Readlist of publications

This is a dump. Entries here should be sent to their respective dendron notes when treated.

Monday 14 June 2021

Functional biology in its natural context: A search for emergent simplicity

https://elifesciences.org/articles/67646

Monday 31 May 2021

A deep generative model enables automated structure elucidation of novel psychoactive substances

https://chemrxiv.org/articles/preprint/A_Deep_Generative_Model_Enables_Automated_Structure_Elucidation_of_Novel_Psychoactive_Substances/14644854/1

Thursday 27 May 2021

Multiomic Big Data Analysis Challenges: Increasing Confidence in the Interpretation of Artificial Intelligence Assessments

https://pubs.acs.org/doi/abs/10.1021/acs.analchem.0c04850

Tuesday 25 May 2021

Identifying molecules as biosignatures with assembly theory and mass spectrometry

https://www.nature.com/articles/s41467-021-23258-x

The search for alien life is hard because we do not know what signatures are unique to life. We show why complex molecules found in high abundance are universal biosignatures and demonstrate the first intrinsic experimentally tractable measure of molecular complexity, called the molecular assembly index (MA). To do this we calculate the complexity of several million molecules and validate that their complexity can be experimentally determined by mass spectrometry. This approach allows us to identify molecular biosignatures from a set of diverse samples from around the world, outer space, and the laboratory, demonstrating it is possible to build a life detection experiment based on MA that could be deployed to extraterrestrial locations, and used as a complexity scale to quantify constraints needed to direct prebiotically plausible processes in the laboratory. Such an approach is vital for finding life elsewhere in the universe or creating de-novo life in the lab.

Supervised topic modeling for predicting molecular substructure from mass spectrometry [version 1; peer review: awaiting peer review]

https://f1000research.com/articles/10-403

Small-molecule metabolites are principal actors in myriad phenomena across biochemistry and serve as an important source of biomarkers and drug candidates. Given a sample of unknown composition, identifying the metabolites present is difficult given the large number of small molecules both known and yet to be discovered. Even for biofluids such as human blood, building reliable ways of identifying biomarkers is challenging. A workhorse method for characterizing individual molecules in such untargeted metabolomics studies is tandem mass spectrometry (MS/MS). MS/MS spectra provide rich information about chemical composition. However, structural characterization from spectra corresponding to unknown molecules remains a bottleneck in metabolomics. Current methods often rely on matching to pre-existing databases in one form or another. Here we develop a preprocessing scheme and supervised topic modeling approach to identify modular groups of spectrum fragments and neutral losses corresponding to chemical substructures using labeled latent Dirichlet allocation (LLDA) to map spectrum features to known chemical structures. These structures appear in new unknown spectra and can be predicted. We find that LLDA is an interpretable and reliable method for structure prediction from MS/MS spectra. Specifically, the LLDA approach has the following advantages: (a) molecular topics are interpretable; (b) A practitioner can select any set of chemical structure labels relevant to their problem; (c ) LLDA performs well and can exceed the performance of other methods in predicting substructures in novel contexts.

Tuesday 11 May 2021