Turki Turki, Professor (Full)

Research Interests

Artificial Intelligence (Machine Learning - Deep Learning); Data Science; Computational Genomics; Bioinformatics

Education

King Abdulaziz University
B.S. in Computer Science

Stanford University
Summer School

NYU.POLY
M.S. in Computer Science

New Jersey Institute of Technology
Doctor of Philosophy in Computer Science
PhD Dissertation: “Development and evaluation of machine learning algorithms for biomedical applications”
Advisors: Jason T. L. Wang and Zhi Wei

Journal Editorial Board

1. Computers in Biology and Medicine.
2. Sustainable Computing: Informatics and Systems.
3. BMC Medical Genomics.
4. PLOS ONE (Academic Editor).
5. Informatics in Medicine Unlocked.
6. BMC Artificial Intelligence.
7. Frontiers in Artificial Intelligence (Review Editor).
8. Frontiers in Big Data (Review Editor). 

Services

1. Nature Communications.
2. Bioinformatics (Oxford).
3. Briefings in Bioinformatics (Oxford).
4. Scientific Reports (Nature).
5. PLOS Computational Biology.
6. Journal of Hazardous Materials (Elsevier).
7. Information Sciences (Elsevier).
8. IEEE Transactions on Neural Networks and Learning Systems.
9. Applied Soft Computing (Elsevier).
10. Computers in Biology and Medicine (Elsevier).
11. Computational and Structural Biotechnology Journal (Elsevier).

Publications
-Y-h. Taguchi and Turki Turki, "Novel Tensor Decomposition-Based Approach for Cell-Type Deconvolution in Visium Datasets with Reference scRNA-Seq Data Containing Multiple Minor Cell Types," Mathematics, 2025.
-Y-h. Taguchi and Turki Turki, "Gene and cell line efficiency of CRISPR computed by tensor decomposition in genome-wide CRISPR-Cas9 knockout screens ," bioRxiv, 2025.
-Y-h. Taguchi and Turki Turki, "RHO GTPase by L1 GABAergic neurons in frontal cortex and L6 glutamatergic neurons in prefrontal cortex differentiates states of unconsciousness," Scientific Reports (Nature), 2025.
-Sumaya Alghamdi, Turki and Turki, Y-h. Taguchi, "De Novo Single-Cell Biological Analysis of Drug Resistance in Human Melanoma Through a Novel Deep Learning-Powered Approach,", Mathematics, 2025.
-Y.-h Taguchi and Turki Turki, "Novel AI-powered computational method using tensor

decomposition for identification of common optimal bin sizes when integrating multiple Hi-C datasets," Scientific Reports (Nature), 2025.
-Y.-h Taguchi and Turki Turki, "Novel artificial intelligence-based identification of drug-gene-disease interaction using protein-protein interaction," BMC Bioinformatics.

-Mansour Almutaani, Turki Turki, and Y-h. Taguchi, "Novel large empirical study of deep transfer learning for COVID-19 classification based on CT and X-ray images," Scientific Reports (Nature), 2024

- Turki Turki, Sarah Al Habib, and Y-h. Taguchi, "Novel Automatic Classification of Human Adult Lung Alveolar Type II Cells Infected with SARS-CoV-2 through the Deep Transfer Learning Approach," Mathematics (MDPI), 2024. IF: 2.4. JCR Math Category: 23/330 (Q1)

- Turki Turki and Y-h. Taguchi, "maGENEgerZ: An Efficient Artificial Intelligence-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism," Mathematics (MDPI), 2024. IF: 2.4. JCR Math

Category: 23/330 (Q1)

-Sumaya Alghamdi and Turki Turki, "A novel interpretable deep transfer learning combining diverse learnable parameters for improved T2D prediction based on single-cell gene regulatory networks," Scientific Reports (Nature), 2024. IF: 4.6.

-Turki Turki, Sanjiban Sekhar Roy, and Y-h. Taguchi, "Optimized Tensor Decomposition and PCA Outperforming State-of-the-Art Methods When Analyzing Histone Modification ChIP-seq Profiles," Algorithms (MDPI), 2023. IF: 2.3

-Y-h. Taguchi and Turki Turki, " Integrated analysis of gene expression and protein-protein interaction with tensor decomposition," Mathematics (MDPI), 2023. IF: 2.4

-Y-h. Taguchi and Turki Turki, "TDbasedUFE and TDbasedUFEadv: bioconductor packages to perform tensor decomposition based unsupervised feature extraction," Frontiers in Artificial Intelligence, 2023. IF: 4

-Turki Turki and Y-h. Taguchi, "GENEvaRX: A Novel AI-Driven Method and Web Tool Can Identify Critical Genes and Effective Drugs for Lichen Planus," Engineering Applications of Artificial Intelligence (Elsevier), 2023. IF: 8.0.

