Protein–Protein Interactions Prediction via Multimodal Deep Polynomial Network and Regularized Extreme Learning Machine

Abstract

Predicting the protein-protein interactions (PPIs) has played an important role in many applications. Hence, a novel computational method for PPIs prediction is highly desirable. PPIs endow with protein amino acid mutation rate and two physicochemical properties of protein (e.g., hydrophobicity, and hydrophilicity). Deep polynomial network (DPN) is well-suited to integrate these modalities since it can represent any function on a finite sample dataset via the supervised deep learning algorithm. We propose a multimodal DPN (MDPN) algorithm to effectively integrate these modalities to enhance prediction performance. MDPN consists of a two-stage DPN, the first stage feeds multiple protein features into DPN encoding to obtain high-level feature representation while the second stage fuses and learns features by cascading three types of high-level features in the DPN encoding. We employ a regularized extreme learning machine (RELM) to predict PPIs. The proposed method is tested on the public dataset of H. pylori, Human, and Yeast and achieves average accuracies of 97.87%, 99.90%, and 98.11%, respectively. The proposed method also achieves good accuracies on other datasets. Furthermore, we test our method on three kinds of PPI networks and obtain superior prediction results.

Machine Learning and Integrative Analysis of Biomedical Big Data

“…Specifically, convex optimization, a key competent in training many ML and statistical models, is being reinvented for scalability and parallelism in the wake of big data [ 193 ]. Recently, methods based on ELM theory have been employed in single omics studies [ 163 , 182 , 194 , 195 , 196 , 197 ] and may be extended to multi-omics for efficient integrative analyses. Moreover, scalable MKL methods like dual-layer kernel extreme learning machine (DKELM) [ 198 ] and easyMKL [ 199 ] can be employed in multi-omics integrative analysis since MKL, a popular approach for integrating multiple omics datasets, can be computationally very expensive for large datasets.…”

A review on extreme learning machine

“…And it seemed that ELM won again among several competitors including MLR and SVM. In 2019, Lei and Wen [80] proposed their method of predicting protein-protein interactions based on regularized extreme learning machine and claimed to achieve promising performance. Further in 2020, Li and Shi [83] also presented their modified method to predict protein-protein interactions.…”

Aplicaciones de las redes neuronales y el deep learning a la ingeniería biomédica

“…Las interacciones entre las proteínas y el ADN o ARN juegan un papel importante en diferentes procesos celulares como la transcripción, la replicación, la traducción y la reparación, que tienen objeto de estudio dentro de la farmacogenómica, ya que permiten investigar en el diseño y descubrimiento de medicamentos. Por lo tanto, dentro de la proteómica, la epigenómica y la farmacogenómica, se ha investigado en el uso de redes neuronales y deep learning para la predicción de unión de proteínas al ADN y ARN [53][54], la predicción de la interacción proteína-proteína [55][56][57], la predicción de la interacción compuesto-proteína [58][59], y la predicción de las interacciones farmacológicas, medicamento-objetivo [60][61][62].…”