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Article ; Online: A machine learning method based on the genetic and world competitive contests algorithms for selecting genes or features in biological applications.

Masoudi-Sobhanzadeh, Yosef / Motieghader, Habib / Omidi, Yadollah / Masoudi-Nejad, Ali

2021 Volume 11, Issue 1, Page(s) 3349

Abstract: Gene/feature selection is an essential preprocessing step for creating models using machine learning techniques. It also plays a critical role in different biological applications such as the identification of biomarkers. Although many feature/gene ... ...

Abstract	Gene/feature selection is an essential preprocessing step for creating models using machine learning techniques. It also plays a critical role in different biological applications such as the identification of biomarkers. Although many feature/gene selection algorithms and methods have been introduced, they may suffer from problems such as parameter tuning or low level of performance. To tackle such limitations, in this study, a universal wrapper approach is introduced based on our introduced optimization algorithm and the genetic algorithm (GA). In the proposed approach, candidate solutions have variable lengths, and a support vector machine scores them. To show the usefulness of the method, thirteen classification and regression-based datasets with different properties were chosen from various biological scopes, including drug discovery, cancer diagnostics, clinical applications, etc. Our findings confirmed that the proposed method outperforms most of the other currently used approaches and can also free the users from difficulties related to the tuning of various parameters. As a result, users may optimize their biological applications such as obtaining a biomarker diagnostic kit with the minimum number of genes and maximum separability power.
MeSH term(s)	Genetic Markers ; Machine Learning ; Models, Genetic
Chemical Substances	Genetic Markers
Language	English
Publishing date	2021-02-08
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-021-82796-y
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Drug Repurposing for Alzheimer's Disease Based on Protein-Protein Interaction Network.

Soleimani Zakeri, Negar Sadat / Pashazadeh, Saeid / MotieGhader, Habib

BioMed research international

2021 Volume 2021, Page(s) 1280237

Abstract: Alzheimer's disease (AD) is known as a critical neurodegenerative disorder. It worsens as symptoms concerning dementia grow severe over the years. Due to the globalization of Alzheimer's disease, its prevention and treatment are vital. This study ... ...

Abstract	Alzheimer's disease (AD) is known as a critical neurodegenerative disorder. It worsens as symptoms concerning dementia grow severe over the years. Due to the globalization of Alzheimer's disease, its prevention and treatment are vital. This study proposes a method to extract substantial gene complexes and then introduces potential drugs in Alzheimer's disease. To this end, a protein-protein interaction (PPI) network was utilized to extract five meaningful gene complexes functionally interconnected. An enrichment analysis to introduce the most important biological processes and pathways was accomplished on the obtained genes. The next step is extracting the drugs related to AD and introducing some new drugs which may be helpful for this disease. Finally, a complete network including all the genes associated with each gene complex group and genes' target drug was illustrated. For validating the proposed potential drugs, Connectivity Map (CMAP) analysis was accomplished to determine target genes that are up- or downregulated by proposed drugs. Medical studies and publications were analyzed thoroughly to introduce AD-related drugs. This analysis proves the accuracy of the proposed method in this study. Then, new drugs were introduced that can be experimentally examined as future work. Raloxifene and gentian violet are two new drugs, which have not been introduced as AD-related drugs in previous scientific and medical studies, recommended by the method of this study. Besides the primary goal, five bipartite networks representing the genes of each group and their target miRNAs were constructed to introduce target miRNAs.
MeSH term(s)	Alzheimer Disease/drug therapy ; Alzheimer Disease/genetics ; Alzheimer Disease/metabolism ; Alzheimer Disease/pathology ; Computational Biology/methods ; Databases, Genetic ; Drug Repositioning/methods ; Gene Regulatory Networks ; Humans ; Protein Interaction Maps ; Transcriptome
Language	English
Publishing date	2021-10-14
Publishing country	United States
Document type	Journal Article
ZDB-ID	2698540-8
ISSN	2314-6141 ; 2314-6133
ISSN (online)	2314-6141
ISSN	2314-6133
DOI	10.1155/2021/1280237
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Article ; Online: Application of Protein-Protein Interaction Network Analysis in Order to Identify Cervical Cancer miRNA and mRNA Biomarkers.