- Hamed Alghamdi and Turki Turki, "PDD-Net: Plant Disease Diagnoses Using Multilevel and Multiscale Convolutional Neural Network Features," Agriculture (MDPI), 2023. IF: 3.6

- Wael Alhazmi and Turki Turki, "Applying Deep Transfer Learning to Assess the Impact of Imaging Modalities on Colon Cancer Detection," Diagnostics (MDPI), 2023. IF: 3.6

-Y-h. Taguchi and Turki Turki, "Advanced tensor decomposition-based integrated analysis of protein-protein interaction with cancer gene expression can improve coincidence with clinical labels," bioRxiv, 2023

- Y-h. Taguchi and Turki Turki, "Tensor Decomposition Discriminates Tissues Using scATAC-seq," BBA - General Subject (Elsevier) , 2023. IF: 3 

- Y-h. Taguchi and Turki Turki, "Principal component analysis- and tensor decomposition-based unsupervised feature extraction to select more reasonable differentially methylated cytosines: Optimization of standard deviation versus state-of-the-art methods," Genomics (Elsevier), 2023. IF: 4.4

- Turki Turki and Y-h. Taguchi, "A new machine learning based computational framework identifies therapeutic targets and unveils influential genes in pancreatic islet cells," Gene (Elsevier), 2023. IF: 3.913.

-Arif Ahmed Sk, Turki Turki, Tarun Kumar Ghosh, Subhankar Joardar, Subhabrata Barman, "Artificial Intelligence: First International Symposium, ISAI 2022, Haldia, India, February 17-22, 2022, Revised Selected Papers", Springer

-Y-h. Taguchi and Turki Turki, "A tensor decomposition-based integrated analysis applicable to multiple gene expression profiles without sample matching," Scientific Reports (Nature), 2022, IF: 4.996

- Y-h. Taguchi and Turki Turki, "Adapted Tensor decomposition and PCA based unsupervised feature extraction select more biologically reasonable differentially expressed genes than conventional methods," Scientific Reports (Nature), 2022, IF: 4.996.

- Y-h. Taguchi and Turki Turki, "Projection in genomic analysis: A theoretical basis to rationalize tensor decomposition and principal component analysis as feature selection tools," PLOS ONE, 2022, IF: 3.752.

- Turki Turki and SANJIBAN SEKHAR ROY, "Novel hate speech detection using word cloud visualization and ensemble learning coupled with count vectorizer, " Applied Sciences (MDPI), 2022. IF: 2.679

- Khalil Aljohani and Turki Turki, "AUTOMATIC CLASSIFICATION OF MELANOMA SKIN CANCER WITH DEEP CONVOLUTIONAL NEURAL NETWORKS," AI (MDPI), 2022.

- Y-h. Taguchi and Turki Turki, "Integrated Analysis of Tissue-specific Gene Expression in Diabetes by Tensor Decomposition Can Identify Possible Associated Diseases," Genes, 2022. IF: 4.096.

- Y-h. Taguchi and Turki Turki, "Novel feature selection via kernel tensor decomposition for improved multi-omics data analysis," BMC Medical Genomics, 2022. IF: 3.063

- Turki Turki and Zhi Wei, "Improved Deep Convolutional Neural Networks via Boosting for Predicting the Quality of in Vitro Bovine Embryos," in Electronics (Medical Image Computing and Analysis Special Issue), 2022. IF: 2.397

- Y-h. Taguchi and Turki Turki, "Effects of the collagen–glycosaminoglycan mesh on gene expression as determined by using principal component analysis-based unsupervised feature extraction, " Polymers, 2021. IF: 4.329

-Y-h. Taguchi and Turki Turki, "Tensor-decomposition-based unsupervised feature extraction in single-cell multiomics data analysis, " Genes, 2021. IF: 4.096

- Xiang Lin, Jie Zhang, Zhi Wei, and Turki Turki,"An Omnibus Test for Differential Distribution Analysis of Continuous Microbiome Data," IEEE Access, 2021. IF: 3.367

- Turki Turki, Anmar Al-Sharif and Y-h. Taguchi, "End-to-End Deep Learning for Detecting Metastatic Breast Cancer in Axillary Lymph Node from Digital Pathology Images," IDEAL, 2021.

