This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.
Sareena A. Mulani,
- Student, Department of Electrical Engineering, D.Y.Patil Institute of Technology, Pune, India, Maharashtra, India
Abstract
Genomic selection (GS) predicts complex quantitative traits directly from genome-wide molecular markers, bypassing the need for extensive phenotypic trials and accelerating plant breeding cycles. We conducted a comparative evaluation of seven regression approaches — ridge regression (the machine-learning equivalent of RR-BLUP), Lasso, Elastic Net, Partial Least Squares, linear Support Vector Regression, Random Forest, and Gradient Boosting — for predicting grain yield from 1,279 Diversity Array Technology (DArT) molecular markers genotyped across 599 CIMMYT wheat lines evaluated in four independent environments (Crossa et al., 2010; data distributed with the BGLR package). Using 5-fold cross-validation, we measured predictive ability as the Pearson correlation between predicted and observed yield. With default hyperparameters, Random Forest achieved the highest mean predictive ability (r = 0.469 across environments), outperforming ridge regression (r = 0.414). However, when we corrected this comparison by properly tuning the ridge penalty via nested cross-validation, ridge 2 regression’s accuracy rose to r = 0.462 — nearly matching Random Forest and substantially narrowing what had first appeared to be a clear machine-learning advantage. In contrast, applying the same automated tuning procedure to Lasso and Elastic Net did not improve, and in most environments reduced, their accuracy (mean r falling from 0.408 to 0.349 for Lasso), a result we trace to the coarse inner-cross-validation grid and to the mismatch between the tuning objective (mean squared error) and the evaluation metric (Pearson correlation). We report this correction transparently because it materially changes the paper’s central comparative claim and illustrates a documented but often underreported pitfall in applied genomic-prediction benchmarking: an apparent superiority of one model family over another can be, in whole or in part, an artifact of unequal hyperparameter-tuning effort rather than a genuine difference in model capacity. Our measured predictive abilities (r = 0.25-0.56 across models and environments) fall within the range reported in prior wheat genomic-selection studies using comparable marker densities and population sizes. We report Random Forest feature importance for exploratory marker prioritization, while noting that linkage disequilibrium among DArT markers makes individual marker importance rankings unstable and not directly interpretable as causal effect estimates. We discuss the study’s limitations, including its restriction to a single population and marker platform, the omission of pedigree-based relationship information available in the source dataset, and the absence of genotype-by-environment interaction modeling, and outline a concrete agenda for extending this comparison to reaction-norm and multi-environment models.
Keywords: Genomic selection; machine learning; wheat; DArT markers; grain yield; ridge regression; random forest; cross-validation; computational genomics
References
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35. Patale, J. P., Jagadale, A. B., Mulani, A. O., & Pise, A. (2023). A Systematic survey on Estimation of Electrical Vehicle. Journal of Electronics, Computer Networking and Applied Mathematics (JECNAM) ISSN, 2799-1156.
36. Gadade, B., & Mulani, A. (2022). Automatic System for Car Health Monitoring. International Journal of Innovations in Engineering Research and Technology, 57-62.
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55. Mulani, D. A. O. (2024). A Comprehensive Survey on Semi-Automatic Solar-Powered Pesticide Sprayers for Farming. Journal of Energy Engineering and Thermodynamics (JEET) ISSN, 2815-0945.
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73. Dr. Vaishali Satish Jadhav, Dr. Shweta Sadanand Salunkhe, Dr. Geeta Salunkhe, Pranali Rajesh Yawle, Dr. Rahul S. Pol, Dr. Altaf Osman Mulani, Dr. Manish Rana, Iot Based Health Monitoring System for Human, Afr. J. Biomed. Res. Vol. 27 (September 2024).
74. Dr. Vaishali Satish Jadhav, Geeta D. Salunke, Kalyani Ramesh Chaudhari, Dr. Altaf Osman Mulani, Dr. Sampada Padmakar Thigale, Dr. Rahul S. Pol, Dr. Manish Rana, Deep Learning- Based Face Mask Recognition in Real-Time Photos and Videos, Afr. J. Biomed. Res. Vol. 27 (September 2024).
75. Altaf Osman Mulani, Electric Vehicle Parameters Estimation Using Web Portal, Recent Trends in Electronics & Communication Systems, Volume 10, Issue 3, 2023.
76. Aryan Ganesh Nagtilak, Sneha Nitin Ulegaddi, Mahesh Mane, Altaf O. Mulani, Automatic Solar Powered Pesticide Sprayer for Farming, International Journal of Microwave Engineering and Technology, Volume 9 No. 2, 2023.
77. Annasaheb S. Dandage, Vitthal R. Rupnar, Tejas A Pise, and A. O. Mulani, Real-Time Language Translation Application Using Tkinter. International Journal of Digital Communication and Analog Signals. 2025; 11(01): -p.
