Akshai Parakkal Sreenivasan

The MassBank contributions of the mFam collaboration

Ahlendorf, Anusha; Aharoni, Asaph; Vahabi, Khabat; Fischer, Tillmann G. et al.

Part of Metabolomics, 2026

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Data-driven insights into the biological functions of chemicals and the progression of multiple sclerosis: A machine learning approach

Parakkal Sreenivasan, Akshai

2026

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Co-exposure to PFAS and hydroxylated PCBs is associated with increased odds of multiple sclerosis

Vaivade, Aina; Parakkal Sreenivasan, Akshai; Erngren, Ida; Freyhult, Eva et al.

Part of Environment International, 2026

• DOI for Co-exposure to PFAS and hydroxylated PCBs is associated with increased odds of multiple sclerosis
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Targeted CSF metabolomics and conformal prediction improve diagnostic accuracy of normal pressure hydrocephalus

Hofling, Ulrika; Jakobsson, Jenny; Erngren, Ida; Ekman, Oskar et al.

Part of Fluids and Barriers of the CNS, 2026

• DOI for Targeted CSF metabolomics and conformal prediction improve diagnostic accuracy of normal pressure hydrocephalus
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Conformal prediction enables disease course prediction and allows individualized diagnostic uncertainty in multiple sclerosis

Sreenivasan, Akshai Parakkal; Vaivade, Aina; Noui, Yassine; Emami Khoonsari, Payam et al.

Part of npj Digital Medicine, 2025

• DOI for Conformal prediction enables disease course prediction and allows individualized diagnostic uncertainty in multiple sclerosis
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The MassBank contributions of the mFam collaboration

Ahlendorf, Anusha; Aharoni, Asaph; Vahabi, Khabat; Fischer, Tillmann G. et al.

Part of Metabolomics, 2026

• DOI for The MassBank contributions of the mFam collaboration
• Download full text (pdf) of The MassBank contributions of the mFam collaboration

Co-exposure to PFAS and hydroxylated PCBs is associated with increased odds of multiple sclerosis

Vaivade, Aina; Parakkal Sreenivasan, Akshai; Erngren, Ida; Freyhult, Eva et al.

Part of Environment International, 2026

• DOI for Co-exposure to PFAS and hydroxylated PCBs is associated with increased odds of multiple sclerosis
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Targeted CSF metabolomics and conformal prediction improve diagnostic accuracy of normal pressure hydrocephalus

Hofling, Ulrika; Jakobsson, Jenny; Erngren, Ida; Ekman, Oskar et al.

Part of Fluids and Barriers of the CNS, 2026

• DOI for Targeted CSF metabolomics and conformal prediction improve diagnostic accuracy of normal pressure hydrocephalus
• Download full text (pdf) of Targeted CSF metabolomics and conformal prediction improve diagnostic accuracy of normal pressure hydrocephalus

Conformal prediction enables disease course prediction and allows individualized diagnostic uncertainty in multiple sclerosis

Sreenivasan, Akshai Parakkal; Vaivade, Aina; Noui, Yassine; Emami Khoonsari, Payam et al.

Part of npj Digital Medicine, 2025

• DOI for Conformal prediction enables disease course prediction and allows individualized diagnostic uncertainty in multiple sclerosis
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Combining molecular and cell painting image data for mechanism of action prediction

Tian, Guangyan; Harrison, Philip J.; Sreenivasan, Akshai P.; Carreras-Puigvert, Jordi et al.

Part of Artificial intelligence in the life sciences, 2023

• DOI for Combining molecular and cell painting image data for mechanism of action prediction
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Combining molecular and cell painting image data for mechanism of action prediction

Tian, Guangyan; Harrison, Philip J.; Sreenivasan, Akshai P.; Carreras-Puigvert, Jordi et al.

Part of Artificial Intelligence in the Life Sciences, 2023

• DOI for Combining molecular and cell painting image data for mechanism of action prediction
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Combining the targeted and untargeted screening of environmental contaminants reveals associations between PFAS exposure and vitamin D metabolism in human plasma.

Carlsson, Henrik; Sreenivasan, Akshai P.; Erngren, Ida; Larsson, Anders et al.

Part of Environmental Science, p. 1116-1130, 2023

• DOI for Combining the targeted and untargeted screening of environmental contaminants reveals associations between PFAS exposure and vitamin D metabolism in human plasma.
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Predicting protein network topology clusters from chemical structure using deep learning

Sreenivasan, Akshai P.; Harrison, Philip J; Schaal, Wesley; Matuszewski, Damian J. et al.

Part of Journal of Cheminformatics, 2022

• DOI for Predicting protein network topology clusters from chemical structure using deep learning
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Data-driven insights into the biological functions of chemicals and the progression of multiple sclerosis: A machine learning approach

Parakkal Sreenivasan, Akshai

2026

• Download full text (pdf) of Data-driven insights into the biological functions of chemicals and the progression of multiple sclerosis: A machine learning approach

Identifying transition to secondary progressive multiple sclerosis across international registries using uncertainty-aware machine learning

Parakkal Sreenivasan, Akshai; Noui, Yassine; Erngren, Ida; Spjuth, Ola et al.

Prospective validation of a machine learning model for early identification of secondary progressive multiple sclerosis

Parakkal Sreenivasan, Akshai; Noui, Yassine; Erngren, Ida; Spjuth, Ola et al.