Biology subjects

Vijay-Shanker, K.

Publications and source records attributed to Vijay-Shanker, K..

6 recordsLinked to original sources

bioRxiv2025

KSMoFinder - Knowledge graph embedding of proteins and motifs for predicting kinases of human phosphosites

MotivationProtein kinases regulate cellular signaling pathways through a cascade of phosphorylation activity, selectively targeting specific residues on substrate proteins (phosphosites). Determining the characteristics of kinases that phosphorylate specific substrates have been extensively studied. Most tools utilize amino acid sequence motifs around phosphosites but do not consider the biological characteristics of substrate proteins. ResultsWe present KSMoFinder, a kinase-substrate-motif prediction model that learns factors beyond motif similarities by integrating the biological contexts of proteins. We learn the semantics in a knowledge graph containing contextual relationships of proteins, kinase-specific motifs and motif composition, and represent the proteins and motifs as embedded vectors. Using the representations as features, we train a supervised deep learning classifier to identify kinase-phosphosite relationships. We use ground truth kinase-substrate-motif dataset from iPTMnet and PhosphositePlus and evaluate the prediction performance of KSMoFinder. Pairwise comparative assessments with prior kinase-substrate prediction tools demonstrate the superior performance of KSMoFinder. KSMoFinder trained using our knowledge graph embeddings surpasses the prediction performances using embeddings of popular protein language models such as ProtT5, ESM2 and ESM3 with a ROC-AUC of 0.851 and PR-AUC of 0.839 on a testing dataset with equal number of positives and negatives. Unlike most existing tools, KSMoFinder can be utilized to predict at the motif and at the substrate protein level. Availability and implementationAll code to reproduce the results are available at https://github.com/manju-anandakrishnan/KSMoFinder. All data and KSMoFinder predictions are deposited at https://doi.org/10.5281/zenodo.15730847.

bioinformatics↗

bioRxiv2023

eMIND: Enabling automatic collection of protein variation impacts in Alzheimer's disease from the literature

Alzheimers disease and related dementias (AD/ADRDs) are among the most common forms of dementia, and yet no effective treatments have been developed. To gain insight into the disease mechanism, capturing the connection of genetic variations to their impacts, at the disease and molecular levels, is essential. The scientific literature continues to be a main source for reporting experimental information about the impact of variants. Thus, development of automatic methods to identify publications and extract the information from the unstructured text would facilitate collecting and organizing information for reuse. We developed eMIND, a deep learning-based text mining system that supports the automatic extraction of annotations of variants and their impacts in AD/ADRDs. In particular, we use this method to capture the impacts of protein-coding variants affecting a selected set of protein properties, such as protein activity/function, structure and post-translational modifications. We conducted an evaluation on the efficacy of eMIND to extract variant impact relations and obtained a recall of 0.84 and a precision of 0.94. The publications and extracted information are integrated into the UniProtKB computationally mapped bibliography to expand annotations on protein entries. eMINDs text-mined output are presented using controlled vocabularies and ontologies for variant, disease and impact along with the evidence sentences. A sample of annotated abstracts can be accessed at URL: https://research.bioinformatics.udel.edu/itextmine/emind.

bioinformatics↗

bioRxiv2022

Text mining of CHO bioprocess bibliome: Topic modeling and document classification

Chinese hamster ovary (CHO) cells are widely used for mass production of therapeutic proteins in the pharmaceutical industry. With the growing need in optimizing the performance of producer CHO cell lines, research on CHO cell line development and bioprocess continues to increase in recent decades. Bibliographic mapping and classification of relevant research studies will be essential for identifying research gaps and trends in literature. To qualitatively and quantitatively understand the CHO literature, we have conducted topic modeling using a CHO bioprocess bibliome manually compiled in 2016, and compared the topics uncovered by the Latent Dirichlet Allocation (LDA) models with the human labels of the CHO bibliome. The results show a significant overlap between the manually selected categories and computationally generated topics, and reveal the machine-generated topic-specific characteristics. To identify relevant CHO bioprocessing papers from new scientific literature, we have developed a supervised learning model, Logistic Regression, to identify specific article topics and evaluated the results using three CHO bibliome datasets, Bioprocessing set, Glycosylation set, and Phenotype set. The use of top terms as features supports the explainability of document classification results to yield insights on new CHO bioprocessing papers.

