A5077365599- Biomedical Text Mining and Ontologies
- Bioinformatics and Genomic Networks
- Machine Learning in Bioinformatics
- Cancer-related molecular mechanisms research
- Genetics, Bioinformatics, and Biomedical Research
- RNA modifications and cancer
- Computational Drug Discovery Methods
- Antibiotic Resistance in Bacteria
- Bacteriophages and microbial interactions
- Semantic Web and Ontologies
- Protein Structure and Dynamics
- MicroRNA in disease regulation
- Tuberculosis Research and Epidemiology
- Topic Modeling
- Congenital heart defects research
- Antimicrobial Peptides and Activities
- Gene expression and cancer classification
- vaccines and immunoinformatics approaches
- Leprosy Research and Treatment
- RNA Research and Splicing
- Mycobacterium research and diagnosis
- Antimicrobial Resistance in Staphylococcus
- Cancer therapeutics and mechanisms
- Advanced Text Analysis Techniques
- Circular RNAs in diseases
Bharathiar University
2016-2025
Broad Institute
2023
Weatherford College
2013
Northwestern University
2013
National Center for Biotechnology Information
2013
Carnegie Institution for Science
2013
Spanish National Cancer Research Centre
2013
Johnson & Johnson (Sweden)
2011
Madurai Kamaraj University
2007-2010
University of Ulster
2004-2006
Fully automated text mining (TM) systems promote efficient literature searching, retrieval, and review but are not sufficient to produce ready-to-consume curated documents. These meant replace biocurators, instead assist them in one or more curation steps. To do so, the user interface is an important aspect that needs be considered for tool adoption. The BioCreative Interactive task (IAT) a track designed exploring user-system interactions, promoting development of useful TM tools, providing...
In many databases, biocuration primarily involves literature curation, which usually retrieving relevant articles, extracting information that will translate into annotations and identifying new incoming literature. As the volume of biological increases, use text mining to assist in becomes increasingly relevant. A number groups have developed tools for from a computer science/linguistics perspective, there are initiatives curate some aspect biology Some efforts already make tool, but not...
A wealth of knowledge concerning relations between genes and its associated diseases is present in biomedical literature. Mining these biological associations from literature can provide immense support to research ranging drug-targetable pathways biomarker discovery. However, time cost manual curation heavily slows it down. In this current scenario one the crucial technologies text mining, relation extraction shows promising result explore with diseases. By developing automatic gene-disease...
One of the most common and challenging problem in biomedical text mining is to mine protein–protein interactions (PPIs) from MEDLINE abstracts full-text research articles because PPIs play a major role understanding various biological processes impact proteins diseases. We implemented, PPInterFinder—a web-based tool extract human literature. PPInterFinder uses relation keyword co-occurrences with protein names information on consists three phases. First, it identifies using parser Tregex...
Abstract Background Sphingosine 1-phosphate (S1P), a lysophospholipid, is involved in various cellular processes such as migration, proliferation, and survival. To date, the impact of S1P on human glioblastoma not fully understood. Particularly, concerted role played by matrix metalloproteinases (MMP) aggressive tumor behavior angiogenesis remains to be elucidated. Results gain new insights effect invasion this type malignant tumor, we used microarrays investigate gene expression response...
Microarrays are one of the latest breakthroughs in experimental molecular biology that allow monitoring expression levels tens thousands genes simultaneously.Arrays have been applied to studies gene expression, genome mapping, SNP discrimination, transcription factor activity, toxicity, pathogen identification and many other applications.In this paper we concentrate on discussing various bioinformatics tools used for microarray data mining tasks with its underlying algorithms, web resources...
A novel coronavirus (SARS-CoV-2) has caused a major outbreak in human all over the world. There are several proteins interplay during entry and replication of this virus human. Here, we have used text mining named entity recognition method to identify co-occurrence important COVID 19 genes/proteins interaction network based on frequency interaction. Network analysis revealed set genes/proteins, highly dense genes/protein clusters sub-networks Angiotensin-converting enzyme 2 (ACE2), Helicase,...
