Enter the matrix: factorization uncovers knowledge from omics Names/Affiliations
Abstract Omics data contains signal from the molecular, physical, and kinetic inter- and intra-cellular interactions that control biological systems. Matrix factorization techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in topics ranging from pathway discovery to time course analysis. We review exemplary applications of matrix factorization for systems-level analyses. We discuss appropriate application of these methods, their limitations, and focus on analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with matrix factorization enables discovery from high-throughput data beyond the limits of current biological knowledge—answering questions from high-dimensional data that we have not yet thought to ask..
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Preprint| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
bioRxiv.org - (2023) vom: 26. Aug. Zur Gesamtaufnahme - year:2023 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Stein-O’Brien, Genevieve L. [VerfasserIn] |
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| doi: |
10.1101/196915 |
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| PPN (Katalog-ID): |
XBI000184802 |
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| 245 | 1 | 0 | |a Enter the matrix: factorization uncovers knowledge from omics Names/Affiliations |
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| 520 | |a Abstract Omics data contains signal from the molecular, physical, and kinetic inter- and intra-cellular interactions that control biological systems. Matrix factorization techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in topics ranging from pathway discovery to time course analysis. We review exemplary applications of matrix factorization for systems-level analyses. We discuss appropriate application of these methods, their limitations, and focus on analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with matrix factorization enables discovery from high-throughput data beyond the limits of current biological knowledge—answering questions from high-dimensional data that we have not yet thought to ask. | ||
| 650 | 4 | |a Biology |7 (dpeaa)DE-84 | |
| 650 | 4 | |a 570 |7 (dpeaa)DE-84 | |
| 700 | 1 | |a Arora, Raman |4 aut | |
| 700 | 1 | |a Culhane, Aedin C. |0 (orcid)0000-0002-1395-9734 |4 aut | |
| 700 | 1 | |a Favorov, Alexander V. |0 (orcid)0000-0003-2595-1091 |4 aut | |
| 700 | 1 | |a Garmire, Lana X. |4 aut | |
| 700 | 1 | |a Greene, Casey S. |0 (orcid)0000-0001-8713-9213 |4 aut | |
| 700 | 1 | |a Goff, Loyal A. |0 (orcid)0000-0003-2875-451X |4 aut | |
| 700 | 1 | |a Li, Yifeng |0 (orcid)0000-0002-4873-6928 |4 aut | |
| 700 | 1 | |a Ngom, Aloune |0 (orcid)0000-0003-2092-2494 |4 aut | |
| 700 | 1 | |a Ochs, Michael F. |4 aut | |
| 700 | 1 | |a Xu, Yanxun |4 aut | |
| 700 | 1 | |a Fertig, Elana J. |0 (orcid)0000-0003-3204-342X |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t bioRxiv.org |g (2023) vom: 26. Aug. |
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