Exploration of Potential Drug Targets for Parkinson’s via Text

Mining and Data Analysis

Zihao Yang

1,# a

and Sixian Wang

2,* b

East China University of Political Science and Law,Shanghai 200042, China

Department of Transfusion, Minhang Hospital, Fudan University, Shanghai 201199, China

*Corresponding Author: Sixian Wang, Email: 2021110083@ecupl.edu.cn

Keywords: Text Mining, Differentially Expressed Genes (Degs), Genetic Diagnosis, Drug Discovery.

Abstract: Parkinson's disease (PD) is a chronic neurodegenerative disease of the central nerve system around the world.

However, the current therapeutic regimens were not always effective. We found gene targets of existing drug

and give indications of the potential value of new drugs by text mining and microarray data analysis. We

firstly used text mining (“Parkinson's disease” and “parkinson”) and microarray data analysis (GSE22491) to

screen the genes that we want. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes

(KEGG) analysis, as well as the protein-protein interaction (PPI) network were used to analysis the genes.

Gene-drug interaction analysis was finally applied to the significant genes to provide insight into potential

drug. As a result, we got 1,116 text mining genes (TMGs) and 4,437 differentially expressed genes (DEGs)

through text mining and microarray data analysis. 258 genes were up-regulated genes and 31 genes were

down regulated among the genes overlapped between TMGs and DEGs. There are six genes are significantand

target 16 existing drugs. In summary, in this study, these six genes (Bax, Apaf-1, BCL2L11, Bcl-2, BCL2L1

and CYCS), associated with apoptosis, are the targets of 16 existing drugs. The finding may shed light on the

indication of the drugs indications to Parkinson's disease.

1 INTRODUCTION

Parkinson's disease (PD) is a chronic

neurodegenerative disease that affects the central

nerve system and affects more than 6 million people

worldwide. Among them, PD typically occurs in

people over 60-year-old, with about 1% of the older

suffering from the disease. Furthermore, males are

more likely to suffer from PD than females (Scholpa

et al. 2018). PD is less common in young adults under

the age of 40, most cases are sporadic, and only

around 10% are familial. Degeneration of dopamine

(DA) neurons in the substantia nigra are one of the

most common pathological features of PD, and the

precise etiology of this pathological change remains

unknown. Degeneration of dopamine neurons may be

caused by genetic factors, aging, environmental

factors, oxidative stress, and possibly other elements

(Chen et al. 2017). Due to the slow progression of PD,

the most obvious symptoms in the early phases are

https://orcid.org/0000-0002-0351-3501

https://orcid.org/0000-0002-0777-3204

tremor, rigidity, bradykinesia and postural instability,

and there may also be cognitive and behavioral

problems. Although some treatments including

medication, surgery and physical therapy have been

used to relieve symptoms, such as dopamine receptor

agonists and monoamine oxidase inhibitors to

improve motor function, there is still no effective

treatment for PD (Ganguly et al. 2018). Thus, there is

an urgent need to discover new therapeutic drugs and

effective strategies to effectively prevent the

progression of PD to improve the therapeutic effect.

In short, drugs that may be applied to prevent and

treat PD can be obtained from text mining and data

analysis strategies, providing new ideas for drug

research and development and new applications.

In this study, we firstly used bioinformatics tools

such as text mining and microarray data analysis to

obtain common and unique genes. Significant

differences between PD patients and control groups

were depicted, while correlations between these

Yang, Z. and Wang, S.

Exploration of Potential Drug Targets for Parkinson’s via Text Mining and Data Analysis.

DOI: 10.5220/0011231400003438

In Proceedings of the 1st International Conference on Health Big Data and Intelligent Healthcare (ICHIH 2022), pages 83-91

ISBN: 978-989-758-596-8

genes existed. Secondly, these genes were screened

for module genes and associated pathways using

protein-protein interaction (PPI) network analysis.

Finally, drug-gene interaction of module genes was

performed in the drug gene interaction database

(DGIdb), with the goal of discovering some current

drugs to provide new ideas for the prevention and

treatment of Parkinson's disease. Figure 1 depicts the

framework of this study.

Figure 1: An overview of the workflow. Text mining for TMGs, Microarray data analysis for DEGs.

