Gene function finding through cross-organism ensemble learning

Abstract

Background Structured biological information about genes and proteins is a valuable resource to improve discovery and understanding of complex biological processes via machine learning algorithms. Gene Ontology (GO) controlled annotations describe, in a structured form, features and functions of genes and proteins of many organisms. However, such valuable annotations are not always reliable and sometimes are incomplete, especially for rarely studied organisms. Here, we present GeFF (Gene Function Finder), a novel cross-organism ensemble learning method able to reliably predict new GO annotations of a target organism from GO annotations of another source organism evolutionarily related and better studied. Results Using a supervised method, GeFF predicts unknown annotations from random perturbations of existing annotations. The perturbation consists in randomly deleting a fraction of known annotations in order to produce a reduced annotation set. The key idea is to train a supervised machine learning algorithm with the reduced annotation set to predict, namely to rebuild, the original annotations. The resulting prediction model, in addition to accurately rebuilding the original known annotations for an organism from their perturbed version, also effectively predicts new unknown annotations for the organism. Moreover, the prediction model is also able to discover new unknown annotations in different target organisms without retraining.We combined our novel method with different ensemble learning approaches and compared them to each other and to an equivalent single model technique. We tested the method with five different organisms using their GO annotations: Homo sapiens, Mus musculus, Bos taurus, Gallus gallus and Dictyostelium discoideum. The outcomes demonstrate the effectiveness of the cross-organism ensemble approach, which can be customized with a trade-off between the desired number of predicted new annotations and their precision.A Web application to browse both input annotations used and predicted ones, choosing the ensemble prediction method to use, is publicly available at http://tiny.cc/geff/. Conclusions Our novel cross-organism ensemble learning method provides reliable predicted novel gene annotations, i.e., functions, ranked according to an associated likelihood value. They are very valuable both to speed the annotation curation, focusing it on the prioritized new annotations predicted, and to complement known annotations available.

Efficient Memory-Enhanced Transformer for Long-Document Summarization in Low-Resource Regimes

“…The community should envisage novel graph representation learning methods [ 57 , 58 , 59 , 60 ] to densely represent multi-relational structured data following a Linked Open Data vision centered on the integration of several source knowledge graphs or relational databases via automatic entity matching [ 61 ]. Taking inspiration from biology [ 62 , 63 ] and communication networks [ 64 , 65 , 66 , 67 ], we underline the importance of managing dynamic scenarios, tracking knowledge refinements among sentences, and propagating information, which is pivotal when processing lengthy inputs. Segmentation strategies and memory-enhanced encoder–decoder transformers could be inspected in other downstream tasks with long documents and cross-dependencies among chunks, such as claim verification with evidence retrieval [ 68 , 69 ].…”