R.I.P.
👻
Ghosted
Biological Systems as Heterogeneous Information Networks: A Mini-review and Perspectives
December 24, 2017 · Declared Dead · 🏛 arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Koki Tsuyuzaki, Itoshi Nikaido
arXiv ID
1712.08865
Category
q-bio.MN
Cross-listed
cs.IR
Citations
15
Venue
arXiv.org
Last Checked
1 month ago
Abstract
In the real world, most objects and data have multiple types of attributes and inter-connections. Such data structures are named "Heterogeneous Information Networks" (HIN) and have been widely researched. Biological systems are also considered to be highly complicated HIN. In this work, we review various applications of HIN methods to biological and chemical data, discuss some advanced topics, and describe some future research directions.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
📜 Similar Papers
In the same crypt — q-bio.MN
R.I.P.
👻
Ghosted
Large-scale analysis of disease pathways in the human interactome
R.I.P.
👻
Ghosted
Diffusion Component Analysis: Unraveling Functional Topology in Biological Networks
R.I.P.
👻
Ghosted
AptRank: An Adaptive PageRank Model for Protein Function Prediction on Bi-relational Graphs
R.I.P.
👻
Ghosted
Learning of signaling networks: molecular mechanisms
R.I.P.
👻
Ghosted
Control of Gene Regulatory Networks with Noisy Measurements and Uncertain Inputs
Died the same way — 👻 Ghosted
R.I.P.
👻
Ghosted
Language Models are Few-Shot Learners
R.I.P.
👻
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
👻
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
👻
Ghosted