activity
20182022
most citedWeighted graphlets and deep neural networks for protein structure classification

3 citations · 3 across the 3 of their papers we have counts for

collaborators

5 papers

q-bio.MN2022

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…

q-bio.MN2021

Dynamic network analysis improves protein 3D structural classification

Khalique Newaz, Jacob Piland, Patricia L. Clark +3

Protein structural classification (PSC) is a supervised problem of assigning proteins into pre-defined structural (e.g., CATH or SCOPe) classes based on the proteins' sequence or 3…

stat.ML20193 cited

Weighted graphlets and deep neural networks for protein structure classification

Hongyu Guo, Khalique Newaz, Scott Emrich +2

As proteins with similar structures often have similar functions, analysis of protein structures can help predict protein functions and is thus important. We consider the problem o…

q-bio.MN2019

Network analysis of synonymous codon usage

Khalique Newaz, Gabriel Wright, Jacob Piland +4

Most amino acids are encoded by multiple synonymous codons. For an amino acid, some of its synonymous codons are used much more rarely than others. Analyses of positions of such ra…

q-bio.MN2018

Network-based protein structural classification

Khalique Newaz, Mahboobeh Ghalehnovi, Arash Rahnama +2

Experimental determination of protein function is resource-consuming. As an alternative, computational prediction of protein function has received attention. In this context, prote…