Indian Institute of Technology Madras

Enzyme function prediction has relied on homology search or on mapping enzymes to known reactions, so it cannot output a reaction absent from its training data. A model conditioned on enzyme sequence and AlphaFold structure and generating a reaction metabolite by metabolite recovers the complete correct reaction for 25.0% of metabolite-recombination test pairs within 400 sampled candidates on the reaction-disjoint EnzymeMap_x benchmark, and for only 1.6% of pairs containing a metabolite unseen in training. A matched ablation finds no measurable effect of the structural channel on exact recovery; vocabulary coverage and chemical initialisation account for most of the gain.

Enzyme function prediction has relied on homology search or on mapping enzymes to known reactions, so it cannot output a reaction absent from its training data. A model conditioned on enzyme sequence and AlphaFold structure and generating a reaction metabolite by metabolite recovers the complete correct reaction for 25.0% of metabolite-recombination test pairs within 400 sampled candidates on the reaction-disjoint EnzymeMap_x benchmark, and for only 1.6% of pairs containing a metabolite unseen in training. A matched ablation finds no measurable effect of the structural channel on exact recovery; vocabulary coverage and chemical initialisation account for most of the gain.

Training data for wearable fall detection is compressed into a very small class-balanced synthetic set. Earlier pipelines either used hand-crafted features with shallow classifiers or trained deep networks on the full imbalanced corpus with re-sampling, at high data and compute cost. FallNest aligns synthetic and real samples in both time and frequency domains through gradient direction and embedding distribution, initialized by class-balanced K-Means and augmented with multi-view Fourier views. On SisFall, KFall and MobiFall under subject-wise splits with naturally imbalanced test sets, a 102K-parameter TinyCNN trained on only 32 to 146 synthetic windows reaches 93.5 to 95.8 percent mean accuracy and 0.89 to 0.98 PR-AUC over five seeds.

Training data for wearable fall detection is compressed into a very small class-balanced synthetic set. Earlier pipelines either used hand-crafted features with shallow classifiers or trained deep networks on the full imbalanced corpus with re-sampling, at high data and compute cost. FallNest aligns synthetic and real samples in both time and frequency domains through gradient direction and embedding distribution, initialized by class-balanced K-Means and augmented with multi-view Fourier views. On SisFall, KFall and MobiFall under subject-wise splits with naturally imbalanced test sets, a 102K-parameter TinyCNN trained on only 32 to 146 synthetic windows reaches 93.5 to 95.8 percent mean accuracy and 0.89 to 0.98 PR-AUC over five seeds.

This work develops a method to infer a Corpus-Wide Causal Score (CWCS) for a gene-disease pair by integrating network-based causal signals in a gene regulatory network (CWCS-Net) with corpus-wide literature evidence from PubMed abstracts quantified by a newly developed Truth Discovery algorithm (CWCS-TD), achieving a causal class F1 score of 0.600 across ten diseases using OMIM as an external expert-curated reference, outperforming GPT-4o (0.505) and MMed-Llama 3 (0.522).

This work develops a method to infer a Corpus-Wide Causal Score (CWCS) for a gene-disease pair by integrating network-based causal signals in a gene regulatory network (CWCS-Net) with corpus-wide literature evidence from PubMed abstracts quantified by a newly developed Truth Discovery algorithm (CWCS-TD), achieving a causal class F1 score of 0.600 across ten diseases using OMIM as an external expert-curated reference, outperforming GPT-4o (0.505) and MMed-Llama 3 (0.522).