AI Medical Compendium Journal:
BMC medical genomics

Showing 21 to 30 of 52 articles

Machine learning based refined differential gene expression analysis of pediatric sepsis.

BMC medical genomics
BACKGROUND: Differential expression (DE) analysis of transcriptomic data enables genome-wide analysis of gene expression changes associated with biological conditions of interest. Such analysis often provides a wide list of genes that are differentia...

A machine learning framework for genotyping the structural variations with copy number variant.

BMC medical genomics
BACKGROUND: Genotyping of structural variation is an important computational problem in next generation sequence data analysis. However, in cancer genomes, the copy number variant(CNV) often coexists with other types of structural variations which si...

Convolutional neural network models for cancer type prediction based on gene expression.

BMC medical genomics
BACKGROUND: Precise prediction of cancer types is vital for cancer diagnosis and therapy. Through a predictive model, important cancer marker genes can be inferred. Several studies have attempted to build machine learning models for this task however...

The International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019): computational methods and applications in medical genomics.

BMC medical genomics
In this editorial, we briefly summarized the International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019) that was held on June 9-11, 2019 at Columbus, Ohio, USA. We further introduced the 19 research articles included in this suppl...

Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations.

BMC medical genomics
BACKGROUND: Recent advances in kernel-based Deep Learning models have introduced a new era in medical research. Originally designed for pattern recognition and image processing, Deep Learning models are now applied to survival prognosis of cancer pat...

DeepTRIAGE: interpretable and individualised biomarker scores using attention mechanism for the classification of breast cancer sub-types.

BMC medical genomics
BACKGROUND: Breast cancer is a collection of multiple tissue pathologies, each with a distinct molecular signature that correlates with patient prognosis and response to therapy. Accurately differentiating between breast cancer sub-types is an import...

Interpretable deep neural network for cancer survival analysis by integrating genomic and clinical data.

BMC medical genomics
BACKGROUND: Understanding the complex biological mechanisms of cancer patient survival using genomic and clinical data is vital, not only to develop new treatments for patients, but also to improve survival prediction. However, highly nonlinear and h...

A deep neural network approach to predicting clinical outcomes of neuroblastoma patients.

BMC medical genomics
BACKGROUND: The availability of high-throughput omics datasets from large patient cohorts has allowed the development of methods that aim at predicting patient clinical outcomes, such as survival and disease recurrence. Such methods are also importan...

Analysis of disease comorbidity patterns in a large-scale China population.

BMC medical genomics
BACKGROUND: Disease comorbidity is popular and has significant indications for disease progress and management. We aim to detect the general disease comorbidity patterns in Chinese populations using a large-scale clinical data set.

A comparison of machine learning classifiers for dementia with Lewy bodies using miRNA expression data.

BMC medical genomics
BACKGROUND: Dementia with Lewy bodies (DLB) is the second most common subtype of neurodegenerative dementia in humans following Alzheimer's disease (AD). Present clinical diagnosis of DLB has high specificity and low sensitivity and finding potential...