bioinformatics↗

bioRxiv2020

emiRIT: A text-mining based resource for microRNA information

MotivationmicroRNAs (miRNAs) are essential gene regulators and their dysregulation often leads to diseases. Easy access to miRNA information is crucial for interpreting generated experimental data, connecting facts across publications, and developing new hypotheses built on previous knowledge. Here, we present emiRIT, a text mining-based resource, which presents miRNA information mined from the literature through a user-friendly interface. ResultsWe collected 149,233 miRNA-PubMed ID pairs from Medline between January 1997 to May 2020. emiRIT currently contains miRNA-gene regulation (60,491 relations); miRNA-disease (cancer) (12,300 relations); miRNA-biological process and pathways (23,390 relations); and circulatory miRNAs in extracellular locations (3,782 relations). Biological entities and their relation to miRNAs were extracted from Medline abstracts using publicly available and in-house developed text mining tools, and the entities were normalized to facilitate querying and integration. We built a database and an interface to store and access the integrated data, respectively. ConclusionWe provide an up-to-date and user-friendly resource to facilitate access to comprehensive miRNA information from the literature on a large-scale, enabling users to navigate through different roles of miRNA and examine them in a context specific to their information needs. To assess our resources information coverage, in the absence of gold standards, we have conducted two case studies focusing on the target and differential expression information of miRNAs in the context of diseases. Database URL: https://research.bioinformatics.udel.edu/emirit/

bioinformatics↗

bioRxiv2019

Using distant supervision to augment manually annotated data for relation extraction

Significant progress has been made in applying deep learning on natural language processing tasks recently. However, deep learning models typically require a large amount of annotated training data while often only small labeled datasets are available for many natural language processing tasks in biomedical literature. Building large-size datasets for deep learning is expensive since it involves considerable human effort and usually requires domain expertise in specialized fields. In this work, we consider augmenting manually annotated data with large amounts of data using distant supervision. However, data obtained by distant supervision is often noisy, we first apply some heuristics to remove some of the incorrect annotations. Then using methods inspired from transfer learning, we show that the resulting models outperform models trained on the original manually annotated sets.

bioinformatics↗

bioRxiv2017

eGARD: Extracting associations between genomic anomalies and drug responses from text

Tumor molecular profiling plays an integral role in identifying genomic anomalies which may help in personalizing cancer treatments, improving patient outcomes and minimizing risks associated with different therapies. However, critical information regarding the evidence of clinical utility of such anomalies is largely buried in biomedical literature. It is becoming prohibitive for biocurators, clinical researchers and oncologists to keep up with the rapidly growing volume and breadth of information, especially those that describe therapeutic implications of biomarkers and therefore relevant for treatment selection. In an effort to improve and speed up the process of manually reviewing and extracting relevant information from literature, we have developed a natural language processing (NLP)-based text mining (TM) system called eGARD (extracting Genomic Anomalies association with Response to Drugs). This system relies on the syntactic nature of sentences coupled with various textual features to extract relations between genomic anomalies and drug response from MEDLINE abstracts. Our system achieved high precision, recall and F-measure of up to 0.95, 0.86 and 0.90, respectively, on annotated evaluation datasets created in-house and obtained externally from PharmGKB. Additionally, the system extracted information that helps determine the confidence level of extraction to support prioritization of curation. Such a system will enable clinical researchers to explore the use of published markers to stratify patients upfront for best-fit therapies and readily generate hypotheses for new clinical trials.

bioinformatics↗