple possible functions (GO terms) of a protein based on the input amino acid sequence.

Model Features

Protein sequence analysis

Can directly process protein amino acid sequences without complex feature engineering

Multi-label prediction

Can simultaneously predict multiple possible functions (GO terms) of a protein

Based on Transformer architecture

Utilizes the powerful representation ability of the Bert model to capture complex patterns in protein sequences

Model Capabilities

Protein function prediction

Multi-label classification

Biological sequence analysis

Use Cases

Bioinformatics

Protein function annotation

Predict possible functional categories for newly discovered protein sequences

Output probability scores associated with Gene Ontology (GO) terms

Proteomics research

Function prediction in large-scale proteomic data analysis

Can process a large number of protein sequences in batches to improve research efficiency

Drug discovery

Target protein function analysis

Analyze the functional characteristics of potential drug target proteins

Help researchers understand the biological functions of target proteins

🚀 Finetuned Bert-Base-Uncased Protein Classifier

A finetuned Bert-Base-Uncased model for predicting protein functions based on amino acid sequences.

🚀 Quick Start

This is a finetuned Bert-Base-Uncased model designed for a multilabel classification task. It predicts protein functions by taking amino acid sequences as input and outputs probability scores indicating how likely a sequence belongs to a certain protein class.

✨ Features

Input: The model takes sequence data and protein class names as input.
Output: It outputs probability scores, showing the likelihood of a sequence belonging to a specific group.

💻 Usage Examples

Basic Usage

The way to use this model for a demo is to paste a protein sequence into the inference box. The model will then output the relevant probabilities that certain GO terms are associated with that sequence.

For example, when you input the following protein sequence:

MMSTTHLLVFLLGVVTLTTPTFGTYESPNYGKPPTPVFKPPKVKPPPYEPKPPVYEPPKKEKPEPKPPVYAPPKKEKHGPKPTMYEPPKKEKPEPKPPVYTPPKKEVPKPKPPVYEPPKKEKPEPKPPIYTPPKKEKPEPKPPVYEPPKKEKPEPKPPVYTPPKKEKPEPKPPVYEPPKKPPMYEPKPPKPPVYTPPKKEKPEPKPPMYEPPKKPPMYEPKPPKPPVYTPPKKEKPEPKPPMYQPPNNPPIYEPKPPKPPVYAPPKEEKPKPKPPVYEPPAHEPPYGHYPGHPPLGKPQ

It outputs the following score:

[
  [
    {
      "label": "GO:0000122",
      "score": 0.29775485396385193
    },
    {
      "label": "GO:0000070",
      "score": 0.10477513074874878
    },
    {
      "label": "GO:0000075",
      "score": 0.08593793958425522
    },
    {
      "label": "GO:0000118",
      "score": 0.05860009789466858
    },
    {
      "label": "GO:0000082",
      "score": 0.05373986065387726
    },
    {
      "label": "GO:0000077",
      "score": 0.03928716108202934
    },
    {
      "label": "GO:0000096",
      "score": 0.03705739229917526
    },
    {
      "label": "GO:0000079",
      "score": 0.02797592058777809
    },
    {
      "label": "GO:0000045",
      "score": 0.026528609916567802
    },
    {
      "label": "GO:0000097",
      "score": 0.026119187474250793
    },
    {
      "label": "GO:0000086",
      "score": 0.019697198644280434
    },
    {
      "label": "GO:0000049",
      "score": 0.018551582470536232
    },
    {
      "label": "GO:0000041",
      "score": 0.016929756850004196
    },
    {
      "label": "GO:0000054",
      "score": 0.015105823054909706
    },
    {
      "label": "GO:0000083",
      "score": 0.01434631273150444
    },
    {
      "label": "GO:0000105",
      "score": 0.013960960321128368
    },
    {
      "label": "GO:0000076",
      "score": 0.013064960949122906
    },
    {
      "label": "GO:0000109",
      "score": 0.012523632496595383
    },
    {
      "label": "GO:0000113",
      "score": 0.012152223847806454
    },
    {
      "label": "GO:0000062",
      "score": 0.01127714291214943
    },
    {
      "label": "GO:0000101",
      "score": 0.011041304096579552
    }
  ]
]

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