Alzheimers is a chronic neurodegenerative disease that usually worsens over time, progressively destroys memory and other important mental functions. Number of people suffered for Alzheimers is increased over these years. Treatment may help but this condition has no cure. Facing this alarming problem, an implementation of Factor Analysis for Bicluster Acquisition : Sparseness Projection (FABIAS) is studied to get biclusters from Alzheimers gene expression data. These biclusters might have some valuable information to help improvement of researches in Alzheimers. The data that is used in this research included 54675 Alzheimers gene expression data from 161 samples with 74 control samples and 87 affected samples. This research is focused on implementation of FABIAS as one of well-known method for biclustering. From the results of this study, there are ten biclusters obtained from the Alzheimers gene expression data, with three true biclusters.

Topics
Microarrays, Molecular genetics
1.
Alzheimers Association
.,
Data about Alzheimer and Dementia in 2018
, https://www.alz.org/alzheimers-dementia/facts-figures
2.
Ardaneswari
,
G.
,
Bustamam
,
A.
, and
Sarwinda
,
D.
,
Implementation of plaid model biclustering method on microarray of carcinoma and adenoma tumor gene expression data
,
The Asian Mathematical Conference 2016
,
893
(
1
),
012046
,
2017
.
Google Scholar
 
3.
Ardaneswari
,
G.
,
Bustamam
,
A.
, and
Siswantining
,
T.
,
Implementation of parallel k-means algorithm for two-phase method biclustering in Carcinoma tumor gene expression data
,
J. Phys. Conf. Ser.
, vol.
893
, no.
1
,
2017
.
Google Scholar
 
4.
Bustamam
,
A.
,
Zubedi
,
F.
,
Siswantining
,
T.
,
Implementation χ-sim co-similarity and agglomerative hierarchical to cluster gene expression data of lymphoma by gene and condition
,
American Institute of Physics doi
:
https://doi.org/10.1063/1.5064218
,
2018
.
Google Scholar
 
5.
Bustamam
,
A.
,
Formalidin
,
S.
,
Siswantining
,
T.
,
Clustering and analyzing microarray data of lymphoma using singular value decomposition (SVD) and hybrid clustering
,
American Institute of Physics
, doi:
https://doi.org/10.1063/1.5064217
,
2018
.
Google Scholar
 
6.
Bustamam
,
A.
,
Sarwinda
,
D.
,
Ardaneswari
,
G.
,
Texture and Gene Expression Analysis of The MRI Brain in Detection of Alzheimer’s Disease
,
Journal of Artificial Intelligence and Soft Computing Research
 
8
(
2
),
111
–
120
,
2018
.
https://doi.org/10.1515/jaiscr-2018-0008
Google Scholar
Crossref
Search ADS
 
7.
Cheng
,
Y.
,
Church
,
G.
,
Biclustering of expression data.
,
Proc. Eighth Int. Conf. On Intelligent Systems for Molecular Biology (ISMB00)
pp.
93
–
103
,
2000
.
Google Scholar
 
8.
Dahiru
,
T.
,
P Value, A True Test of Statistical Significance? A Cautionary Note.
,
Annals of Ibadan Postgraduate Medicine.
Vol.
6
. No.
1
June,
2008
.
Google Scholar
 
9.
Denitto
,
M.
,
Bicego
,
M.
,
Farinelli
,
A.
,
Figueiredo
,
M. A.
,
Reordering based integrative expression profiling for microarray classification
,
Spike and Slab Biclustering. Pattern Recognition
,
2017
.
Google Scholar
 
10.
Gao
,
C.
 et al.,
Context Spesific and Differential Gene Co-expression Networks via Bayesian Biclustering.
,
PloS Computational Biology
,
12
(
7
):
e1004791
,
2016
.
https://doi.org/10.1371/journal.pcbi.1004791
Google Scholar
Crossref
Search ADS
PubMed
 
11.
Girolami
,
M.
 et al.,
A variational method for learning sparse and overcomplete representations.
,
Neural Comput.
,
13
(
11
),
25172532
,
2001
.
https://doi.org/10.1162/089976601753196003
Google Scholar
Crossref
Search ADS
 
12.
Hochreiter
,
S.
 et al.,
FABIA : factor analysis for bicluster acquisition.
,
Bioinformatics
,
26
,
1520
–
1527
,
2010
.
https://doi.org/10.1093/bioinformatics/btq227
Google Scholar
Crossref
Search ADS
PubMed
 
13.
National Center for Biotechnology Information
.
Alzheimer’s disease and the normal aged brain (GSE5281)
, [Data File]. Retrieved from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5281
14.
Padilha
,
V. A.
,
Campello
,
R.
 
A systematic comparative evaluation of biclustering techniques.
,
BMC Bioin-formatics.
,
2017
.
Google Scholar
 
15.
Palmer
,
J.
 et al.,
Variational EM algorithms for non-Gaussian latent variable models.
, In
Advances in Neural Information Processing Systems
 
18
, pages
10591066
.
16.
Prelic
,
A.
 et al.,
A systematic comparison and evaluation of biclustering methods for gene expression data.
,
Bioinformatics
,
22
(
9
),
11221129
,
2006
.
https://doi.org/10.1093/bioinformatics/btl060
Google Scholar
Crossref
Search ADS
 
This content is only available via PDF.
© 2019 Author(s).
2019
Author(s)
You do not currently have access to this content.