Small-Sample-Size Trait Imputation Using Deep-Learning Techniques.

In this study, we introduce Dual-Branch BioTraitNet, a deep-learning model tailored for trait imputation in small-sample ecological and biological datasets. By combining unsupervised and supervised learning strategies, the model jointly leverages quantitative and qualitative trait information. Its dual-branch architecture enables efficient learning under data-sparse conditions and generalizes well across diverse taxa. On the lizard dataset, the model achieved R2 values of 0.862 for mean body length and 0.67 for average body weight; on the fish dataset, R2 values for maximum body length, minimum spawning temperature, and egg diameter were 0.876, 0.402, and 0.496, respectively. Unlike conventional approaches such as K-nearest neighbors (KNN) and genetic algorithms (and their variants), which are often prone to overfitting or underfitting, BioTraitNet demonstrates strong predictive stability and robustness. This is evident in its consistent avoidance of negative R2 values. Notably, it maintains high accuracy even without incorporating phylogenetic information, making it particularly suitable for scenarios where evolutionary data are missing or uncertain. The proposed framework offers a flexible and reliable solution for addressing missing trait data in ecological and evolutionary research. The computational Python code was available from https://github.com/BB-yu/Dual-Branch-BioTraitNet.