2 METHODS

2.1 Text Mining Analysis

We used GENCLIP3

(http://ci.smu.edu.cn/genclip3/analysis.php) to

perform text mining. We entered the keyword, and

the GENCLIP3 website can retrieve and extract all

gene markers associated with the keyword from

PubMed published articles (Wang et al. 2019). We

entered the keyword "Parkinson's disease" and

"parkinson" into GENCLIP3 and then extracted all

non-repeat genes, and these gene sets formed the Text

Mining Genes (TMGs).

2.2 Microarray Data Analysis

The microarray datasets of PD patients were searched

from the publicly available GEO database (gene

expression omnibus dataset). We screened the

literature against inclusion and exclusion criteria and

cross-checked it. Inclusion criteria: the approval of

the Ethics Committee was indicated within the

research; diagnosis of PD by clinical and

neuropathology; raw microarray gene data can be

obtained; raw GeneChip data had high quality.

Exclusion criteria: the approval of the Ethics

Committee was not indicated within the research;

diagnosis of PD was not demonstrated by clinic and

neuropathology; raw microarray gene data cannot be

obtained; raw GeneChip data had poor quality.

Following selection, GSE22491 files were obtained

and downloaded.

GSE22491 expression files (.txt format files) and

meta clinical information data (.soft format file)

downloaded from GEO website, which was executed

on the GPL6480 platform (Ron Edgar 2002, Barrett

et al. 2009). The GSE22491 dataset comprises 18

blood samples from 10 Parkinson's disease (PD)

patients and 8 healthy controls (Control) (Barrett et

ICHIH 2022 - International Conference on Health Big Data and Intelligent Healthcare

al. 2007, Mutez et al. 2011). The probe identification

numbers were converted into official gene symbols

according to the annotation information of the

GPL6480 platform. Afterwards, we counted

duplicate genes, retained mRNA probes, deleted non-

mRNA probes, and retained probes showing

significant gene expression values for multiple probes

of the same gene. Finally,

we followed previous methods, and through

manipulated the R language limma package to detect

gene expression matrix, processed by Affy, AffyPLM

packages, and obtain differentially expressed genes

(DEGs) in Parkinson's disease samples and normal

controls (Gautier et al. 2004, Larriba et al. 2019). As

the threshold standards were utilized for the follow-

up research, DEGs with the |log2 fold change (FC)|

≥1 and adjust P value <0.01, corrected by the

Benjamini–Hochberg (BH) method (Wan et al.

2020). Crossing of DEGs and TMGs was then used

as a starting point for further analysis.

2.3 Gene Ontology (GO) and KEGG

Analysis of Overlapping genes

We adopted a research path similar to Zhao B et al.,

and briefly describe as follows (Zhao et al. 2020).

Gene ontology (GO) is a common and useful note

approach for annotating their functional features and

gene products. Then, the GO terms were divided into

three categories: biological process (BP), cellular

component (CC), and molecular function (MF). The

Kyoto Encyclopedia of Genes and Genomes (KEGG)

is an open database resource for discovering

biological functions and features of organic systems,

especially in the datasets of gene chips and high-

throughput experiments. (Kanehisa et al. 2017).

Overlapping genes were analyzed by DAVID, a

functional note bioinformatics microarray analysis

website. Significance was assumed for P < 0.05.

2.4 Potential Protein-protein

Interaction (PPI) Network

Construction for Overlapping

Genes

Potential protein-protein interaction of selected genes

was generated using the STRING database

(http://string-db.org) (Szklarczyk et al. 2019). As in a

previous study by Wan Z et al., we also apply a

similar principle (Wan et al. 2020). In this study, we

used the STRING to construct the PPI network of

overlapping genes, with a combined score > 0.4

considered statistically significant. We downloaded

the TSV format file of protein-protein interaction

(PPI), and PPI networks were created by Cytoscape

software. Important gene modules (clusters) were

classified by Molecular Complex Detection

(MCODE) and STRING appin Cytoscape. These

important gene modules are highly interconnected.

Execute MCODE with default parameters. Drug-gene

interaction analysis was applied to the genes in the

gene module.

2.5 Drug-gene Interaction and

Function Analysis of Potential

Genes

To explore the possible application of new drug

indications for the treatment of Parkinson's disease in

humans, the drug gene interaction database (DGIdb)

was handled to search for interactions between

selected genes and existing drugs. The DGIdb

database (http://www.dgidb.org) is an open access

information website, which contains 41,102 genes,

14,449 drugs and 54,591 drug-gene interactions

(Freshour et al. 2021). In the present study, we used

the DGIdb database to search and filter information

on the interactions between selected genes and

existing drugs, screen potential matches with these

drugs, and carry out functional enrichment analysis

(Zhao et al. 2020).