Tabrizi-Nezhadi, Parinaz / MotieGhader, Habib / Maleki, Masoud / Sahin, Soner / Nematzadeh, Sajjad / Torkamanian-Afshar, Mahsa

TheScientificWorldJournal

2023 Volume 2023, Page(s) 6626279

Abstract: Cervical cancer (CC) is one of the world's most common and severe cancers. This cancer includes two histological types: squamous cell carcinoma (SCC) and adenocarcinoma (ADC). The current study aims at identifying novel potential candidate mRNA and miRNA ...

Abstract	Cervical cancer (CC) is one of the world's most common and severe cancers. This cancer includes two histological types: squamous cell carcinoma (SCC) and adenocarcinoma (ADC). The current study aims at identifying novel potential candidate mRNA and miRNA biomarkers for SCC based on a protein-protein interaction (PPI) and miRNA-mRNA network analysis. The current project utilized a transcriptome profile for normal and SCC samples. First, the PPI network was constructed for the 1335 DEGs, and then, a significant gene module was extracted from the PPI network. Next, a list of miRNAs targeting module's genes was collected from the experimentally validated databases, and a miRNA-mRNA regulatory network was formed. After network analysis, four driver genes were selected from the module's genes including
MeSH term(s)	Female ; Humans ; Uterine Cervical Neoplasms/diagnosis ; Uterine Cervical Neoplasms/genetics ; Protein Interaction Maps/genetics ; Biomarkers ; MicroRNAs/genetics ; RNA, Messenger/genetics ; NF-kappa B
Chemical Substances	Biomarkers ; MicroRNAs ; RNA, Messenger ; TONSL protein, human ; NF-kappa B
Language	English
Publishing date	2023-09-14
Publishing country	United States
Document type	Journal Article
ZDB-ID	2075968-X
ISSN	1537-744X ; 1537-744X
ISSN (online)	1537-744X
ISSN	1537-744X
DOI	10.1155/2023/6626279
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Article ; Online: Interleukin-2 and Oncolytic Virotherapy: A New Perspective in Cancer Therapy.

Aghbash, Parisa Shiri / Rasizadeh, Reyhaneh / Yari, Amir Hossein / Lahouti, Shiva / MotieGhader, Habib / Nahand, Javid Sadri / Entezari-Maleki, Taher / Baghi, Hossein Bannazadeh

Anti-cancer agents in medicinal chemistry

2023 Volume 23, Issue 18, Page(s) 2008–2026

Abstract: By triggering immune responses in malignancies that have generally been linked to poor outcomes, immunotherapy has recently shown effectiveness. On the other hand, tumors provide an environment for cells that influence the body's immunity against cancer. ...

Abstract	By triggering immune responses in malignancies that have generally been linked to poor outcomes, immunotherapy has recently shown effectiveness. On the other hand, tumors provide an environment for cells that influence the body's immunity against cancer. Malignant cells also express large amounts of soluble or membrane-bound ligands and immunosuppressive receptors. In this regard, the combination of oncolytic viruses with pro-inflammatory or inflammatory cytokines, including IL-2, can be a potential therapy for some malignancies. Indeed, oncolytic viruses cause the death of cancerous cells and destroy the tumor microenvironment. They result in the local release of threat signals and antigens associated with tumors. As a result, it causes lymphocyte activity and the accumulation of antigenpresenting cells which causes them to accumulate in the tumor environment and release cytokines and chemokines. In this study, we reviewed the functions of IL-2 as a crucial type of inflammatory cytokine in triggering immune responses, as well as the effect of its release and increased expression following combination therapy with oncolytic viruses in the process of malignant progression, as an essential therapeutic approach that should be taken into consideration going forward.
Language	English
Publishing date	2023-07-27
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2217610-X
ISSN	1875-5992 ; 1871-5206
ISSN (online)	1875-5992
ISSN	1871-5206
DOI	10.2174/1871520623666230727095154
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Article ; Online: FeatureSelect: a software for feature selection based on machine learning approaches.

Masoudi-Sobhanzadeh, Yosef / Motieghader, Habib / Masoudi-Nejad, Ali

BMC bioinformatics

2019 Volume 20, Issue 1, Page(s) 170

Abstract: Background: Feature selection, as a preprocessing stage, is a challenging problem in various sciences such as biology, engineering, computer science, and other fields. For this purpose, some studies have introduced tools and softwares such as WEKA. ... ...

