Analyzing the Role and Optimization of Loss Functions in Machine Learning and Deep Learning

Abstract

Trinh Quang Minh, Ngo Thi Lan, Nguyen Minh Hieu, Tran Minh Tan, Do Chi Tam, Lam Tan Phuong, Nguyen Chi Cuong and Bui Xuan Tung

In the field of machine learning and deep learning, the loss function serves as a critical mathematical frame-work for quantifying the discrepancy between predicted outputs and ground-truth labels. This study provides a comprehensive analysis of loss function optimization across diverse and complex biological datasets, ranging from multi-label protein function prediction (CAFA 6) to high-dimensional RNA 3D structure modeling (Stan-ford RNA 3D Folding). Our methodology integrates advanced feature engineering—including K-mer tokeni-zation, TF-IDF vectorization, and Truncated SVD—with specialized modeling strategies such as Template-Based Modeling (TBM) and sequence alignment. For categorical tasks, we demonstrate the efficacy of One-vs-Rest (OvR) strategies utilizing Binary Cross- Entropy (BCE) to manage large-scale Gene Ontology (GO) terms. In the domain of structural biology, the study explores the transition to spatial coordinate regression, where the optimization objective focuses on the precise positioning of C1' atoms to define molecular back-bones. Experimental results, validated on Kaggle competition benchmarks, show that appropriate loss func-tion selection and fine-tuning significantly enhance model convergence and prediction reliability, achieving a confidence threshold of over 0.85 for high-frequency biological annotations. This research underscores the pivotal role of loss functions in bridging the gap between raw genomic sequences and functional 3D molecu-lar insights.

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