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Validation of the Predictive Capability of the PLSR Prognostic Model

Journal: Cancer Cell International

Article Title: Amino acid-based risk stratification model improves prognostic precision in diffuse large B-cell lymphoma

doi: 10.1186/s12935-025-03879-8

Figure Lengend Snippet: Validation of the Predictive Capability of the PLSR Prognostic Model

Article Snippet: A PLSR prognostic model was established via MATLAB, with repeated training and validation steps, and the prognostic expression for PFS is derived as follows: PFS = 110.2092 + (-5.5576) * Gln + (-6.7974) * Trp + 0.1395 * Age + 3.1042 * Number of extranodal sites + 0.0047 * Number of LDH + (-7.2143) * Number of IPI risk stratifications + (-2.8840) * IPI score.

Techniques: Biomarker Discovery

Validation of the Predictive Capability of the PLSR Prognostic Model

Journal: Cancer Cell International

Article Title: Amino acid-based risk stratification model improves prognostic precision in diffuse large B-cell lymphoma

doi: 10.1186/s12935-025-03879-8

Figure Lengend Snippet: Validation of the Predictive Capability of the PLSR Prognostic Model

Article Snippet: MATLAB was used to create a partial least squares regression (PLSR) prognostic model and a support vector regression (SVR) machine learning prognostic model.

Techniques: Biomarker Discovery