Big data analytics in bioinformatics

Alexander (Gulliver Bjørnholt) Gronning

doi:10.26481/dis.20200828ag

Big data analytics in bioinformatics

Alexander (Gulliver Bjørnholt) Gronning

Research output: Thesis › Doctoral Thesis › Internal

620 Downloads (Pure)

Abstract

This thesis presents new bioinformatic solutions that efficiently can analyze and investigate big data sets of various types. Three manuscripts are included in the thesis. The first manuscript shows how unsupervised learning techniques can extract the mechanistic patterns that underlie cellular development trajectories. In the second manuscript, a customized neural network is used to aid the development of treatments for diseases induced by aberrant RNA splicing. In the third manuscript, a computer simulation is used to discover certain molecular mechanisms that can help establish therapies for stroke-induced brain and tissue damages. Altogether, this thesis shows how “big data analytics in bioinformatics” can be approached in current research strategies

Original language	English
Awarding Institution	Maastricht University
Supervisors/Advisors	Baumbach, Jan, Supervisor, External person Schmidt, Harald, Supervisor Röttger, Richard, Co-Supervisor, External person
Award date	28 Aug 2020
Place of Publication	Maastricht
Publisher	Maastricht University
DOIs	https://doi.org/10.26481/dis.20200828ag
Publication status	Published - 2020

Keywords

bio-informatica
big data
machine learning
clustering
diepgaand leren
therapieontwikkeling

Access to Document

10.26481/dis.20200828ag

Full TextFinal published version, 16.9 MB
AbstractFinal published version, 95.5 KB
PropositionsFinal published version, 76.2 KB
CoverFinal published version, 18 KB
ValorisationFinal published version, 101 KB

Embargoed Document

Full Text
Final published version, 16.8 MB
Embargo ends: 28/08/25
Request copy

Cite this

@phdthesis{fa67c4576c2948ec8135a434570d82c3,

title = "Big data analytics in bioinformatics",

abstract = "This thesis presents new bioinformatic solutions that efficiently can analyze and investigate big data sets of various types. Three manuscripts are included in the thesis. The first manuscript shows how unsupervised learning techniques can extract the mechanistic patterns that underlie cellular development trajectories. In the second manuscript, a customized neural network is used to aid the development of treatments for diseases induced by aberrant RNA splicing. In the third manuscript, a computer simulation is used to discover certain molecular mechanisms that can help establish therapies for stroke-induced brain and tissue damages. Altogether, this thesis shows how “big data analytics in bioinformatics” can be approached in current research strategies",

keywords = "bio-informatica, big data, machine learning, clustering, diepgaand leren, therapieontwikkeling",

author = "Gronning, {Alexander (Gulliver Bj{\o}rnholt)}",

note = "Double doctoral degree UM-University of Southern Denmark",

year = "2020",

doi = "10.26481/dis.20200828ag",

language = "English",

publisher = "Maastricht University",

address = "Netherlands",

school = "Maastricht University",

}

TY - BOOK

T1 - Big data analytics in bioinformatics

AU - Gronning, Alexander (Gulliver Bjørnholt)

N1 - Double doctoral degree UM-University of Southern Denmark

PY - 2020

Y1 - 2020

N2 - This thesis presents new bioinformatic solutions that efficiently can analyze and investigate big data sets of various types. Three manuscripts are included in the thesis. The first manuscript shows how unsupervised learning techniques can extract the mechanistic patterns that underlie cellular development trajectories. In the second manuscript, a customized neural network is used to aid the development of treatments for diseases induced by aberrant RNA splicing. In the third manuscript, a computer simulation is used to discover certain molecular mechanisms that can help establish therapies for stroke-induced brain and tissue damages. Altogether, this thesis shows how “big data analytics in bioinformatics” can be approached in current research strategies

AB - This thesis presents new bioinformatic solutions that efficiently can analyze and investigate big data sets of various types. Three manuscripts are included in the thesis. The first manuscript shows how unsupervised learning techniques can extract the mechanistic patterns that underlie cellular development trajectories. In the second manuscript, a customized neural network is used to aid the development of treatments for diseases induced by aberrant RNA splicing. In the third manuscript, a computer simulation is used to discover certain molecular mechanisms that can help establish therapies for stroke-induced brain and tissue damages. Altogether, this thesis shows how “big data analytics in bioinformatics” can be approached in current research strategies

KW - bio-informatica

KW - big data

KW - machine learning

KW - clustering

KW - diepgaand leren

KW - therapieontwikkeling

U2 - 10.26481/dis.20200828ag

DO - 10.26481/dis.20200828ag

M3 - Doctoral Thesis

PB - Maastricht University

CY - Maastricht

ER -

Big data analytics in bioinformatics

Abstract

Keywords

Access to Document

Embargoed Document

Fingerprint

Cite this