Abstract	Background: Feature selection, as a preprocessing stage, is a challenging problem in various sciences such as biology, engineering, computer science, and other fields. For this purpose, some studies have introduced tools and softwares such as WEKA. Meanwhile, these tools or softwares are based on filter methods which have lower performance relative to wrapper methods. In this paper, we address this limitation and introduce a software application called FeatureSelect. In addition to filter methods, FeatureSelect consists of optimisation algorithms and three types of learners. It provides a user-friendly and straightforward method of feature selection for use in any kind of research, and can easily be applied to any type of balanced and unbalanced data based on several score functions like accuracy, sensitivity, specificity, etc. RESULTS: In addition to our previously introduced optimisation algorithm (WCC), a total of 10 efficient, well-known and recently developed algorithms have been implemented in FeatureSelect. We applied our software to a range of different datasets and evaluated the performance of its algorithms. Acquired results show that the performances of algorithms are varying on different datasets, but WCC, LCA, FOA, and LA are suitable than others in the overall state. The results also show that wrapper methods are better than filter methods. Conclusions: FeatureSelect is a feature or gene selection software application which is based on wrapper methods. Furthermore, it includes some popular filter methods and generates various comparison diagrams and statistical measurements. It is available from GitHub ( https://github.com/LBBSoft/FeatureSelect ) and is free open source software under an MIT license.
MeSH term(s)	Algorithms ; Machine Learning ; Sensitivity and Specificity ; Software
Language	English
Publishing date	2019-04-03
Publishing country	England
Document type	Evaluation Study ; Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-019-2754-0
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A machine learning method based on the genetic and world competitive contests algorithms for selecting genes or features in biological applications

Yosef Masoudi-Sobhanzadeh / Habib Motieghader / Yadollah Omidi / Ali Masoudi-Nejad

Scientific Reports, Vol 11, Iss 1, Pp 1-

2021 Volume 19

Abstract: Abstract Gene/feature selection is an essential preprocessing step for creating models using machine learning techniques. It also plays a critical role in different biological applications such as the identification of biomarkers. Although many feature/ ... ...

Abstract	Abstract Gene/feature selection is an essential preprocessing step for creating models using machine learning techniques. It also plays a critical role in different biological applications such as the identification of biomarkers. Although many feature/gene selection algorithms and methods have been introduced, they may suffer from problems such as parameter tuning or low level of performance. To tackle such limitations, in this study, a universal wrapper approach is introduced based on our introduced optimization algorithm and the genetic algorithm (GA). In the proposed approach, candidate solutions have variable lengths, and a support vector machine scores them. To show the usefulness of the method, thirteen classification and regression-based datasets with different properties were chosen from various biological scopes, including drug discovery, cancer diagnostics, clinical applications, etc. Our findings confirmed that the proposed method outperforms most of the other currently used approaches and can also free the users from difficulties related to the tuning of various parameters. As a result, users may optimize their biological applications such as obtaining a biomarker diagnostic kit with the minimum number of genes and maximum separability power.
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2021-02-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Drug Repurposing for Alzheimer’s Disease Based on Protein-Protein Interaction Network

Negar Sadat Soleimani Zakeri / Saeid Pashazadeh / Habib MotieGhader

BioMed Research International, Vol

2021 Volume 2021

Abstract: Alzheimer’s disease (AD) is known as a critical neurodegenerative disorder. It worsens as symptoms concerning dementia grow severe over the years. Due to the globalization of Alzheimer’s disease, its prevention and treatment are vital. This study ... ...

