Network Modeling and Pathway Inference from Incomplete Data ("PathInf")

TL;DR

This paper introduces PathInf, a two-stage network inference method designed to handle incomplete data, which successfully infers pathways in complex biological systems and reveals novel insights into lymph node metastasis.

Contribution

PathInf is a novel two-stage inference model that effectively manages massive missing data and infers biological pathways, outperforming existing Bayesian network methods.

Findings

Superior performance on simulation data compared to Bayesian networks.

Discovery of jumping metastasis pathways among lymph nodes.

Potential to improve clinical diagnosis and treatment planning.

Abstract

In this work, we developed a network inference method from incomplete data ("PathInf") , as massive and non-uniformly distributed missing values is a common challenge in practical problems. PathInf is a two-stages inference model. In the first stage, it applies a data summarization model based on maximum likelihood to deal with the massive distributed missing values by transforming the observation-wise items in the data into state matrix. In the second stage, transition pattern (i.e. pathway) among variables is inferred as a graph inference problem solved by greedy algorithm with constraints. The proposed method was validated and compared with the state-of-art Bayesian network method on the simulation data, and shown consistently superior performance. By applying the PathInf on the lymph vascular metastasis data, we obtained the holistic pathways of the lymph node metastasis with novel discoveries on the jumping metastasis among nodes that are physically apart. The discovery indicates the possible presence of sentinel node groups in the lung lymph nodes which have been previously speculated yet never found. The pathway map can also improve the current dissection examination protocol for better individualized treatment planning, for higher diagnostic accuracy and reducing the patients trauma.