Biology subjects

Kahanda, I.

Publications and source records attributed to Kahanda, I..

4 recordsLinked to original sources

bioRxiv2026

PanACRpred: Predicting Accessible Chromatin Regions in Pangenomes using Motif Chaining

In this work, we investigate using motif subsequence features to predict whether a genomic region is accessible to regulatory proteins, i.e. an accessible chromatin region (ACR), enabling transcription of associated genes. We focus on plants, whose agricultural and ecological importance make them interesting and important organisms to study, and whose complex genomes provide important stress tests for our algorithm. We show that motif sequence similarity as found by co-linear chaining can be used in combination with machine learning models to effectively predict ACRs in genome assemblies.

bioinformatics↗

bioRxiv2021

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

microRNAs (miRNAs) are known as one of the small non-coding RNA molecules that control the expression of genes at the RNA level, while some operate at the DNA level. They typically range from 20-24 nucleotides in length and can be found in the plant and animal kingdoms as well as in some viruses. Computational approaches have overcome the limitations of the experimental methods and have performed well in identifying miRNAs. Compared to mature miRNAs, precursor miRNAs (pre-miRNAs) are long and have a hairpin loop structure with structural features. Therefore, most in-silico tools are implemented for pre-miRNA identification. This study presents a multilayer perceptron (MLP) based classifier implemented using 180 features under sequential, structural, and thermodynamic feature categories for plant pre-miRNA identification. This classifier has a 92% accuracy, a 94% specificity, and a 90% sensitivity. We have further tested this model with other small non-coding RNA types and obtained 78% accuracy. Furthermore, we introduce a novel dataset to train and test machine learning models, addressing the overlapping data issue in the positive training and testing datasets presented in PlantMiRNAPred for the classification of real and pseudo-plant pre-miRNAs. The new dataset and the classifier that can be used with any plant species are deployed on a web server freely accessible at http://mirnafinder.shyaman.me/.

bioinformatics↗

bioRxiv2020

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

The biomedical literature provides an extensive source of information in the form of unstructured text. One of the most important types of information hidden in biomedical literature is the relationships between human proteins and their phenotypes, which, due to the exponential growth of publications, can remain hidden. This provides a range of opportunities for the development of computational methods to extract the biomedical relationships from the unstructured text. In our previous work, we developed a supervised machine learning approach, called PPPred, for classifying the validity of a given sentence-level human protein-phenotype co-mention. In this work, we propose DeepPPPred, an ensemble classifier composed of PPPred and three deep neural network models: RNN, CNN, and BERT. Using an expanded gold-standard co-mention dataset, we demonstrate that the proposed ensemble method significantly outperforms its constituent components and provides a new state-of-the-art performance on classifying the co-mentions of human proteins and phenotype terms.

bioinformatics↗

bioRxiv2019

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

The MEDLINE database provides an extensive source of scientific articles and heterogeneous biomedical information in the form of unstructured text. One of the most important knowledge present within articles are the relations between human proteins and their phenotypes, which can stay hidden due to the exponential growth of publications. This has presented a range of opportunities for the development of computational methods to extract these biomedical relations from the articles. However, currently, no such method exists for the automated extraction of relations involving human proteins and human phenotype ontology (HPO) terms. In our previous work, we developed a comprehensive database composed of all co-mentions of proteins and phenotypes. In this study, we present a supervised machine learning approach called PPPred (Protein-Phenotype Predictor) for classifying the validity of a given sentence-level co-mention. Using an in-house developed gold standard dataset, we demonstrate that PPPred significantly outperforms several baseline methods. This two-step approach of co-mention extraction and classification constitutes a complete biomedical relation extraction pipeline for extracting protein-phenotype relations.\n\nCCS CONCEPTS*Computing methodologies [->] Information extraction; Supervised learning by classification; *Applied computing [->]Bioinformatics;

bioinformatics↗