Incorporating network based protein complex discovery into automated model construction

• URL: http://arxiv.org/abs/2010.00387v1
• Date: Tue, 29 Sep 2020 18:46:33 GMT
• Title: Incorporating network based protein complex discovery into automated model construction
• Authors: Paul Scherer, Maja Tr\c{e}bacz, Nikola Simidjievski, Zohreh Shams, Helena Andres Terre, Pietro Li\`o, Mateja Jamnik
• Abstract summary: We propose a method for gene expression based analysis of cancer phenotypes network incorporating knowledge through unsupervised construction of computational graphs. The structural construction of the computational graphs is driven by the use of topological clustering algorithms on protein-protein networks.
• Score: 6.587739898387445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a method for gene expression based analysis of cancer phenotypes incorporating network biology knowledge through unsupervised construction of computational graphs. The structural construction of the computational graphs is driven by the use of topological clustering algorithms on protein-protein networks which incorporate inductive biases stemming from network biology research in protein complex discovery. This structurally constrains the hypothesis space over the possible computational graph factorisation whose parameters can then be learned through supervised or unsupervised task settings. The sparse construction of the computational graph enables the differential protein complex activity analysis whilst also interpreting the individual contributions of genes/proteins involved in each individual protein complex. In our experiments analysing a variety of cancer phenotypes, we show that the proposed methods outperform SVM, Fully-Connected MLP, and Randomly-Connected MLPs in all tasks. Our work introduces a scalable method for incorporating large interaction networks as prior knowledge to drive the construction of powerful computational models amenable to introspective study.

Related papers

• Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types [50.55583697209676]
  We develop an attention-enhanced graph autoencoder, which is designed to efficiently capture the topological features between cells. During the clustering process, we integrated both sets of information and reconstructed the features of both cells and genes to generate a discriminative representation. This research offers enhanced insights into the characteristics and distribution of cells, thereby laying the groundwork for early diagnosis and treatment of diseases.
  arXiv  Detail & Related papers  (2023-11-28T09:14:55Z)
• Neural Embeddings for Protein Graphs [0.8258451067861933]
  We propose a novel framework for embedding protein graphs in geometric vector spaces. We learn an encoder function that preserves the structural distance between protein graphs. Our framework achieves remarkable results in the task of protein structure classification.
  arXiv  Detail & Related papers  (2023-06-07T14:50:34Z)
• Generative Pretrained Autoregressive Transformer Graph Neural Network applied to the Analysis and Discovery of Novel Proteins [0.0]
  We report a flexible language-model based deep learning strategy, applied here to solve complex forward and inverse problems in protein modeling. The model is applied to predict secondary structure content (per-residue level and overall content), protein solubility, and sequencing tasks. We find that adding additional tasks yields emergent synergies that the model exploits in improving overall performance.
  arXiv  Detail & Related papers  (2023-05-07T12:30:24Z)
• A Systematic Study of Joint Representation Learning on Protein Sequences and Structures [38.94729758958265]
  Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein functions. Recent sequence representation learning methods based on Protein Language Models (PLMs) excel in sequence-based tasks, but their direct adaptation to tasks involving protein structures remains a challenge. Our study undertakes a comprehensive exploration of joint protein representation learning by integrating a state-of-the-art PLM with distinct structure encoders.
  arXiv  Detail & Related papers  (2023-03-11T01:24:10Z)
• Integration of Pre-trained Protein Language Models into Geometric Deep Learning Networks [68.90692290665648]
  We integrate knowledge learned by protein language models into several state-of-the-art geometric networks. Our findings show an overall improvement of 20% over baselines. Strong evidence indicates that the incorporation of protein language models' knowledge enhances geometric networks' capacity by a significant margin.
  arXiv  Detail & Related papers  (2022-12-07T04:04:04Z)
• State-specific protein-ligand complex structure prediction with a multi-scale deep generative model [68.28309982199902]
  We present NeuralPLexer, a computational approach that can directly predict protein-ligand complex structures. Our study suggests that a data-driven approach can capture the structural cooperativity between proteins and small molecules, showing promise in accelerating the design of enzymes, drug molecules, and beyond.
  arXiv  Detail & Related papers  (2022-09-30T01:46:38Z)
• Learning Geometrically Disentangled Representations of Protein Folding Simulations [72.03095377508856]
  This work focuses on learning a generative neural network on a structural ensemble of a drug-target protein. Model tasks involve characterizing the distinct structural fluctuations of the protein bound to various drug molecules. Results show that our geometric learning-based method enjoys both accuracy and efficiency for generating complex structural variations.
  arXiv  Detail & Related papers  (2022-05-20T19:38:00Z)
• Structure-aware Protein Self-supervised Learning [50.04673179816619]
  We propose a novel structure-aware protein self-supervised learning method to capture structural information of proteins. In particular, a well-designed graph neural network (GNN) model is pretrained to preserve the protein structural information. We identify the relation between the sequential information in the protein language model and the structural information in the specially designed GNN model via a novel pseudo bi-level optimization scheme.
  arXiv  Detail & Related papers  (2022-04-06T02:18:41Z)
• PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction [0.07340017786387766]
  In this work, we isolate protein structure to make functional annotations for proteins in the Protein Data Bank. We present PersGNN - an end-to-end trainable deep learning model that combines graph representation learning with topological data analysis.
  arXiv  Detail & Related papers  (2020-10-30T02:24:35Z)
• Transfer Learning for Protein Structure Classification at Low Resolution [124.5573289131546]
  We show that it is possible to make accurate ($geq$80%) predictions of protein class and architecture from structures determined at low ($leq$3A) resolution. We provide proof of concept for high-speed, low-cost protein structure classification at low resolution, and a basis for extension to prediction of function.
  arXiv  Detail & Related papers  (2020-08-11T15:01:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.