2.6 Statistics Analysis

According to the experience of many academic

circles(Kirk et al. 2018, Pan et al. 2018, Zhang et al.

2019, Wan et al. 2020, Zhao et al. 2020, Zhao et al.

2020), we used a moderated t-test to identify DEGs.

We used fisher’s Exact Test to analyzed GO and

KEGG annotation enrichments (Fisher 1922). All

statistical analysis was performed with the R version

3.5.3 software.

3 RESULTS

3.1 Screening of TMGs and DEGs

As shown in Figure 1, We got 1,116 TMGs and 4,437

DEGs through text mining and microarray data

analysis. As shown in Table I, there were 289

overlapping genes between TMGs and DEGs, and

258 genes were up-regulated and 31 genes were

down-regulated.

Exploration of Potential Drug Targets for Parkinson’s via Text Mining and Data Analysis

Table 1: The 289 overlapped genes between TMGs and DEGs.

Overlapped

enes

Gene Symbol

Up-regulated

genes

DYRK1A,PPP3CA,SKP1,EP300,TAF9,DLD,HBB,*CYCS,UBE2K,YWHAE,NDUFS1,HMGB1,HPRT1,GLUL,A

IFM1,SMS,CLINT1,TCP1,HSPA9,MTHFR,REL,CARD8,OPA1,GLO1,FBXO7,MTR,NDUFA5,GSTO1,SON,GT

F2I,YWHAZ,CDC42,CREB1,ATF6,ITM2B,UCHL3,ATG5,DR1,CHMP2B,DECR1,ADRBK2,FMR1,RAN,MUTE

D,BNIP3L,MAP2K3,UBE2L3,ALDH2,TFAM,BCL10,PDCD2,PARK7,DNM1L,PPM1A,FECH,PDHB,EGLN1,P

DP1,RBX1,MAP2K4,APPL1,PTEN,GSK3B,TXN,SSR1,SFPQ,SLC2A1,FAS,MEF2A,MXI1,ARPP19,SERPINI1,

YY1,NUCB2,DEK,TP53INP1,BAG5,MAPK14,YWHAQ,C1orf9,LARS,FKBP1A,ZCRB1,MYO5A,ABHD5,PANK

2,PCNA,ARHGDIB,NEDD8,TAF1,GCLC,CD55,RB1,NOC2L,IRF2,BCKDHB,FLOT1,RNF41,DPYSL2,ABAT,

USP24,MTIF3,SRI,GLB1,*BCL2L11,LAMP2,GTPBP4,POLG,PSMC1,GNE,AGPS,PDIA3,MAP3K5,PSMD9,A

NXA1,ASPSCR1,DLG4,ATXN3,NDUFB6,UBE2A,HSD17B4,TFCP2,CNDP2,*APAF1,MSN,PGK1,CBS,MTFM

T,AOC3,MAPK1,*BCL2L1,KIAA1267,FXN,TUBB,GNPTAB,HSPA4,GPI,GLUD1,UBB,HSPA8,MEA1,PSMA6,

EIF2AK2,PPP4C,EIF2AK3,DPYD,BCL2L2,B2M,BAP1,LMNA,S100A6,HSP90AB1,TARDBP,HSF1,MRPS7,M

AP3K7,SMG1,FOXO4,MBP,LIAS,GCH1,LPP,FANCB,SHMT1,TFB2M,IKBKAP,MDM2,SSNA1,PRRG4,NDUF

S4,EIF2C2,MTSS1,SNCA,IMMT,RTN4,ACTB,COPS2,AHR,HSPA5,PRKAA1,YWHAH,SMN2,ADCY7,ACO2,HS

PD1,PTPRC,CAST,RAF1,GSN,PAWR,SLC6A8,NMT1,PAF1,PPP3CC,HNMT,TBP,LIMS1,UPP1,NONO,CTNN

BL1,HSPA1A,PES1,LMOD1,IQCB1,HDAC9,PDLIM7,CHM,VIM,TOR1B,UBR5,ATP1A3,HIF1A,BRCA1,SDP

R,SMARCB1,TSPO,AGFG1,ERGIC2,DHDDS,CSNK2A1,TKT,CD44,PARP1,GALC,RAI1,GALNS,PDPK1,TUB

A1A,MAP3K1,CASP1,ENO1,UROD,PPARA,KIF11,MAP2K1,CANX,RB1CC1,TNFRSF1A,FES,IGF2R,DLX4,U

CHL1,GATA1,KIAA0101,HLAE,TUBB3,AES,CA1,PTPN11,IDE,SOD1,PON2,ITGAM,PINK1,ANXA5,NOD2,H

A-

,HLA-

Down-regulated

genes

BEST1,SOX1,FHIT,PRNP,CKB,TPSG1,THY1,GRIN1,EN2,SYN1,ZFPM1,TH,*BCL2,CD4,GDNF,BBC3,SLC17

A7,AVP,CHRNA4,ATN1,FGF3,*BAX,DRD4,ALDOA,ACAP3,TBX1,OXT,DRD2,GGT1,MYC,GFRA3

*, the final six genes

3.2 GO and Pathway Enrichment

Analysis

To better understand the functions of overlapping

genes, GO and KEGG pathway enrichment analyses

were analyzed using an online tool DAVID. Figure 2

showed that the top six significant enrichment terms

for biological process (BP), cellular component (CC),

molecular function (MF) and KEGG of overlapping

genes. In BP annotation, it was significantly involved

