| Title |
A three-layered framework integrating classical, multivariate, and machine-learning methods for systemic treatment effect detection in high-dimensional biomarker data |
| Authors |
Ošmianskienė, Monika |
| DOI |
10.15388/LMITT.2026.21 |
| Full Text |
|
| Is Part of |
Lietuvos magistrantų informatikos ir IT tyrimai: konferencijos darbai, 2026 m. gegužės 6 d. Vilnius.. Vilnius : Vilniaus universiteto leidykla. 2026, p. 208-213.. eISSN 2783-784X |
| Keywords [eng] |
biomarker analysis ; framework ; systems biology ; machine learning ; PCA |
| Abstract [eng] |
Longevity supplement trials often rely on single-biomarker tests, which can miss distributed systemic effects. This paper proposes a three-layer analytical framework: (i) classical biostatistics, (ii) multivariate systems biology, and (iii) machine learning with responder analysis. Applied to a 99-participant trial with 20 biomarkers, Layer 1 found few isolated effects, Layer 2 detected significant multivariate separation, and Layer 3 supported reliable directional effects for nicotinamide adenine dinucleotide (NAD+) and low-density lipoprotein cholesterol (LDL-C). Together, the layers provide complementary evidence beyond any single method. |
| Published |
Vilnius : Vilniaus universiteto leidykla |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2026 |
| CC license |
|
