Biomedical　relation　extraction　aims　to　identify　underlying　relationships　among　entities,　such　as　gene　associations　and　drug　interactions,　within　biomedical　texts.　Despite　advancements　in　relation　extraction　in　general　knowledge　domains,　the　scarcity　of　labeled　training　data　remains　a　significant　challenge　in　the　biomedical　field.　This　paper　provides　a　novel　approach　for　biomedical　relation　extraction　that　leverages　a　noisy　student　self-training　strategy　combined　with　negative　learning.　This　method　addresses　the　challenge　of　data　insufficiency　by　utilizing　distantly　supervised　data　to　generate　high-quality　labeled　samples.　Negative　learning,　as　opposed　to　traditional　positive　learning,　offers　a　more　robust　mechanism　to　discern　and　relabel　noisy　samples,　preventing　model　overfitting.　The　integration　of　these　techniques　ensures　enhanced　noise　reduction　and　relabeling　capabilities,　leading　to　improved　performance　even　with　noisy　datasets.　Experimental　results　demonstrate　the　effectiveness　of　the　proposed　framework　in　mitigating　the　impact　of　noisy　data　and　outperforming　existing　benchmarks.