A large-scale comparative study on peptide encodings for biomedical classification

Spänig, Sebastian; Mohsen, Siba; Hattab, Georges; Hauschild, Anne-Christin; Heider, Dominik

doi:10.1093/nargab/lqab039

Cited by 19 publications

(36 citation statements)

References 42 publications

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“…Another difference is that PEPred-Suite and Peptipedia use elaborate techniques to encode the peptide sequences, and MultiPep uses a simple one-hot encoding. Many peptide encoding techniques exist and peptide encoding in general is a field that is gaining a lot of attention [ 77 , 78 ]. PEPred-Suite uses adaptive feature representation strategy, where they, among other things, use 10 feature encoding algorithms, which together efficiently capture local and global compositional information and well as position-specific residue information and physiochemical information [ 26 ].…”

Section: Discussionmentioning

confidence: 99%

“…Peptipedia encodes peptides using representations of physicochemical properties and transforms them using Fourier transforms [ 27 , 79 ]. Although some general selection rules have been suggested, it is difficult to find a single universally optimal peptide encoding technique [ 77 , 78 ]. As it has been found that deep learning models require little encoding for the classification process [ 77 ], we chose to use a simple and, in our opinion, reliable encoding technique where all amino acids are equally similar or dissimilar (one-hot encoding).…”

Section: Discussionmentioning

confidence: 99%

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MultiPep: a hierarchical deep learning approach for multi-label classification of peptide bioactivities

2021

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Peptide-based therapeutics are here to stay and will prosper in the future. A key step in identifying novel peptide-drugs is the determination of their bioactivities. Recent advances in peptidomics screening approaches hold promise as a strategy for identifying novel drug targets. However, these screenings typically generate an immense number of peptides and tools for ranking these peptides prior to planning functional studies are warranted. Whereas a couple of tools in the literature predict multiple classes, these are constructed using multiple binary classifiers. We here aimed to use an innovative deep learning approach to generate an improved peptide bioactivity classifier with capacity of distinguishing between multiple classes. We present MultiPep: a deep learning multi-label classifier that assigns peptides to zero or more of 20 bioactivity classes. We train and test MultiPep on data from several publically available databases. The same data are used for a hierarchical clustering, whose dendrogram shapes the architecture of MultiPep. We test a new loss function that combines a customized version of Matthews correlation coefficient with binary cross entropy (BCE), and show that this is better than using class-weighted BCE as loss function. Further, we show that MultiPep surpasses state-of-the-art peptide bioactivity classifiers and that it predicts known and novel bioactivities of FDA-approved therapeutic peptides. In conclusion, we present innovative machine learning techniques used to produce a peptide prediction tool to aid peptide-based therapy development and hypothesis generation.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

MultiPep: a hierarchical deep learning approach for multi-label classification of peptide bioactivities

2021

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“…For a comprehensive analysis on peptide encodings, Spänig et al . (2021) gathered a variety of datasets from multiple biomedical domains [20].…”

Section: Methodsmentioning

confidence: 99%

“…In the case at least one model is significantly different, we used the Nemenyi test for post-hoc analysis [54]. Refer also to Spänig et al . (2021) for more details [20].…”

Section: Methodsmentioning

confidence: 99%

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Unsupervised encoding selection through ensemble pruning for biomedical classification

Spaenig

Michel

Heider

2022

Preprint

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Owing to the rising levels of multi-resistant pathogens, antimicrobial peptides, an alternative strategy to classic antibiotics, got more attention. A crucial part is thereby the costly identification and validation. With the ever growing amount of annotated peptides, researchers employed artificial intelligence to circumvent the cumbersome, wet-lab-based identification and automate the detection of promising candidates. However, the prediction of a peptide's function is not limited to antimicrobial efficiency. To date, multiple studies successfully classified additional properties, e.g., antiviral or cell-penetrating effects. In this light, ensemble classifiers are employed to utilize the advantages of peptide encodings; hence, further improving the prediction. Although we recently presented a workflow to significantly diminish the initial encoding choice, an entire unsupervised encoding selection, considering various machine learning models, is still lacking. We developed a workflow, automatically selecting encodings and generating classifier ensembles by employing sophisticated pruning methods. We observed that the Pareto frontier pruning is a good method to create encoding ensembles for the datasets at hand. In addition, encodings combined with the Decision Tree classifier as the base model are often superior. However, our results also demonstrate that none of the ensemble building techniques is outstanding for all datasets. The workflow conducts multiple pruning methods to evaluate ensemble classifiers composed from a wide range of peptide encodings and base models. Consequently, researchers can use the workflow for unsupervised encoding selection and ensemble creation. Ultimately, the extensible workflow can be used as a plugin for the PEPTIDE REACToR, further establishing it as a versatile tool in the domain.

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Vision for Improving Pregnancy Health: Innovation and the Future of Pregnancy Research

et al. 2022

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Understanding, predicting, and preventing pregnancy disorders have been a major research target. Nonetheless, the lack of progress is illustrated by research results related to preeclampsia and other hypertensive pregnancy disorders. These remain a major cause of maternal and infant mortality worldwide. There is a general consensus that the rate of progress toward understanding pregnancy disorders lags behind progress in other aspects of human health. In this presentation, we advance an explanation for this failure and suggest solutions. We propose that progress has been impeded by narrowly focused research training and limited imagination and innovation, resulting in the failure to think beyond conventional research approaches and analytical strategies. Investigations have been largely limited to hypothesis-generating approaches constrained by attempts to force poorly defined complex disorders into a single “unifying” hypothesis. Future progress could be accelerated by rethinking this approach. We advise taking advantage of innovative approaches that will generate new research strategies for investigating pregnancy abnormalities. Studies should begin before conception, assessing pregnancy longitudinally, before, during, and after pregnancy. Pregnancy disorders should be defined by pathophysiology rather than phenotype, and state of the art agnostic assessment of data should be adopted to generate new ideas. Taking advantage of new approaches mandates emphasizing innovation, inclusion of large datasets, and use of state of the art experimental and analytical techniques. A revolution in understanding pregnancy-associated disorders will depend on networks of scientists who are driven by an intense biological curiosity, a team spirit, and the tools to make new discoveries.

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A large-scale comparative study on peptide encodings for biomedical classification

Cited by 19 publications

References 42 publications

MultiPep: a hierarchical deep learning approach for multi-label classification of peptide bioactivities

MultiPep: a hierarchical deep learning approach for multi-label classification of peptide bioactivities

Unsupervised encoding selection through ensemble pruning for biomedical classification

Vision for Improving Pregnancy Health: Innovation and the Future of Pregnancy Research

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