- Y-h. Taguchi and Turki Turki, "Mathematical formulation and application of kernel tensor decomposition based unsupervised feature extraction," Knowledge-Based Systems, 2021, Impact Factor: 8.038

- Y-h. Taguchi and Turki Turki, "Unsupervised tensor decomposition-based method to extract candidate transcription factors as histone modification bookmarks in post-mitotic transcriptional reactivation," PLOS ONE, 2021.

- Turki Turki and Y-h. Taguchi, "Discriminating the Single-cell Gene Regulatory Networks of Human Pancreatic Islets: A Novel Deep Learning Application," Computers in Biology and Medicine, 2021, IF: 4.589

- Y-h. Taguchi and Turki Turki, "Tensor decomposition based unsupervised feature extraction in big data analytics applied to prostate cancer multiomics data," Genes, 2020. Impact Factor: 3.759

- Y-h. Taguchi and Turki Turki, "Universal nature of drug treatment responses in drug-tissue-wide model-animal experiments using tensor decomposition-based unsupervised feature extraction," Frontiers in Genetics, 2020. Impact Factor: 3.258

- Y-h. Taguchi and Turki Turki, “Application of Tensor Decomposition to Gene Expression of Infection of Mouse Hepatitis Virus can Identify Critical Human Genes and Efffective Drugs for SARS-CoV-2 Infection,” 2021, IEEE Journal on Selected Topics in Signal Processing. Impact Factor: 6.856

- Y-h. Taguchi and Turki Turki, “Novel Method for the Prediction of Drug–Drug Interactions Based on Gene Expression Profiles,” European Journal of Pharmaceutical Sciences, 2021, Cite Score: 7.6

- Y-h. Taguchi and Turki Turki, “A New Advanced In Silico Drug Discovery Method for Novel Coronavirus (SARS-CoV-2) with Tensor Decomposition-Based Unsupervised Feature

Extraction,” PLOS ONE, 2020, Impact Factor: 2.74

- Abdullah Algarni, Emad Kaen, Turki Turki, "New Machine Learning Approaches to Improve Software Bug Prediction," International Conference on Machine Learning and Data Mining, 2020, New York.

- Zhihang Hu, Turki Turki, Jason T. L. Wang, "Generative Adversarial Networks for Stochastic Video Prediction with Action Control," IEEE Access, 2020. Impact Factor: 4.098

- Turki Turki and Y-h. Taguchi, "SCGRNs: Novel supervised inference of single-cell gene regulatory networks of complex diseases," Computers in Biology and Medicine, 2020. Impact Factor: 2.286

- Y-h. Taguchi and Turki Turki, "Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis," Frontiers in Genetics, 2019. Impact Factor: 3.517

- Y-h. Taguchi and Turki Turki, "Neurological disorder drug discovery from gene expression with tensor decomposition," Current Pharmaceutical Design, 2019. Impact Factor: 2.412

- Turki Turki and Y-h. Taguchi, "Machine Learning Algorithms for Predicting Drugs-Tissues Relationships," Expert Systems with Applications, 2019. Impact Factor: 3.768

- Turki Turki and Jason T. L. Wang, "Clinical intelligence: New machine learning techniques for predicting clinical drug response," Computers in Biology and Medicine, 2019. Impact Factor: 2.115

- Haodi Jiang, Turki Turki and Jason T. L. Wang, "DLGraph: Malware Detection Using Deep Learning and Graph Embedding," the 17th ICMLA, 2018.

- Turki Turki and Zhi Wei, "Boosting Support Vector Machines for Cancer Discrimination Tasks," Computers in Biology and Medicine, 2018. Impact Factor: 2.115

- Z. Hu, T. Turki, N. Phan and J. T. L. Wang, "A 3D Atrous Convolutional Long Short-Term Memory Network for Background Subtraction," in IEEE Access, 2018. Impact Factor: 3.557

- Liulin Yang, Yun Li, Turki Turki, Huizi Tan, Zhi Wei and Xiao Chang, "Weighted Gene Co- Expression Network Analysis Reveals Dysregulation of Mitochondrial Oxidative Phosphorylation in Eating Disorders," Genes, 2018. Impact Factor: 3.191

- Haodi Jiang, Turki Turki, Sen Zhang and Jason T. L. Wang, "Reverse Engineering Gene Regulatory Networks Using Graph Mining," the 14th International Conference on Machine Learning and Data Mining (MLDM 2018), July 13 - 18, 2018, New York, USA.

- Turki Turki, Zhi Wei, Jason T. L. Wang, "A Transfer Learning Approach via Procrustes Analysis and Mean Shift for Cancer Drug Sensitivity Prediction," the Journal of Bioinformatics and Computational Biology (JBCB). Impact Factor: 0.800

- Turki Turki, "An Empirical Study of Machine Learning Algorithms for Cancer Identification," the 15th IEEE International Conference on Networking, Sensing and Control (ICNSC 2018), Zhuhai, China, March 27-29, 2018.

