Biology subjects

Mehryary, F.

Publications and source records attributed to Mehryary, F..

3 recordsLinked to original sources

bioRxiv2024

RegulaTome: a corpus of typed, directed, and signed relations between biomedical entities in the scientific literature

MotivationIn the field of biomedical text mining, the ability to extract relations from literature is crucial for advancing both theoretical research and practical applications. There is a notable shortage of corpora designed to enhance the extraction of multiple types of relations, particularly focusing on proteins and protein-containing entities such as complexes and families, as well as chemicals. ResultsIn this work we present RegulaTome, a corpus that overcomes the limitations of several existing biomedical relation extraction (RE) corpora, many of which concentrate on single-type relations at the sentence level. RegulaTome stands out by offering 16,962 relations annotated in over 2,500 documents, making it the most extensive dataset of its kind to date. This corpus is specifically designed to cover a broader spectrum of over 40 relation types beyond those traditionally explored, setting a new benchmark in the complexity and depth of biomedical RE tasks. Our corpus both broadens the scope of detected relations and allows for achieving noteworthy accuracy in RE. A Transformer-based model trained on this corpus has demonstrated a promising F1-score (66.6%) for a task of this complexity, underscoring the effectiveness of our approach in accurately identifying and categorizing a wide array of biological relations. This achievement highlights RegulaTomes potential to significantly contribute to the development of more sophisticated, efficient, and accurate RE systems to tackle biomedical tasks. Finally, a run of the trained relation extraction system on all PubMed abstracts and PMC Open Access full-text documents resulted in over 18 million relations, extracted from the entire biomedical literature. AvailabilityThe corpus and all introduced resources are openly accessible via Zenodo (https://zenodo.org/doi/10.5281/zenodo.10808330) and GitHub (https://github.com/farmeh/RegulaTome_extraction).

bioinformatics↗

bioRxiv2023

STRING-ing together protein complexes: extracting physical protein interactions from the literature

Understanding biological processes relies heavily on curated knowledge of physical interactions between proteins. Yet, a notable gap remains between the information stored in databases of curated knowledge and the plethora of interactions documented in the scientific literature. To bridge this gap, we introduce ComplexTome, a manually annotated corpus designed to facilitate the development of text-mining methods for the extraction of complex formation relationships among biomedical entities. This corpus comprises 1,287 documents with [~]3, 500 relationships. We train a novel relation extraction model on this corpus and find that it can highly reliably identify physical protein interactions (F1-score=82.8%). We additionally enhance the models capabilities through unsupervised trigger word detection and apply it to extract relations and trigger words for these relations from all open publications in the domain literature. This information has been fully integrated into the latest version of the STRING database, and all introduced resources are openly accessible via Zenodo and GitHub.

bioinformatics↗

bioRxiv2019

Neural network and random forest models in protein function prediction

Over the past decade, the demand for automated protein function prediction has increased due to the volume of newly sequenced proteins. In this paper, we address the function prediction task by developing an ensemble system automatically assigning Gene Ontology (GO) terms to the given input protein sequence.\n\nWe develop an ensemble system which combines the GO predictions made by random forest (RF) and neural network (NN) classifiers. Both RF and NN models rely on features derived from BLAST sequence alignments, taxonomy and protein signature analysis tools. In addition, we report on experiments with a NN model that directly analyzes the amino acid sequence as its sole input, using a convolutional layer. The Swiss-Prot database is used as the training and evaluation data.\n\nIn the CAFA3 evaluation, which relies on experimental verification of the functional predictions, our submitted ensemble model demonstrates competitive performance ranking among top-10 best-performing systems out of over 100 submitted systems. In this paper, we evaluate and further improve the CAFA3-submitted system. Our machine learning models together with the data pre-processing and feature generation tools are publicly available as an open source software at https://github.com/TurkuNLP/CAFA3\n\nAuthor summaryUnderstanding the role and function of proteins in biological processes is fundamental for new biological discoveries. Whereas modern sequencing methods have led to a rapid growth of protein databases, the function of these sequences is often unknown and expensive to determine experimentally. This has spurred a lot of interest in predictive modelling of protein functions.\n\nWe develop a machine learning system for annotating protein sequences with functional definitions selected from a vast set of predefined functions. The approach is based on a combination of neural network and random forest classifiers with features covering structural and taxonomic properties and sequence similarity. The system is thoroughly evaluated on a large set of manually curated functional annotations and shows competitive performance in comparison to other suggested approaches. We also analyze the predictions for different functional annotation and taxonomy categories and measure the importance of different features for the task. This analysis reveals that the system is particularly efficient for bacterial protein sequences.

bioinformatics↗