Abstract	Alzheimer’s disease (AD) is known as a critical neurodegenerative disorder. It worsens as symptoms concerning dementia grow severe over the years. Due to the globalization of Alzheimer’s disease, its prevention and treatment are vital. This study proposes a method to extract substantial gene complexes and then introduces potential drugs in Alzheimer’s disease. To this end, a protein-protein interaction (PPI) network was utilized to extract five meaningful gene complexes functionally interconnected. An enrichment analysis to introduce the most important biological processes and pathways was accomplished on the obtained genes. The next step is extracting the drugs related to AD and introducing some new drugs which may be helpful for this disease. Finally, a complete network including all the genes associated with each gene complex group and genes’ target drug was illustrated. For validating the proposed potential drugs, Connectivity Map (CMAP) analysis was accomplished to determine target genes that are up- or downregulated by proposed drugs. Medical studies and publications were analyzed thoroughly to introduce AD-related drugs. This analysis proves the accuracy of the proposed method in this study. Then, new drugs were introduced that can be experimentally examined as future work. Raloxifene and gentian violet are two new drugs, which have not been introduced as AD-related drugs in previous scientific and medical studies, recommended by the method of this study. Besides the primary goal, five bipartite networks representing the genes of each group and their target miRNAs were constructed to introduce target miRNAs.
Keywords	Medicine ; R
Subject code	570
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Hindawi Limited
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Gene biomarker discovery at different stages of Alzheimer using gene co-expression network approach.

Soleimani Zakeri, Negar Sadat / Pashazadeh, Saeid / MotieGhader, Habib

Scientific reports

2020 Volume 10, Issue 1, Page(s) 12210

Abstract: Alzheimer's disease (AD) is a chronic neurodegenerative disorder. It is the most common type of dementia that has remained as an incurable disease in the world, which destroys the brain cells irreversibly. In this study, a systems biology approach was ... ...

Abstract	Alzheimer's disease (AD) is a chronic neurodegenerative disorder. It is the most common type of dementia that has remained as an incurable disease in the world, which destroys the brain cells irreversibly. In this study, a systems biology approach was adopted to discover novel micro-RNA and gene-based biomarkers of the diagnosis of Alzheimer's disease. The gene expression data from three AD stages (Normal, Mild Cognitive Impairment, and Alzheimer) were used to reconstruct co-expression networks. After preprocessing and normalization, Weighted Gene Co-Expression Network Analysis (WGCNA) was used on a total of 329 samples, including 145 samples of Alzheimer stage, 80 samples of Mild Cognitive Impairment (MCI) stage, and 104 samples of the Normal stage. Next, three gene-miRNA bipartite networks were reconstructed by comparing the changes in module groups. Then, the functional enrichment analyses of extracted genes of three bipartite networks and miRNAs were done, respectively. Finally, a detailed analysis of the authentic studies was performed to discuss the obtained biomarkers. The outcomes addressed proposed novel genes, including MBOAT1, ARMC7, RABL2B, HNRNPUL1, LAMTOR1, PLAGL2, CREBRF, LCOR, and MRI1and novel miRNAs comprising miR-615-3p, miR-4722-5p, miR-4768-3p, miR-1827, miR-940 and miR-30b-3p which were related to AD. These biomarkers were proposed to be related to AD for the first time and should be examined in future clinical studies.
MeSH term(s)	Acetyltransferases/genetics ; Alzheimer Disease/genetics ; Alzheimer Disease/pathology ; Biomarkers/metabolism ; Cognitive Dysfunction/genetics ; Cognitive Dysfunction/pathology ; DNA-Binding Proteins/genetics ; Databases, Genetic ; Female ; Gene Regulatory Networks/genetics ; Humans ; Male ; Membrane Proteins/genetics ; MicroRNAs/metabolism ; RNA-Binding Proteins/genetics ; Severity of Illness Index ; Transcription Factors/genetics ; rab GTP-Binding Proteins/genetics
Chemical Substances	Biomarkers ; DNA-Binding Proteins ; Membrane Proteins ; MicroRNAs ; PLAGL2 protein, human ; RNA-Binding Proteins ; Transcription Factors ; Acetyltransferases (EC 2.3.1.-) ; MBOAT1 protein, human (EC 2.3.1.-) ; RABL2B protein, human (EC 3.6.1.-) ; rab GTP-Binding Proteins (EC 3.6.5.2)
Language	English
Publishing date	2020-07-22
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-020-69249-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: World Competitive Contests (WCC) algorithm: A novel intelligent optimization algorithm for biological and non-biological problems.