- Haodi Jiang, Turki Turki, and Jason T. L. Wang, ”Reverse Engineering Regulatory Networks in Cells Using a Dynamic Bayesian Network and Mutual Information Scoring Function,” the 16th IEEE International Conference on Machine Learning and Applications, Cancun, Mexico, December 18-21, 2017.

- Turki Turki, Zhi Wei, and Jason T. L. Wang, "A Transfer Learning Approach via Procrustes Analysis and Mean Shift for Cancer Drug Sensitivity Prediction," the 28th International Conference on Genome Informatics Workshop (GIW) / BIOINFO 2017, Seoul, Korea, Oct 31-Nov 3, 2017.

- Turki Turki, Zhi Wei, and Jason T. L. Wang, “Transfer Learning Approaches to Improve Drug Sensitivity Prediction in Multiple Myeloma Patients,” IEEE Access. Impact Factor: 3.224

- Turki Turki and Jason T. L. Wang, “Reverse Engineering Gene Regulatory Networks Using Sampling and Boosting Techniques,” the 13th International Conference on Machine Learning and Data Mining, New York, NY, 2017.

- Yasser Abduallah, Turki Turki, Kevin Byron, Zongxuan Du, Miguel Cervantes-Cervantes, and Jason T. L. Wang, “MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach,” BioMed Research International, vol. 2017, Article ID 6261802, 8 pages, 2017. Impact Factor: 2.467

- Turki Turki and Zhi Wei, “A Noise-Filtering Approach for Cancer Drug Sensitivity Prediction,” in the Proceedings of the Neural Information Processing Systems Workshop on Machine Learning for Health (NIPS ML4HC), Barcelona, Spain, 2016.

-Turki Turki and Zhi Wei, “A Link Prediction Approach to Cancer Drug Sensitivity Prediction,” International Conference on Intelligent Biology and Medicine (ICIBM), Houston, Texas, USA, 2016. Accepted for inclusion as a special issue in BioMed Central

(BMC) Systems Biology. Impact Factor: 2.303

- Turki Turki, Jason T. L. Wang, and Ibrahim Rajikhan, “Inferring Gene Regulatory Networks by Combining Supervised and Unsupervised Methods,” in the Proceedings of the 15th International Conference on Machine Learning and Applications (ICMLA), Anaheim, California, 2016.

- Turki Turki and Zhi Wei, “Learning Approaches to Improve Prediction of Drug Sensitivity in Breast Cancer Patients,” in the Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Orlando, FL, 2016.

61. Turki Turki and Jason T. L. Wang, “A Learning Framework to Improve Unsupervised Gene Network Inference,” in the Proceedings of the 12th International Conference on Machine Learning and Data Mining, New York, NY, pp. 28-42, 2016.

- Turki Turki and Zhi Wei, “A Greedy-Based Oversampling Approach to Improve the Prediction of Mortality in MERS Patients,” in the Proceedings of the 10th Annual IEEE International Systems Conference, Orlando, FL, 2016.

- Turki Turki and Jason T. L. Wang, "A New Approach to Link Prediction in Gene Regulatory Networks," in the Proceedings of the 16th International Conference on Intelligent Data Engineering and Automated Learning, Wroclaw, Poland, 2015.

- Turki Turki and Zhi Wei, “IPRed: Instance Reduction Algorithm Based on the Percentile of the Partitions,” in the Proceedings of the 26th Modern AI and Cognitive Science Conference, Greensboro, NC, 2015.

- Turki Turki and Usman Roshan, "MaxSSmap: A GPU program for divergent short read mapping to genomes with the maximum scoring subsequence," BioMed Central Genomics (BMC Genomics), 2014. Impact Factor: 3.729.

- Turki Turki, Muhammad Amimul Ihsan, Nouf Turki, Jie Zhang, Usman Roshan and Zhi Wei, “Top-k Parametrized Boost,” in the Proceedings of the Second International Conference on Mining Intelligence and Knowledge Exploration, Cork, Ireland, 2014.

- Turki and Usman Roshan, “Weighted Maximum Variance Dimensionality Reduction,” in the Proceedings of the 6th Mexican Conference on Pattern Recognition, Cancun, Mexico, 2014

Teaching

-Machine Learning
-Special Topics: Machine Learning and Data Mining
-Advanced Artificial Intelligence
-Deep Learning
-Artificial Intelligence Topics
-Discrete Structures
-Programming II
-Programming I

Programming Skills:

-Python
-R