Author

Kuldip K. Paliwal

Griffith UniversityORCID 0000-0002-3553-3662Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 3 Assay:protein sequence· 3 Approach:statistical model· 3 Approach:deep learning· 2 Research Focus:protein structure prediction· 2 Research Focus:gene regulatory network inference· 1 Assay:long-read sequencing· 1 Research Focus:sequence-to-function modeling· 1

Frequent Co-Authors

Yaoqi ZhouiD· 5 shared Jaswinder SinghiD· 4 shared Thomas LitfiniD· 4 shared Jaspreet SinghiD· 3 shared A. Keith DunkeriD· 1 shared Ágnes TantosiD· 1 shared Alexander Miguel MonzóniD· 1 shared Alok K. SharmaiD· 1 shared

Activity by Year

2021-2022 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

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5 shared papersBioinformatics

Yaoqi ZhouORCID

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Author profile →

5 papers

Recent·Most cited

Improved RNA homology detection and alignment by automatic iterative search in an expanded database

Jaswinder Singh, Kuldip K. Paliwal, Jaspreet Singh, Thomas Litfin, Yaoqi Zhou

The paper improves RNA homology detection and alignment using an automated iterative search with an expanded database, achieving performance comparable to Rfam.

bioRxiv · Oct 3, 2022 · PreprintBioinformatics

SPOT-Contact-Single: Improving Single-Sequence-Based Prediction of Protein Contact Map using a Transformer Language Model

Jaspreet Singh, Thomas Litfin, Jaswinder Singh, Kuldip K. Paliwal, Yaoqi Zhou

The paper introduces SPOT-Contact-Single, a method that improves single-sequence-based protein contact map prediction using a transformer language model, achieving better performance than existing methods on proteins without homologs.

Bioinformatics · Jan 26, 2022Peer-reviewedBioinformatics

SPOT-1D-LM: Reaching Alignment-profile-based Accuracy in Predicting Protein Secondary and Tertiary Structural Properties without Alignment.

Jaspreet Singh, Kuldip K. Paliwal, Jaswinder Singh, Yaoqi Zhou

The paper introduces SPOT-1D-LM, a model that achieves alignment-profile-based accuracy in predicting protein structural properties without using multiple sequence alignments.

bioRxiv · Oct 16, 2021 · PreprintBioinformatics

RNAcmap: A Fully Automatic Method for Predicting Contact Maps of RNAs by Evolutionary Coupling Analysis

Tongchuan Zhang, Jaswinder Singh, Thomas Litfin, Jian Zhan, Kuldip K. Paliwal, Yaoqi Zhou

RNAcmap automatically predicts RNA contact maps using evolutionary coupling analysis, outperforming manual methods and showing consistency across RNA sequences.

Bioinformatics · May 18, 2021Peer-reviewedBioinformatics

Critical Assessment of Protein Intrinsic Disorder Prediction

Marco Necci, Damiano Piovesan, CAID Predictors, Md Tamjidul Hoque, Ian Walsh, Sumaiya Iqbal, Michele Vendruscolo, Pietro Sormanni, Chen Wang, Daniele Raimondi, Ronesh Sharma, Yaoqi Zhou, Thomas Litfin, Oxana V. Galzitskaya, Michail Yu. Lobanov, , Silvio C. E. Tosatto

The paper evaluates 43 methods for predicting intrinsically disordered regions and binding regions in proteins, finding that deep learning methods outperform older physicochemical approaches but binding prediction remains challenging.

Nature Methods · Apr 19, 2021Peer-reviewedBioinformatics