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  • Geting Start
  • Algorithm
  • Online analysis
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  • Source code
FAQ
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A prediction model of HLA class I antigen presentation based on ensemble learning framework

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More Accurate Datasets

The training set for DLpMHCI is made up of very precise single allele mass spectrometry data rather than conventional binding affinity data. The single allele mass spectrometry data may precisely depict the peptide binding to a particular allele as well as the mechanism of peptide processing in vivo. It benefits from low false positive rates and high rates of accuracy.

Biochemically Comprehensible Coding

To better reflect the biological features of the sequence and improve biological interpretability, DLpMHCI adopts amino acid physicochemical characteristics coding as opposed to the conventional BLOSUM coding.

An Ensemble Learning Framework Based Model

In order to extract features, DLpMHCI incorporates self-attentional mechanisms and residual networks. It then classifies characteristics using a complete connection layer and determines if the output peptide is provided by HLA. We demonstrate that the integrated learning framework improves the model's prediction performance.

More Accurate Datasets

The training set for DLpMHCI is made up of very precise single allele mass spectrometry data rather than conventional binding affinity data. The single allele mass spectrometry data may precisely depict the peptide binding to a particular allele as well as the mechanism of peptide processing in vivo. It benefits from low false positive rates and high rates of accuracy.

Biochemically Comprehensible Coding

To better reflect the biological features of the sequence and improve biological interpretability, DLpMHCI adopts amino acid physicochemical characteristics coding as opposed to the conventional BLOSUM coding.

An Ensemble Learning Framework Based Model

In order to extract features, DLpMHCI incorporates self-attentional mechanisms and residual networks. It then classifies characteristics using a complete connection layer and determines if the output peptide is provided by HLA. We demonstrate that the integrated learning framework improves the model's prediction performance.

Jiang Lab


# Citation

OpenHarmonyOpenHarmonyOpenHarmonyOpenHarmonyOpenHarmony.

xxxxxx,submitted

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#   desc: 开放原子开源基金会
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- name: OpenHarmonyOpenHarmonyOpenHarmonyOpenHarmonyOpenHarmony. 
  desc: xxxxxx,submitted
  link: https://www.iqiyi.com/v_10331zk5kcg.html?vfm=2008_aldbd&fv=p_02_01
  bgColor: '#f1f1f1'
  textColor: '#2A3344'

# Update

  • v0.9 2022.08.27:The attention mechanism display is online, you can Click here (opens new window) to experience
  • v0.8.2 2022.05.22:Basic functions are online.Click here (opens new window) to access

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