Abstract Rationale and objectives Lung adenocarcinoma, a type of non-small cell lung cancer (NSCLC), originates from the peripheral tissue is most prevalent histologic subtype, constituting around 40% cases. Understanding intricate relationships among signaling pathways their changes in adenocarcinoma crucial for developing targeted therapies personalized treatment. This study aimed to analyze utility radiomics assessing alterations oncogenic cancer. Materials methods We investigated use...
Alzheimer's disease is the most common form of dementia.Abnormal hyperphosphorylation Microtubule associated protein tau (MAPT) one hallmarks and related pathies.CDK5 GSK3B are two main kinases that have an important role in abnormal MAPT which leads to disease.Structural information for both MAPT-CDK5 MAPT-GSK3B complexes being absent, we resorted molecular modeling gaining insight into mechanism implication by enzymes.First tertiary structure was modeled its active regions were...
Abstract The timely detection and precise classification of brain tumors using techniques such as magnetic resonance imaging (MRI) are imperative for optimizing treatment strategies improving patient outcomes. This study evaluated five state‐of‐the‐art models to determine the optimal model tumor diagnosis MRI. We utilized 3064 T1‐weighted contrast‐enhanced MRI images that included gliomas, pituitary tumors, meningiomas. Our analysis employed advanced categories: machine learning classifiers,...
The present study aimed to reveal the molecular mechanism of T-2 toxin-induced cerebral edema by aquaporin-4 (AQP4) blocking and permeation. AQP4 is a class aquaporin channels that mainly expressed in brain, its structural changes lead life-threatening complications such as cardio-respiratory arrest, nephritis, irreversible brain damage. We employed dynamics simulation, text mining, vitro vivo analysis functional induced toxin on AQP4. action leads disrupted permeation water coefficients are...
Automatic extraction of protein-protein interaction (PPI) pairs from biomedical literature is a widely examined task in biological information extraction. Currently, many kernel based approaches such as linear kernel, tree graph and combination multiple kernels has achieved promising results PPI task. However, most these methods fail to capture the semantic relation between two entities. In this paper, we present special type for which exploits both syntactic (structural) vectors known...
Leprosy is an infectious disease caused by Mycobacterium leprae.M. leprae has undergone a major reductive evolution leaving minimal set of functional genes for survival.It remains non-cultivable.As M. develops resistance against most the drugs, novel drug targets are required in order to design new drugs.As essential mediate several biosynthetic and metabolic pathways, pathway predictions can predict genes.We used comparative genome analysis enzymes H. sapiens using KEGG database identified...
Abstract The goal of the study was to investigate changes in gut microbiota during advancement gastric cancer (GC) and identify pertinent taxa associated with disease. We used a public fecal amplicon dataset from Sequence Retrieval Archive (SRA), patients GC, gastritis, healthy individuals. did sequence pre-processing, including quality filtering sequences. Then, we performed diversity analysis, evaluating α- β-diversity. Next, taxonomic composition analysis relative abundances different at...
Tagging biomedical entities such as gene, protein, cell, and cell-line is the first step an important pre-requisite in literature mining. In this paper, we describe our hybrid named entity tagging approach namely BCC-NER (bidirectional, contextual clues tagger for gene/protein mention recognition). deployed with three modules. The module text processing which includes basic NLP pre-processing, feature extraction, selection. second training model building bidirectional conditional random...
The predominant step and pre-requisite in the analysis of scientific literature is extraction gene/protein names biomedical texts. Though many taggers are available for this Named Entity Recognition (NER) task, we found none them achieve a good state-of-art tagging human genes/proteins. As most current text mining research related to literature, tagger precisely tag genes proteins highly desirable. In paper, propose new hybrid approach based on (a) machine learning algorithm (conditional...