Utilizing cost-sensitive machine learning classifiers to identify compounds that inhibit Alzheimer's APP translation

Alashwal, Hany; Lucman, Juwayni

doi:10.1145/3416921.3416931

Proceedings of the 2020 4th International Conference on Cloud and Big Data Computing 2020

DOI: 10.1145/3416921.3416931

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Utilizing cost-sensitive machine learning classifiers to identify compounds that inhibit Alzheimer's APP translation

Hany Alashwal

Juwayni Lucman

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2024

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Balancing Imbalanced Toxicity Models: Using MolBERT with Focal Loss

Masood,

Kaski,

Ceulemans

et al. 2024

Lecture Notes in Computer Science

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Drug-induced liver injury (DILI) presents a multifaceted challenge, influenced by interconnected biological mechanisms. Current DILI datasets are characterized by small sizes and high imbalance, posing difficulties in learning robust representations and accurate modeling. To address these challenges, we trained a multi-modal multi-task model integrating preclinical histopathologies, biochemistry (blood markers), and clinical DILI-related adverse drug reactions (ADRs). Leveraging pretrained BERT models, we extracted representations covering a broad chemical space, facilitating robust learning in both frozen and fine-tuned settings. To address imbalanced data, we explored weighted Binary Cross-Entropy (w-BCE) and weighted Focal Loss (w-FL) . Our results demonstrate that the frozen BERT model consistently enhances performance across all metrics and modalities with weighted loss functions compared to their non-weighted counterparts. However, the efficacy of fine-tuning BERT varies across modalities, yielding inconclusive results. In summary, the incorporation of BERT features with weighted loss functions demonstrates advantages, while the efficacy of fine-tuning remains uncertain.

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Balancing Imbalanced Toxicity Models: Using MolBERT with Focal Loss

Masood,

Kaski,

Ceulemans

et al. 2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

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Utilizing cost-sensitive machine learning classifiers to identify compounds that inhibit Alzheimer's APP translation

Cited by 1 publication

References 15 publications

Balancing Imbalanced Toxicity Models: Using MolBERT with Focal Loss

Balancing Imbalanced Toxicity Models: Using MolBERT with Focal Loss

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