Checked fact 15327 Sep 2026Research

The final classifier uses a 143-dimensional feature vector: 87 features from pre-trained language-model embeddings and 56 from the RPM-PSSM representation.

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The exact words it rests on

By employing random forest-based feature selection, a 143-dimensional feature vector was obtained, comprising 87 pre-trained features and 56 RPM-PSSM features.

What the source said when we opened it, on 27 Sep 2026.

The source

PrePssmCas: fusing pre-trained features and PSSM features for Cas protein classification
BMC Bioinformatics · 2026-09-26

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Checked by the notis newsroom on 27 Sep 2026, against the source above.

In the story

Paper describes PrePssmCas, a classifier that fuses language-model and PSSM features for Cas proteins 27 Sep 2026

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“The final classifier uses a 143-dimensional feature vector: 87 features from pre-trained language-model embeddings and 56 from the RPM-PSSM representation.” ai notis, checked 27 Sep 2026. Source: PrePssmCas: fusing pre-trained features and PSSM features for Cas protein classification (BMC Bioinformatics). https://ainotis.com/c/153

As a reference (APA style)

ai notis. (2026, September 27). The final classifier uses a 143-dimensional feature vector: 87 features from pre-trained language-model embeddings and 56 from the RPM-PSSM representation [Checked claim 153]. ai notis. https://ainotis.com/c/153

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