Author

Indika Kahanda

University of North FloridaORCID 0000-0002-4536-6917Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 5

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Approach:machine learning (classical)· 4 Research Focus:benchmarking· 2 Research Focus:gene regulatory network inference· 2 Organism:human· 2 Organism:plant (other)· 2 Assay:proteomics· 2 Approach:statistical model· 2 Organism:C. elegans· 1

Frequent Co-Authors

Morteza Pourreza ShahriiD· 2 shared Ahmet Süreyya RifaioğluiD· 1 shared Alan J. MedlariD· 1 shared Alberto PaccanaroiD· 1 shared Alessandro PetriniiD· 1 shared Alex Alves FreitasiD· 1 shared Alex W. CrockeriD· 1 shared Alex Warwick VesztrocyiD· 1 shared

Activity by Year

2019-2026 · peak 2/yr

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10 top collaborators · 36 mutual co-author links

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2 shared papersBioinformatics

Morteza Pourreza ShahriORCID

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5 papers

Recent·Most cited

PanACRpred: Predicting Accessible Chromatin Regions in Pangenomes using Motif Chaining

Madelyn Warr, Trung Dinh, Bella Root, Elyse Onstott, Kevin Yu, Joann Mudge, Thiruvarangan Ramaraj, Indika Kahanda, Brendan Mumey

The paper predicts accessible chromatin regions in plant genomes using motif chaining and machine learning models.

Communications in computer and information science · Jan 1, 2026Peer-reviewedBioinformatics

miRNAFinder: A Comprehensive Web Resource for Plant Pre-microRNA Classification

Sandali D. Lokuge, Shyaman Jayasundara, Puwasuru Ihalagedara, Indika Kahanda, Damayanthi Herath

The paper introduces miRNAFinder, a web tool for plant pre-miRNA classification using a multilayer perceptron model with high accuracy.

Biosystems · Mar 16, 2022Peer-reviewedBioinformatics

DeepPPPred: An Ensemble of BERT, CNN, and RNN for Classifying Co-mentions of Proteins and Phenotypes

Morteza Pourreza Shahri, Katrina N. Lyon, Julia Schearer, Indika Kahanda

DeepPPPred combines BERT, CNN, and RNN with PPPred to classify protein-phenotype co-mentions, achieving state-of-the-art performance on an expanded gold-standard dataset.

bioRxiv · Sep 18, 2020 · PreprintBioinformatics

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Naihui Zhou, Yuxiang Jiang, Timothy R. Bergquist, Alexandra J. Lee, Balint Z Kacsoh, Alex W. Crocker, Kimberley A. Lewis, George P. Georghiou, Huy N. Nguyen, Md-Nafiz Hamid, Larry Davis, Tunca Doğan, Volkan Atalay, Ahmet Süreyya Rifaioğlu, Alperen Dalkıran, , Enrico Lavezzo

The paper reports results from the CAFA3 challenge, showing improved computational protein function prediction and new annotations for over 1000 genes through experimental screens in C. albicans, P. aureginosa, and Drosophila.

Genome biology · Nov 19, 2019Peer-reviewedBioinformatics

PPPred: Classifying Protein-phenotype Co-mentions Extracted from Biomedical Literature

Morteza Pourreza Shahri, Mandi M. Roe, Gillian L. Reynolds, Indika Kahanda

PPPred classifies protein-phenotype co-mentions from biomedical text using supervised learning, outperforming baselines in validation.

bioRxiv · May 31, 2019 · PreprintBioinformatics