78. AnnaSaheb S Dandage, Vitthal R. Rupnar, Tejas A Pise, and A. O. Mulani, IoT-Powered Weather Monitoring and Irrigation Automation: Transforming Modern Farming Practices. . 2025; 11(01): -p.
79. Mulani, A.O., Kulkarni, T.M. (2025). Face Mask Detection System Using Deep Learning: A Comprehensive Survey. In: Singh, S., Arya, K.V., Rodriguez, C.R., Mulani, A.O. (eds) Emerging Trends in Artificial Intelligence, Data Science and Signal Processing. AIDSP 2023. Communications in Computer and Information Science, vol 2439. Springer, Cham. https://doi.org/10.1007/978-3-031-88759-8_3.
80. Karve, S., Gangonda, S., Birajadar, G., Godase, V., Ghodake, R., Mulani, A.O. (2025). Optimized Neural Network for Prediction of Neurological Disorders. In: Singh, S., Arya, K.V., Rodriguez, C.R., Mulani, A.O. (eds) Emerging Trends in Artificial Intelligence, Data Science and Signal Processing. AIDSP 2023. Communications in Computer and Information Science, vol 2440. Springer, Cham. https://doi.org/10.1007/978-3-031-88762-8_18.
81. Saurabh Singh, Karm Veer Arya, Ciro Rodriguez Rodriguez, and Altaf Osman Mulani, Emerging Trends in Artificial Intelligence, Data Science and Signal Processing, Communications in Computer and Information Science (CCIS), volume 2440.
82. Saurabh Singh, Karm Veer Arya, Ciro Rodriguez Rodriguez, and Altaf Osman Mulani, Emerging Trends in Artificial Intelligence, Data Science and Signal Processing, Communications in Computer and Information Science (CCIS), volume 2439.
83. Godase, V., Mulani, A., Pawar, A., & Sahani, K. (2025). A Comprehensive Review on PIR Sensor-Based Light Automation Systems. International Journal of Image Processing and Smart Sensors, 1(1), 22-29.
84. Godase, V., Mulani, A., Takale, S., & Ghodake, R. (2025). Comprehensive Review on Automated Field Irrigation using Soil Image Analysis and IoT. Journal of Advance Electrical Engineering and Devices, 3(1), 46-55.
85. Altaf Osman Mulani, Deshmukh M., Jadhav V., Chaudhari K., Mathew A.A., Shweta Salunkhe. Transforming Drug Therapy with Deep Learning: The Future of Personalized Medicine. Drug Research. 2025 Aug 29.
86. Altaf O. Mulani, Vaibhav V. Godase, Swapnil R. Takale, Rahul G. Ghodake (2025), Image Authentication Using Cryptography and Watermarking, International Journal of Image Processing and Smart Sensors, Vol. 1, Issue 2, pp 27-34.
87. Altaf O. Mulani, Vaibhav V. Godase, Swapnil R. Takale, Rahul G. Ghodake (2025), Advancements in Artificial Intelligence: Transforming Industries and Society, International Journal of Artificial Intelligence of Things (AIoT) in Communication Industry, Vol. 1, Issue 2, pp 1-5.
88. Altaf O. Mulani, Vaibhav V. Godase, Swapnil R. Takale, Rahul G. Ghodake (2025), AI- Powered Predictive Analytics in Healthcare: Revolutionizing Disease Diagnosis and Treatment, Journal of Advance Electrical Engineering and Devices, Vol. 3, Issue 2, pp 27-34.
89. Godase, V., Mulani, A., Takale, S., & Ghodake, R. (2025). A Holistic Review of Automatic Drip Irrigation Systems: Foundations and Emerging Trends. Available at SSRN 5247778.
90. V. Godase, R. Ghodake, S. Takale, and A. Mulani, Design and Optimization of Reconfigurable Microwave Filters Using AI Techniques, International Journal of RF and Microwave Communication Technologies, vol. 2, no. 2, pp.26–41, Aug. 2025.
91. V. Godase, A. Mulani, R. Ghodake, S. Takale, “Automated Water Distribution Management and Leakage Mitigation Using PLC Systems,” Journal of Control and Instrumentation Engineering, vol.11, no. 3, pp. 1-8, Aug. 2025.
92. V. Godase, A. Mulani, R. Ghodake, S. Takale, “PLC-Assisted Smart Water Distribution with Rapid Leakage Detection and Isolation,” Journal of Control Systems and Converters, vol. 1, no. 3, pp. 1-13, Aug. 2025.
93. V. V. Godase, S. R. Takale, R. G. Ghodake, and A. Mulani, “Attention Mechanisms in Semantic Segmentation of Remote Sensing Images” Journal of Advancement in Electronics Signal Processing, vol. 2, no. 2, pp. 45–58, Aug. 2025.