in the cell death, apoptotic process, and regulation of

programmed cell death, which are all related to

neuronal cell death as the major event in PD. In the

CC category, it was mainly involved in the cytosol,

mitochondrion, myelin sheath. In the MF category,

genes were primarily enriched in “ubiquitin-like

protein ligase binding”, “enzyme binding” and

“protein kinase binding”. KEGG analysis showed that

the overlapping genes were mainly involved in

Alzheimer's disease, Parkinson's disease and

apoptosis.

Figure 2. The top six significant GO terms and KEGG pathways of common genes. The bar charts represent the counts of

genes classified in the BP, CC, MF and KEGG respectively; the yellow line chart represents the significance of enrichment

terms. GO, gene ontology; BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto

Encyclopedia of Genes and Genomes.

ICHIH 2022 - International Conference on Health Big Data and Intelligent Healthcare

3.3 Protein Interaction and Module

Analysis

The 289 overlapping genes were entered into the

STRING database and then statistically analyzed

using the STRING APP within Cytoscape software.

The results are shown in Figure 3A. A total of 199

genes/nodes with 556 edges participated in the

construction of the PPI networks, and 5 genes haven’t

fallen into the PPI networks. The 6 significant genes

were screened as potential targets for drug-gene

interaction analysis using MCODE application built

in Cytoscape software. The significant gene module

consists 6 genes/nodes with 13 edges/interactions,

which exhibit 4 up-regulated genes (CYCS,

BCL2L11, APAF1 and BCL2L1) and 2 down-

regulated genes (BCL2, BAX) (Fig. 3B).

Figure 3. The PPI networks construction and significant

gene module analysis. (A) The entire PPI networks of

common genes. (B) The significant gene module, including

6 genes.

3.4 Drug-gene Interaction and

Functional Analysis

The 6 genes clustered in the significant gene module

were eventually screened as potential targets for drug-

gene interaction analysis using MCODE application

built in Cytoscape software. Six key genes target to

16 drugs. It was divided into 7 types, with their drug

indications (Figure 4A, Table II). Furthermore, as

shown in Figure 4B, the six target genes are mainly

involved in the intrinsic apoptotic signaling pathway,

the positive regulation of the apoptotic process and

the positive regulation of programmed cell death. In

the CC category, it was mainly involved in the Bcl-2

family protein complex, mitochondrial outer

membrane, organelle outer membrane. In the MF

category, genes were primarily enriched in “BH3

domain binding”, “death domain binding” and “BH

domain binding”.

Exploration of Potential Drug Targets for Parkinson’s via Text Mining and Data Analysis

Figure 4. The drugs targeted to genes and its functional enrichment analysis. (A) Chord plot for the connection between 6

drugs and 16 genes. (B) Chord plot for functional enrichments of 6 genes.

Table 2: The specified information of drugs and its target genes.

