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Machine learning approach in inferring main population-level COVID-19 risk factors
(Belgrade : Institute of molecular genetics and genetic engineering, 2023)
Machine-learning methods have become indispensable in scientific research as the
amount of available data has grown exponentially in recent years. It is, thus, necessary
to employ various unsupervised and supervised ...
Some Applications of Graph-Based Machine Learning Methods on Biological Data
(Belgrade : Institute of molecular genetics and genetic engineering, 2023)
Machine learning has made considerable contributions to various fields, most notably by
providing methods for predictive modeling and data analysis. Usually, different kinds of
data are best modeled by specialized machine ...
The past, the present, and the future of RNA secondary structure prediction
(Belgrade : Institute of molecular genetics and genetic engineering, 2023)
RNA is a biopolymer whose primary structure is a sequence of nucleobases. While
messenger RNA is probably the most known, an increasing number of non-coding RNAs
is being discovered. In order to become biologically active, ...
Beyond the Global Health Security Index: A Machine Learning Approach to Analyzing the Official COVID-19 Deaths and Excess Deaths Data
(Belgrade : Institute of molecular genetics and genetic engineering, 2023)
The Global Health Security Index (GHSI) is designed to assess the preparedness of
countries to deal with infectious disease outbreaks. However, the COVID-19 pandemic
has revealed a paradoxical relationship between the ...
Alternative splicing impacts microRNA regulation within coding regions
(Belgrade : Institute of molecular genetics and genetic engineering, 2023)
MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate post-transcriptional
gene expression by binding to specific target sites. Approximately 95% of human multi-exon
genes can be spliced alternatively, which ...
AI-powered framework to predict the toxicity of microplastics
(Belgrade : Institute of molecular genetics and genetic engineering, 2023)
Numerous articles have been published investigating the health effects of exposure
to micro- and nanoplastics (MNPs). However, these studies have yielded inconclusive
findings due to the lack of comparability between ...
Zero- and Few-Shot Machine Learning for Named Entity Recognition in Biomedical Texts
(Belgrade : Institute of molecular genetics and genetic engineering, 2023)
Named entity recognition (NER) is an NLP that involves identifying and classifying named
entities in text. Token classification is a crucial subtask of NER that assumes assigning
labels to individual tokens within a text, ...
Supervised Machine Learning Approach for Prediction of Occult Lymph Node Metastasis in T1-T2 Papillary Thyroid Carcinoma
(Belgrade : Institute of molecular genetics and genetic engineering, 2023)
This study aimed to assess and compare four machine learning (ML) based classifiers in
predicting occult cervical lymph node metastasis (LNM) in clinically node-negative (cN0),
T1-T2 papillary thyroid carcinoma (PTC) ...
The use of Active Machine Learning for Protospacer-Adjacent Motif recovery in Class 2 CRISPR-Cas systems
(Belgrade : Institute of molecular genetics and genetic engineering, 2023)
The recognition of target DNA sequences during the interference phase of prokaryotic
CRISPR-Cas immunity relies on Protospacer-Adjacent Motif (PAM) sequences, specific
for each Cas effector. PAM identification is a ...