94. Dr. Altaf Mulani. Design and Implementation of 256-bit Vedic Multiplier on Reconfigurable Platform. Journal of Materials & Metallurgical Engineering. 2025; 15(03). Available from: https://journals.stmjournals.com/jomme/article=2025/view=230585
95. Altaf Osman Mulani, Vaibhav Godase and Swapnil Takale. Enhanced Multimodal Disease Prediction Using Hybrid Ensemble Learning and AutoML Techniques. Research and Reviews: Journal of Computational Biology. 2025; 15(01). Available from: https://journals.stmjournals.com/rrjocb/article=2025/view=234676
96. Mulani A.O., Godase V.V., Takale S.R., Ghodake R.G. Ethical Challenges and Governance of AI in Healthcare, Education, Finance, and Security Sectors. Advances in Computational Intelligence and Robotics. 2025 Nov 14; 123–154. Available from: https://www.igi- global.com/chapter/ethical-challenges-and-governance-of-ai-in-healthcare-education- finance-and-security-sectors/396083
97. Altaf O. Mulani. Early Alzheimer’s Disease Detection Using Deep Ensemble Learning and MRI Image Analysis. Research and Reviews: Journal of Computational Biology. 2026; 15(01).
98. A. O. Mulani, “A Robust Image Watermarking Framework Integrated with AES Encryption for Secure Digital Media Protection,” Journal of Advancement in Electronics Signal Processing, vol. 2, no. 3, pp. 47-56, Dec. 2025.
99. Kedar, S., & Mulani, A. O. (2024). IoT Based Soil. Water and Air Quality Monitoring System for Pomegranate Farming, NATURALISTA CAMPANO, 28(1).
100. Dhanawade, A. and Mulani, A. O. (2024). Smart farming using IOT based Agri BOT. Naturalista Campano 28 (1), 723-729.
101. Salunkhe Shweta, Mulani, Altaf Osman , Shahane, Deepali , Rana, Manish , Shukla, Shivam Mahendra & Jadhav, Makarand M. (2026) Secure image transmission using chaotic encryption and DWT watermarking on reconfigurable platform, Journal of Discrete Mathematical Sciences and Cryptography, pp. 1-13, DOI: 10.47974/JDMSC-2608
102. Chaudhari Kalyani R., Mulani Altaf O., Gajare Milind P., Jadhav Vaishali, Yawle Pranali & Bang Arti Vasant (2026) Bit error rate analysis of various error correction codes with concatenated RS-convolutional codes, Journal of Discrete Mathematical Sciences and Cryptography, pp. 1-16, DOI: 10.47974/JDMSC-2401
103. Altaf Osman Mulani. Optimized Hardware Realization of AES for High-Throughput FPGA Platforms. International Journal of VLSI Circuit Design & Technology. 2025; 03(02), Available from: https://journals.stmjournals.com/ijvcdt/article=2025/view=235624
104. Kambale, K.S., Sawant, N.M., Mulani, A.O., More, V.P., Zambare, S.A. (2026). RNN- LSTM Based Model for Automatic Heart Disease Prediction Using the UCI Heart Disease Dataset. In: Kumar, A., Gunjan, V.K., Senatore, S., Hu, YC. (eds) Proceedings of the 6th International Conference on Data Science, Machine Learning and Applications- Volume 1. ICDSMLA2024 2024. Lecture Notes in Electrical Engineering, vol 1528. Springer, Singapore. https://doi.org/10.1007/978-981-95-5831-5_28
105. Sawant, N.M., Mulani, A.O., Kondooru, S., Linge, S.G., Gawande, P.G., Koli, M.S. (2026). AgriRent: Renting the Farm Equipment. In: Kumar, A., Gunjan, V.K., Senatore, S., Hu, YC. (eds) Proceedings of the 6th International Conference on Data Science, Machine Learning and Applications- Volume 1. ICDSMLA2024 2024. Lecture Notes in Electrical Engineering, vol 1528. Springer, Singapore. https://doi.org/10.1007/978-981-95-5831-5_35
106. Mulani, A.O., Karande, K.J. (2026). Precision Farming with a Solar-Powered Automated Pesticide Sprayer. In: Kumar, A., Ghinea, G., Merugu, S. (eds) Proceedings of the 4th International Conference on Cognitive and Intelligent Computing—Volume 2. ICCIC 2024. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-95- 0144-1_26
Research and Reviews : Journal of Computational Biology
| Volume | 15 | |
| 02 | ||
| Received | 13/07/2026 | |
| Accepted | 21/07/2026 | |
| Published | 02/08/2026 | |
| Publication Time | 20 Days |