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Found 7 papers, 3 papers with code
SBSM-Pro: Support Bio-sequence Machine for Proteins
We introduce the Support Bio-Sequence Machine for Proteins (SBSM-Pro), a model purpose-built for the classification of biological sequences.
iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations
In this study, we propose iDNA-ABF, a multi-scale deep biological language learning model that enables the interpretable prediction of DNA methylations based on genomic sequences only.
MechRetro is a chemical-mechanism-driven graph learning framework for interpretable retrosynthesis prediction and pathway planning
Leveraging artificial intelligence for automatic retrosynthesis speeds up organic pathway planning in digital laboratories.
Reference-Based Sequence Classification
By utilizing this framework as a tool, we propose new sequence classification algorithms that are quite different from existing solutions.
Instance-Based Classification through Hypothesis Testing
After these two steps, we have two p-values for each test instance and the test instance is assigned to the class associated with the smaller p-value.
HPSLPred: An Ensemble Multi-label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source
Predicting the subcellular localization of proteins is an important and challenging problem.
Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy
TATA-binding protein (TBP) is a kind of DNA binding protein, which plays a key role in the transcription regulation.