Number Gene Drug Interaction Drug class*

1 BCL2 IBUPROFEN Modulator

Anti-inflammatory agents, nephrotoxic agents,

other nonsteroidal anti-inflammatory agents,

causing angioedema agents, causing

hyperkalemia agents, produce hypertension

ents

2 BCL2 NAVITOCLAX Antagonist,Inhibitor Anti-inflammatory agents

3 BCL2 OBATOCLAX Inhibitor Antineoplastic agent

4 BCL2 VENETOCLAX Antagonist,Inhibitor

Antineoplastic agent, apoptosis regulator Bcl-

2 inhibitor,antineoplastic and

immunomodulating agents

5 BCL2 ABT 737 Antagonist Not available

6 BCL2 BORTEZOMIB Inhibitor

Antineoplastic agent,cardiotoxic

antineo

lastic

7 BCL2 OBLIMERSEN

Antisense

oli

onucleotide

agents,hepatotoxic agents,immunosuppressive

ents,

otential

tc-

rolon

ents

8 BCL2 OBATOCLAX Inhibitor Antineoplastic agent

9 BCL2 RASAGILINE Activator Not available

10 BCL2 PACLITAXEL Inhibitor

Antiparkinson agent,antidepressive agents,

serotonin agents

11 BCL2 DEXIBUPROFEN

Inhibitor,negative

modulato

Antineoplastic agent,antiinflammatory

agent,causing

12 BCL2L1 ABT 737 Antagonist

muscle toxicity agents,cardiotoxic

antineoplastic agents,neurotoxic agents

13 BCL2L1 NAVITOCLAX Antagonist,Inhibitor Anti-inflammatory agents,nephrotoxic agents

14 BCL2L1 OBATOCLAX Inhibitor Not available

ICHIH 2022 - International Conference on Health Big Data and Intelligent Healthcare

15 BCL2L1 VENETOCLAX Antagonist

Antineoplastic agent, apoptosis regulator Bcl-

2 inhibitor,antineoplastic and

immunomodulating agents

16 CYCS MINOCYCLINE

Inhibitor,negative

modulato

Photosensitizing agents,causing muscle

toxicity agents

17 CYCS ARTENIMOL Ligand Antiparasitic agents,anti-infective agents

18 BCL2L11 IMATINIB Inhibitor

Antineoplastic agent,antineoplastic

agents,cardiotoxic antineoplastic agents,qtc

rolonging agents,photosensitizing agents

19 APAF1 MYOCET Inhibitor Not available

20 BAX PROCARBAZINE Inhibitor

Antineoplastic agent, antidepressive

agents,serotonin agents

21 BAX CIPROFLOXACIN Inhibitor

Antineoplastic agent,photosensitizing

agents,qtc prolonging agents

*, the drug indications have been approved by FDA

4 DISCUSSION

Parkinson's disease is a common neurodegenerative

disorder caused by the degeneration and apoptosis of

dopaminergic neurons. In this study, the aim was to

find potential therapeutic drugs for PD based on text

mining and data analysis, and to provide fresh ideas

for research into new applications of conventional

drugs. As shown in Figure 4, we finally identified 6

potential genes and 16 existing drugs for PD, which

could be utilized as targets and drugs for the study of

PD.

Based on a search of the published literature, we

found these six genes are tightly associated with PD.

Four genes (Bax, Apaf-1, BCL2L11 and CYCS) show

a promotive effect, while the other two (Bcl-2 and

BCL2L1) sustain an inhibitory effect on PD. The

family of Bcl includes the anti-apoptosis genes (Bcl-

2, Bcl-xL, etc.) and the pro-apoptosis genes (Bax,

BCL2L11, etc.). It inhibits or promotes the release of

cytochrome C (CYCS) into the cytoplasm, which

binds to Apaf-1 (apoptosis protease activating factor-

1) , thus induces apoptosis. This process is involved

in the degeneration and apoptosis of dopaminergic

neurons in PD (Xu et al. 2007, Liu et al. 2020).

According to Wolter et al., BCL2-Associated X (Bax)

is the main pro-apoptotic gene in the Bcl-2 gene

family(Reljic et al. 2016). Under normal conditions,

Bax is present as a monomer in the outer

mitochondrial membrane or cytoplasm, but upon

induction of apoptosis, Bax is specifically

translocated to the mitochondria.

