PiNUI: A Dataset of Protein–Protein Interactions for Machine Learning

Author:

Dubourg-Felonneau Geoffroy,Wesego Daniel Mitiku,Akiva Eyal,Varadan Ranjani

Abstract

AbstractWe introduce a new novel dataset namedPiNUI:ProteinInteractions withNearlyUniformImbalance. PiNUI is a dataset of Protein–Protein Interactions (PPI) specifically designed for Machine Learning (ML) applications that offer a higher degree of representativeness of real-world PPI tasks compared to existing ML-ready PPI datasets. We achieve such by increasing the data size and quality, and minimizing the sampling bias of negative interactions. We demonstrate that models trained on PiNUI almost always outperform those trained on conventional PPI datasets when evaluated on various general PPI tasks using external test sets. PiNUI is availablehere.

Publisher

Cold Spring Harbor Laboratory

Reference13 articles.

1. Using support vector machine combined with auto covariance to predict protein–protein interactions from protein sequences

2. Peer: a comprehensive and multi-task benchmark for protein sequence understanding;Advances in Neural Information Processing Systems,2022

3. Large-Scale Prediction of Human Protein−Protein Interactions from Amino Acid Sequence Based on Latent Topic Features

4. Noemi del Toro , Anjali Shrivastava , Eliot Ragueneau , Birgit Meldal , Colin Combe , Elisabet Barrera , Livia Perfetto , Karyn How , Prashansa Ratan , Gautam Shirodkar , Odilia Lu , Bálint Mészáros , Xavier Watkins , Sangya Pundir , Luana Licata , Marta Iannuccelli , Matteo Pellegrini , Maria Jesus Martin , Simona Panni , Margaret Duesbury , Sylvain D Vallet , Juri Rappsilber , Sylvie Ricard-Blum , Gianni Cesareni , Lukasz Salwinski , Sandra Orchard , Pablo Porras , Kalpana Panneerselvam , and Henning Hermjakob . The IntAct database: efficient access to fine-grained molecular interaction data. Nucleic Acids Research, 50(D1):D648–D653, November 2021.

5. Samuel Kerrien , Sandra Orchard , Luisa Montecchi-Palazzi , Bruno Aranda , Antony F Quinn , Nisha Vinod , Gary D Bader , Ioannis Xenarios , Jérôme Wojcik , David Sherman , Mike Tyers , John J Salama , Susan Moore , Arnaud Ceol , Andrew Chatr-aryamontri , Matthias Oesterheld , Volker Stümpflen , Lukasz Salwinski , Jason Nerothin , Ethan Cerami , Michael E Cusick , Marc Vidal , Michael Gilson , John Armstrong , Peter Woollard , Christopher Hogue , David Eisenberg , Gianni Cesareni , Rolf Apweiler , and Henning Hermjakob . Broadening the horizon – level 2.5 of the HUPO-PSI format for molecular interactions. BMC Biology, 5(1), October 2007.

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