Role of artificial intelligence in radiogenomics for cancers in the era of precision medicine
Simple Summary Recently, radiogenomics has played a significant role and offered a new
understanding of cancer's biology and behavior in response to standard therapy. It also …
understanding of cancer's biology and behavior in response to standard therapy. It also …
Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations
Abstract The coronavirus disease 2019 (COVID-19) pandemic has been severely impacting
global society since December 2019. The related findings such as vaccine and drug …
global society since December 2019. The related findings such as vaccine and drug …
Machine learning-based radiomics signatures for EGFR and KRAS mutations prediction in non-small-cell lung cancer
Early identification of epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral
oncogene homolog (KRAS) mutations is crucial for selecting a therapeutic strategy for …
oncogene homolog (KRAS) mutations is crucial for selecting a therapeutic strategy for …
Radiomics-based machine learning model for efficiently classifying transcriptome subtypes in glioblastoma patients from MRI
Background In the field of glioma, transcriptome subtypes have been considered as an
important diagnostic and prognostic biomarker that may help improve the treatment efficacy …
important diagnostic and prognostic biomarker that may help improve the treatment efficacy …
Improving performance with hybrid feature selection and ensemble machine learning techniques for code smell detection
S Jain, A Saha - Science of Computer Programming, 2021 - Elsevier
Maintaining large and complex software is a significant task in IT industry. One reason for
that is the development of code smells which are design flaws that lead to future bugs and …
that is the development of code smells which are design flaws that lead to future bugs and …
Improving MGMT methylation status prediction of glioblastoma through optimizing radiomics features using genetic algorithm-based machine learning approach
Abstract O6-Methylguanine-DNA-methyltransferase (MGMT) promoter methylation was
shown in many studies to be an important predictive biomarker for temozolomide (TMZ) …
shown in many studies to be an important predictive biomarker for temozolomide (TMZ) …
Machine learning model for identifying antioxidant proteins using features calculated from primary sequences
L Ho Thanh Lam, NH Le, L Van Tuan, H Tran Ban… - Biology, 2020 - mdpi.com
Simple Summary Antioxidant compounds protect the human body from many kinds of
diseases as well as the degeneration of age. Several micronutrients that were found in the …
diseases as well as the degeneration of age. Several micronutrients that were found in the …
A comparison of three different deep learning-based models to predict the MGMT promoter methylation status in glioblastoma using brain MRI
Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. The
standard treatment for GBM consists of surgical resection followed by concurrent …
standard treatment for GBM consists of surgical resection followed by concurrent …
GADTI: graph autoencoder approach for DTI prediction from heterogeneous network
Identifying drug–target interaction (DTI) is the basis for drug development. However, the
method of using biochemical experiments to discover drug-target interactions has low …
method of using biochemical experiments to discover drug-target interactions has low …
Prediction framework on early urine infection in IoT–Fog environment using XGBoost ensemble model
Urine infections are one of the most prevalent concerns for the healthcare industry that may
impair the functioning of the kidney and other renal organs. As a result, early diagnosis and …
impair the functioning of the kidney and other renal organs. As a result, early diagnosis and …