The B-cell lymphoma-2 (Bcl-2), an important

anti-apoptosis gene, is correlated with cell apoptosis.

In general, the Bcl-2 and Bax genes regulate

apoptosis, and Bcl-2 binds to Bax, further inhibiting

Bax and promoting cell survival (Wolter et al. 1997).

CYCS encodes a protein that binds to the inner

membrane of mitochondria, where it receives

electrons from cytochrome B and transfers them to

the cytochrome oxidase complex, thus participating

in the initiation of apoptosis (Reljic et al. 2016). It is

well known that CYCS release, activation of cellular

caspases and subsequent apoptosis are thought to be

among the important factors leading to neuronal cell

death (Lederer et al. 2007). Reljic et al. elucidated

that BCL2L1 belongs to the Bcl-2 protein family of

anti-apoptotic or pro-apoptotic regulators (Nicosia et

al. 2020). The protein encoded by this gene contains

a BCL-2 homologous structural domain 3 (BH3). Its

interaction with other members of the BCL-2 protein

family and role as an apoptotic activator is verified.

Chen, et al. clarified that the BCL2-like protein 1

(BCL2L1) gene encodes a mitochondrial protein

thought to prevent apoptosis in normal cells (Chen et

al. 2019). BCL2L1 may regulate the opening of

channels in the outer mitochondrial membrane and

control the release of cytochrome c. Apaf-1 is a key

molecule in the intrinsic pathway of apoptosis

(Nicosia et al. 2020).

As evidenced by numerous literatures, the main

characteristic of PD is the degeneration and death of

dopaminergic neurons in the midbrain. Apoptosis,

also known as programmed cell death, is one of the

key mechanisms leading to degeneration and death of

dopaminergic neurons in PD patients (Xu et al. 2007,

Wilczynski et al. 2017, Liu et al. 2020). The extrinsic

and the intrinsic apoptosis pathway are the two main

signaling pathways for apoptosis. The extrinsic

apoptosis pathway is activated in PD pathology, but

the underlying mechanisms are yet to be further

investigatied (Mao et al. 2016, Zhang et al. 2020).

The mitochondrial mediated cell apoptosis intrinsic

pathway is stimulated by positive factors (eg. toxins,

radiation and hypoxia) or negative factors (eg. the

absence of hormones and growth factors in the cell)

Exploration of Potential Drug Targets for Parkinson’s via Text Mining and Data Analysis

Pro-apoptotic genes such as BAX are then up-

regulated, while anti-apoptotic genes such as BCL-2

and BCL2L1 are repressed. This arouses change in

the permeability of mitochondrial cell membrane,

resulting in the opening of the mitochondrial

permeability transition pore (mPTP) (Wang et al.

2020). Pro-apoptotic proteins such as cytochrome C

are released from the mitochondria into the cytosol

and bind to Apaf-1 to form apoptosomes, which

activate the caspase cascade. In particular, the

apoptosome activates caspase-9, which in turn

activates caspase-3 and other downstream caspases,

leading to apoptosis (Xu et al. 2007).

The drug interactions between the 6 genes within

16 existing drugs we identified can be divided into

four types, namely modulator, agonist, binder,

antagonist and inhibitor (Table II). These drugs can

be classified into several categories, including anti-

parkinson agents, anti-inflammatory agents, anti-

depressive agents, immunomodulating and anti-

neoplastic agents. The types of drugs identified in this

study were broader and more focused on addressing

PD symptoms that may be caused by apoptotic factors

than those as potential treatments for PD in previous

literature (Xu et al. 2018, Raasmaja et al. 2019,

Elbeddini et al. 2020). The combined use of drugs

may have synergistic therapeutic effects, for example

reducing side effects and improving selectivity.

While these existing drugs offer a new perspective on

the study of PD, their new functions and indications

need to be confirmed in further clinical trials.

5 CONCLUSIONS

According to the text mining conception (keyword:

Parkinson's disease and parkinson) and microarray

data analysis (dataset: GSE22491), we found 16

existing drugs, approved by FDA, target to six genes,

which involved in the intrinsic apoptotic signaling

pathway. These genes might be used for Parkinson's

disease, as well as its original drug indications.

ACKNOWLEDEGMENTS

Thanks to Zhao’s Team of Xiamen University for

suggestions on the manuscript.

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Exploration of Potential Drug Targets for Parkinson’s via Text Mining and Data Analysis