Masoudi-Sobhanzadeh, Yosef / Motieghader, Habib

Informatics in medicine unlocked

2016 Volume 3, Page(s) 15–28

Abstract: Since different sciences face lots of problems which cannot be solved in reasonable time order, we need new methods and algorithms for getting acceptable answers in proper time order. In the present study, a novel intelligent optimization algorithm, ... ...

Abstract	Since different sciences face lots of problems which cannot be solved in reasonable time order, we need new methods and algorithms for getting acceptable answers in proper time order. In the present study, a novel intelligent optimization algorithm, known as WCC (World Competitive Contests), has been proposed and applied to find the transcriptional factor binding sites (TFBS) and eight benchmark functions discovery processes. We recognize the need to introduce an intelligent optimization algorithm because the TFBS discovery is a biological and an NP-Hard problem. Although there are some intelligent algorithms for the purpose of solving the above-mentioned problems, an optimization algorithm with good and acceptable performance, which is based on the real parameters, is essential. Like the other optimization algorithms, the proposed algorithm starts with the first population of teams. After teams are put into different groups, they will begin competing against their rival teams. The highly qualified teams will ascend to the elimination stage and will play each other in the next rounds. The other teams will wait for a new season to start. In this paper, we're going to implement our proposed algorithm and compare it with five famous optimization algorithms from the perspective of the following
Keywords	covid19
Language	English
Publishing date	2016-06-28
Publishing country	England
Document type	Journal Article
ISSN	2352-9148
ISSN	2352-9148
DOI	10.1016/j.imu.2016.06.002
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Drug repurposing for coronavirus (SARS-CoV-2) based on gene co-expression network analysis.

MotieGhader, Habib / Safavi, Esmaeil / Rezapour, Ali / Amoodizaj, Fatemeh Firouzi / Iranifam, Roya Asl

Scientific reports

2021 Volume 11, Issue 1, Page(s) 21872

Abstract: Severe acute respiratory syndrome (SARS) is a highly contagious viral respiratory illness. This illness is spurred on by a coronavirus known as SARS-associated coronavirus (SARS-CoV). SARS was first detected in Asia in late February 2003. The genome of ... ...

Abstract	Severe acute respiratory syndrome (SARS) is a highly contagious viral respiratory illness. This illness is spurred on by a coronavirus known as SARS-associated coronavirus (SARS-CoV). SARS was first detected in Asia in late February 2003. The genome of this virus is very similar to the SARS-CoV-2. Therefore, the study of SARS-CoV disease and the identification of effective drugs to treat this disease can be new clues for the treatment of SARS-Cov-2. This study aimed to discover novel potential drugs for SARS-CoV disease in order to treating SARS-Cov-2 disease based on a novel systems biology approach. To this end, gene co-expression network analysis was applied. First, the gene co-expression network was reconstructed for 1441 genes, and then two gene modules were discovered as significant modules. Next, a list of miRNAs and transcription factors that target gene co-expression modules' genes were gathered from the valid databases, and two sub-networks formed of transcription factors and miRNAs were established. Afterward, the list of the drugs targeting obtained sub-networks' genes was retrieved from the DGIDb database, and two drug-gene and drug-TF interaction networks were reconstructed. Finally, after conducting different network analyses, we proposed five drugs, including FLUOROURACIL, CISPLATIN, SIROLIMUS, CYCLOPHOSPHAMIDE, and METHYLDOPA, as candidate drugs for SARS-CoV-2 coronavirus treatment. Moreover, ten miRNAs including miR-193b, miR-192, miR-215, miR-34a, miR-16, miR-16, miR-92a, miR-30a, miR-7, and miR-26b were found to be significant miRNAs in treating SARS-CoV-2 coronavirus.
MeSH term(s)	COVID-19/immunology ; COVID-19/virology ; Computational Biology ; Drug Repositioning ; Gene Expression Profiling ; Gene Expression Regulation, Viral ; Gene Regulatory Networks ; Genes, Viral ; Genetic Techniques ; Humans ; MicroRNAs/metabolism ; Oligonucleotide Array Sequence Analysis ; SARS-CoV-2 ; Systems Biology ; Transcription Factors ; COVID-19 Drug Treatment
Chemical Substances	MicroRNAs ; Transcription Factors
Language	English
Publishing date	2021-11-08
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-021-01410-3
Database	MEDical Literature Analysis and Retrieval System OnLINE

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