Reliable interpretability of biology-inspired deep neural networks
© 2023. Springer Nature Limited..
Deep neural networks display impressive performance but suffer from limited interpretability. Biology-inspired deep learning, where the architecture of the computational graph is based on biological knowledge, enables unique interpretability where real-world concepts are encoded in hidden nodes, which can be ranked by importance and thereby interpreted. In such models trained on single-cell transcriptomes, we previously demonstrated that node-level interpretations lack robustness upon repeated training and are influenced by biases in biological knowledge. Similar studies are missing for related models. Here, we test and extend our methodology for reliable interpretability in P-NET, a biology-inspired model trained on patient mutation data. We observe variability of interpretations and susceptibility to knowledge biases, and identify the network properties that drive interpretation biases. We further present an approach to control the robustness and biases of interpretations, which leads to more specific interpretations. In summary, our study reveals the broad importance of methods to ensure robust and bias-aware interpretability in biology-inspired deep learning.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:9 |
|---|---|
| Enthalten in: |
NPJ systems biology and applications - 9(2023), 1 vom: 10. Okt., Seite 50 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Esser-Skala, Wolfgang [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
|---|
| Anmerkungen: |
Date Completed 01.11.2023 Date Revised 21.11.2023 published: Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1038/s41540-023-00310-8 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM363099093 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM363099093 | ||
| 003 | DE-627 | ||
| 005 | 20231226092629.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1038/s41540-023-00310-8 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1210.xml |
| 035 | |a (DE-627)NLM363099093 | ||
| 035 | |a (NLM)37816807 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Esser-Skala, Wolfgang |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Reliable interpretability of biology-inspired deep neural networks |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 01.11.2023 | ||
| 500 | |a Date Revised 21.11.2023 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023. Springer Nature Limited. | ||
| 520 | |a Deep neural networks display impressive performance but suffer from limited interpretability. Biology-inspired deep learning, where the architecture of the computational graph is based on biological knowledge, enables unique interpretability where real-world concepts are encoded in hidden nodes, which can be ranked by importance and thereby interpreted. In such models trained on single-cell transcriptomes, we previously demonstrated that node-level interpretations lack robustness upon repeated training and are influenced by biases in biological knowledge. Similar studies are missing for related models. Here, we test and extend our methodology for reliable interpretability in P-NET, a biology-inspired model trained on patient mutation data. We observe variability of interpretations and susceptibility to knowledge biases, and identify the network properties that drive interpretation biases. We further present an approach to control the robustness and biases of interpretations, which leads to more specific interpretations. In summary, our study reveals the broad importance of methods to ensure robust and bias-aware interpretability in biology-inspired deep learning | ||
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Fortelny, Nikolaus |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t NPJ systems biology and applications |d 2015 |g 9(2023), 1 vom: 10. Okt., Seite 50 |w (DE-627)NLM258627204 |x 2056-7189 |7 nnns |
| 773 | 1 | 8 | |g volume:9 |g year:2023 |g number:1 |g day:10 |g month:10 |g pages:50 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1038/s41540-023-00310-8 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 9 |j 2023 |e 1 |b 10 |c 10 |h 50 | ||