activity
20052023
most citedNeuro-symbolic representation learning on biological knowledge graphs

155 citations · 404 across the 20 of their papers we have counts for

collaborators

20 papers

stat.ML2023★ 1 cited

Hybrid Fuzzy-Crisp Clustering Algorithm: Theory and Experiments

Akira R. Kinjo, Daphne Teck Ching Lai

With the membership function being strictly positive, the conventional fuzzy c-means clustering method sometimes causes imbalanced influence when clusters of vastly different sizes…

q-bio.BM2017★ 1 cited

Cooperative "folding transition" in the sequence space facilitates function-driven evolution of protein families

Akira R. Kinjo

In the protein sequence space, natural proteins form clusters of families which are characterized by their unique native folds whereas the great majority of random polypeptides are…

q-bio.BM2017★ 3 cited

Monte Carlo simulation of a statistical mechanical model of multiple protein sequence alignment

Akira R. Kinjo

A grand canonical Monte Carlo (MC) algorithm is presented for studying the lattice gas model (LGM) of multiple protein sequence alignment, which coherently combines long-range inte…

q-bio.GN2017★ 14 cited

Essential role of long non-coding RNAs in de novo chromatin modifications: The genomic address code hypothesis

Ken Nishikawa, Akira R. Kinjo

The epigenome, i.e. the whole of chromatin modifications, is transferred from mother to daughter cells during cell differentiation. When de novo chromatin modifications (establishm…

q-bio.QM2016★ 155 cited

Neuro-symbolic representation learning on biological knowledge graphs

Mona Alshahrani, Mohammed Asif Khan, Omar Maddouri +3

Motivation: Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries…

q-bio.BM2015★ 1 cited

Liquid-theory analogy of direct-coupling analysis of multiple-sequence alignment and its implications for protein structure prediction

Akira R. Kinjo

The direct-coupling analysis is a powerful method for protein contact prediction, and enables us to extract "direct" correlations between distant sites that are latent in "indirect…