A critical review of machine-learning for “multi-omics” marine metabolite datasets

Manochkumar, Janani; Cherukuri, Aswani Kumar; Kumar, Raju Suresh; Almansour, Abdulrahman I.; Ramamoorthy, Siva; Efferth, Thomas

doi:10.1016/j.compbiomed.2023.107425

Cited by 5 publications

(2 citation statements)

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“…Marine biotechnological research is progressing swiftly, with a burgeoning interest in utilizing multi-omics approaches and machine learning techniques to analyze marine metabolite datasets (Manochkumar et al 2023). The developed integrated ML model harnesses the complementary strengths of the basic models to minimize the occurrence of random errors, thereby enhancing the reliability of its predictions.…”

Section: Discussionmentioning

confidence: 99%

“…The advancements in ML and Artificial intelligence (AI) algorithms have profoundly contributed to the easy search for novel natural product-based drug discovery in the 21 st century (Manochkumar and Ramamoorthy 2024). Recently, numerous omics-related datasets have been developed for diverse species of marine organisms and the need to develop and integrate ML algorithms for multi-omics studies has been extensively reviewed (Manochkumar et al 2023). In crop breeding research, multimodal data from three sensors coupled with ML algorithms were efficiently used in a study for the estimation of the crop harvest index of Faba bean and Pea (Ji et al 2024).…”

Section: Introductionmentioning

confidence: 99%

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Enhancing cellular production of fucoxanthin through machine learning assisted predictive approach inIsochrysis galbana

Manochkumar,

Jonnalagadda,

Cherukuri

et al. 2024

Preprint

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The marine microalgae, Isochrysis galbana is a prolific producer of fucoxanthin which is a xanthophyll carotenoid with substantial global market value boasting extensive applications in the food, nutraceutical, pharmaceutical, and cosmetic industries. Although supplementation of different phytohormones to medium enhances fucoxanthin production, the quantification of pigment by conventional means is time-consuming and labor-intensive. This study addressed the multiple methodological limitations of HPLC-based fucoxanthin quantification and emphasized the need to develop a Machine Learning (ML) model as optimization and precise prediction remain a challenging task. Hence, an integrated experimental approach coupled with ML models was employed to predict fucoxanthin yield by supplementation of various phytohormones. The accuracy of fucoxanthin prediction excluding and including hormone descriptors was compared and evaluated using the ML models namely Random Forest (RF), Linear Regression (LR), Artificial Neural Network (ANN), and Support Vector Machine (SVM). RF model provided the most accurate prediction excluding hormone descriptors with coefficient of determination (R^2=0.809) and root-mean-square error (RMSE=0.776) followed by the ANN model with (R^2=0.722) and (RMSE=0.937). The inclusion of hormone descriptors for training and pre-processing of data further improved the fucoxanthin prediction accuracy of the RF model to (R^2=0.839) and (RMSE=0.712) and ANN model to (R^2=0.738) and (RMSE=0.909). These results indicated that the combination of low-cost, Ultraviolet (UV) spectrometric-based fucoxanthin quantification coupled with ML algorithms can be efficiently used for reliable prediction and enhanced fucoxanthin production, therefore highlighting a promising approach and furnishing invaluable insights towards the commercialization of microalgal fucoxanthin production.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%