108 citations · 111 across the 6 of their papers we have counts for
6 papers
Traditional machine learning vs. deep learning from dynamic graph representations of proteins' 3D folds in the task of protein structure classification
Aydin Wells, Francis A. Gatsi, Aaron Striegel +1
Protein structure classification (PSC) uses supervised learning to predict a protein's CATH/SCOP(e) class from the protein's sequence or 3D structural feature(s). We already modele…
Current and future directions in network biology
Marinka Zitnik, Michelle M. Li, Aydin Wells +34
Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseas…
Towards future directions in data-integrative supervised prediction of human aging-related genes
Qi Li, Khalique Newaz, Tijana Milenković
Identification of human genes involved in the aging process is critical due to the incidence of many diseases with age. A state-of-the-art approach for this purpose infers a weight…
Supervised prediction of aging-related genes from a context-specific protein interaction subnetwork
Qi Li, Tijana Milenković
Background. Human aging is linked to many prevalent diseases. The aging process is highly influenced by genetic factors. Hence, it is important to identify human aging-related gene…
Exploring the structure and function of temporal networks with dynamic graphlets
Yuriy Hulovatyy, Huili Chen, Tijana Milenkovic
With the growing amount of available temporal real-world network data, an important question is how to efficiently study these data. One can simply model a temporal network as eith…
Revealing missing parts of the interactome
Ryan W. Solava, Tijana Milenković
Protein interaction networks (PINs) are often used to "learn" new biological function from their topology. Since current PINs are noisy, their computational de-noising via link pre…