Search Results for author:
Found 12 papers, 7 papers with code
JESTR: Joint Embedding Space Technique for Ranking Candidate Molecules for the Annotation of Untargeted Metabolomics Data
Results: We introduce in this paper a novel paradigm (JESTR) for annotation.
MassSpecGym: A benchmark for the discovery and identification of molecules
To address this problem, we propose MassSpecGym -- the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data.
De novo molecule generation from MS/MS spectrum
De novo molecule generation from MS/MS spectrum (bonus chemical formulae)
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On Separate Normalization in Self-supervised Transformers
Self-supervised training methods for transformers have demonstrated remarkable performance across various domains.
Ensemble Spectral Prediction (ESP) Model for Metabolite Annotation
We show that annotation performance, for ESP and other models, is a strong function of the number of molecules in the candidate set and their similarity to the target molecule.
CSI: Contrastive Data Stratification for Interaction Prediction and its Application to Compound-Protein Interaction Prediction
Accurately predicting the likelihood of interaction between two objects (compound-protein sequence, user-item, author-paper, etc.)
Few-shot graph link prediction with domain adaptation
In this work, we propose an adversarial training based modification to the current state-of-the-arts link prediction method to solve this problem.
Boost-RS: Boosted Embeddings for Recommender Systems and its Application to Enzyme-Substrate Interaction Prediction
We show that each of our auxiliary tasks boosts learning of the embedding vectors, and that contrastive learning using Boost-RS outperforms attribute concatenation and multi-label learning.
Stochastic Iterative Graph Matching
In this work, we propose a new model, Stochastic Iterative Graph MAtching (SIGMA), to address the graph matching problem.
Using Graph Neural Networks for Mass Spectrometry Prediction
We compare our results to NEIMS, a neural network model that utilizes molecular fingerprints as inputs.
Enzyme promiscuity prediction using hierarchy-informed multi-label classification
Some interactions are attributed to natural selection and involve the enzyme's natural substrates.
Learning graph representations of biochemical networks and its application to enzymatic link prediction
We develop in this work a technique, Enzymatic Link Prediction (ELP), for predicting the likelihood of an enzymatic transformation between two molecules.
Pathway-Activity Likelihood Analysis and Metabolite Annotation for Untargeted Metabolomics using Probabilistic Modeling
Annotation results are in agreement to those obtained using other tools that utilize additional information in the form of spectral signatures.
