AIMC Topic: Biomarkers

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Biomarker detection using corrected degree of domesticity in hybrid social network feature selection for improving classifier performance.

BMC bioinformatics
BACKGROUND: Dimension reduction, especially feature selection, is an important step in improving classification performance for high-dimensional data. Particularly in cancer research, when reducing the number of features, i.e., genes, it is important...

The invasiveness of robot-assisted total hip replacement is similar to that of conventional surgery.

Journal of robotic surgery
Robot-assisted total hip arthroplasty (R-THA) is increasingly being performed throughout the world. The invasiveness of this operation is unknown. We retrospectively reviewed the cohort of consecutive osteonecrosis of the femoral head (ONFH) patients...

Application of statistical machine learning in biomarker selection.

Scientific reports
In the recent JAVELIN Bladder 100 phase 3 trial, avelumab plus best supportive care significantly prolonged overall survival relative to best supportive care alone as first-line maintenance therapy following first-line platinum-based chemotherapy in ...

Machine learning-based feature selection to search stable microbial biomarkers: application to inflammatory bowel disease.

GigaScience
BACKGROUND: Biomarker discovery exploiting feature importance of machine learning has risen recently in the microbiome landscape with its high predictive performance in several disease states. To have a concrete selection among a high number of featu...

Retina Oculomics in Neurodegenerative Disease.

Annals of biomedical engineering
Ophthalmic biomarkers have long played a critical role in diagnosing and managing ocular diseases. Oculomics has emerged as a field that utilizes ocular imaging biomarkers to provide insights into systemic diseases. Advances in diagnostic and imaging...

Emerging trends in post-translational modification: Shedding light on Glioblastoma multiforme.

Biochimica et biophysica acta. Reviews on cancer
Recent multi-omics studies, including proteomics, transcriptomics, genomics, and metabolomics have revealed the critical role of post-translational modifications (PTMs) in the progression and pathogenesis of Glioblastoma multiforme (GBM). Further, PT...

Automatic selection of spoken language biomarkers for dementia detection.

Neural networks : the official journal of the International Neural Network Society
This paper analyzes diverse features extracted from spoken language to select the most discriminative ones for dementia detection. We present a two-step feature selection (FS) approach: Step 1 utilizes filter methods to pre-screen features, and Step ...

c-Diadem: a constrained dual-input deep learning model to identify novel biomarkers in Alzheimer's disease.

BMC medical genomics
BACKGROUND: Alzheimer's disease (AD) is an incurable, debilitating neurodegenerative disorder. Current biomarkers for AD diagnosis require expensive neuroimaging or invasive cerebrospinal fluid sampling, thus precluding early detection. Blood-based b...

Machine Learning and Omics Analysis in Aortic Aneurysm.

Angiology
Aortic aneurysm is a life-threatening condition and mechanisms underlying its formation and progression are still incompletely understood. Omics approach has brought new insights to identify a broad spectrum of biomarkers and better understand cellul...

NGCICM: A Novel Deep Learning-Based Method for Predicting circRNA-miRNA Interactions.

IEEE/ACM transactions on computational biology and bioinformatics
The circRNAs and miRNAs play an important role in the development of human diseases, and they can be widely used as biomarkers of diseases for disease diagnosis. In particular, circRNAs can act as sponge adsorbers for miRNAs and act together in certa...