Intersection of network medicine and machine learning towards investigating the key biomarkers and pathways underlying amyotrophic lateral sclerosis : a systematic review
© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissionsoup.com..
BACKGROUND: Network medicine is an emerging area of research that focuses on delving into the molecular complexity of the disease, leading to the discovery of network biomarkers and therapeutic target discovery. Amyotrophic lateral sclerosis (ALS) is a complicated rare disease with unknown pathogenesis and no available treatment. In ALS, network properties appear to be potential biomarkers that can be beneficial in disease-related applications when explored independently or in tandem with machine learning (ML) techniques.
OBJECTIVE: This systematic literature review explores recent trends in network medicine and implementations of network-based ML algorithms in ALS. We aim to provide an overview of the identified primary studies and gather details on identifying the potential biomarkers and delineated pathways.
METHODS: The current study consists of searching for and investigating primary studies from PubMed and Dimensions.ai, published between 2018 and 2022 that reported network medicine perspectives and the coupling of ML techniques. Each abstract and full-text study was individually evaluated, and the relevant studies were finally included in the review for discussion once they met the inclusion and exclusion criteria.
RESULTS: We identified 109 eligible publications from primary studies representing this systematic review. The data coalesced into two themes: application of network science to identify disease modules and promising biomarkers in ALS, along with network-based ML approaches. Conclusion This systematic review gives an overview of the network medicine approaches and implementations of network-based ML algorithms in ALS to determine new disease genes, and identify critical pathways and therapeutic target discovery for personalized treatment.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2022 |
|---|---|
| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:23 |
|---|---|
| Enthalten in: |
Briefings in bioinformatics - 23(2022), 6 vom: 19. Nov. |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Das, Trishala [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Amyotrophic lateral sclerosis |
|---|
| Anmerkungen: |
Date Completed 23.11.2022 Date Revised 12.12.2022 published: Print Citation Status MEDLINE |
|---|
| doi: |
10.1093/bib/bbac442 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM34924829X |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM34924829X | ||
| 003 | DE-627 | ||
| 005 | 20231226042151.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1093/bib/bbac442 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1164.xml |
| 035 | |a (DE-627)NLM34924829X | ||
| 035 | |a (NLM)36411673 | ||
| 035 | |a (PII)bbac442 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Das, Trishala |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Intersection of network medicine and machine learning towards investigating the key biomarkers and pathways underlying amyotrophic lateral sclerosis |b a systematic review |
| 264 | 1 | |c 2022 | |
| 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 23.11.2022 | ||
| 500 | |a Date Revised 12.12.2022 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissionsoup.com. | ||
| 520 | |a BACKGROUND: Network medicine is an emerging area of research that focuses on delving into the molecular complexity of the disease, leading to the discovery of network biomarkers and therapeutic target discovery. Amyotrophic lateral sclerosis (ALS) is a complicated rare disease with unknown pathogenesis and no available treatment. In ALS, network properties appear to be potential biomarkers that can be beneficial in disease-related applications when explored independently or in tandem with machine learning (ML) techniques | ||
| 520 | |a OBJECTIVE: This systematic literature review explores recent trends in network medicine and implementations of network-based ML algorithms in ALS. We aim to provide an overview of the identified primary studies and gather details on identifying the potential biomarkers and delineated pathways | ||
| 520 | |a METHODS: The current study consists of searching for and investigating primary studies from PubMed and Dimensions.ai, published between 2018 and 2022 that reported network medicine perspectives and the coupling of ML techniques. Each abstract and full-text study was individually evaluated, and the relevant studies were finally included in the review for discussion once they met the inclusion and exclusion criteria | ||
| 520 | |a RESULTS: We identified 109 eligible publications from primary studies representing this systematic review. The data coalesced into two themes: application of network science to identify disease modules and promising biomarkers in ALS, along with network-based ML approaches. Conclusion This systematic review gives an overview of the network medicine approaches and implementations of network-based ML algorithms in ALS to determine new disease genes, and identify critical pathways and therapeutic target discovery for personalized treatment | ||
| 650 | 4 | |a Systematic Review | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a amyotrophic lateral sclerosis | |
| 650 | 4 | |a biomarkers | |
| 650 | 4 | |a machine learning | |
| 650 | 4 | |a network biology | |
| 650 | 4 | |a neural networks | |
| 650 | 4 | |a therapeutics | |
| 650 | 7 | |a Biomarkers |2 NLM | |
| 700 | 1 | |a Kaur, Harbinder |e verfasserin |4 aut | |
| 700 | 1 | |a Gour, Pratibha |e verfasserin |4 aut | |
| 700 | 1 | |a Prasad, Kartikay |e verfasserin |4 aut | |
| 700 | 1 | |a Lynn, Andrew M |e verfasserin |4 aut | |
| 700 | 1 | |a Prakash, Amresh |e verfasserin |4 aut | |
| 700 | 1 | |a Kumar, Vijay |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Briefings in bioinformatics |d 2000 |g 23(2022), 6 vom: 19. Nov. |w (DE-627)NLM11366883X |x 1477-4054 |7 nnns |
| 773 | 1 | 8 | |g volume:23 |g year:2022 |g number:6 |g day:19 |g month:11 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1093/bib/bbac442 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 23 |j 2022 |e 6 |b 19 |c 11 | ||