AI Medical Compendium Journal:
BMC medical genomics

Showing 31 to 40 of 52 articles

ClearF: a supervised feature scoring method to find biomarkers using class-wise embedding and reconstruction.

BMC medical genomics
BACKGROUND: Feature selection or scoring methods for the detection of biomarkers are essential in bioinformatics. Various feature selection methods have been developed for the detection of biomarkers, and several studies have employed information-the...

NeoMutate: an ensemble machine learning framework for the prediction of somatic mutations in cancer.

BMC medical genomics
BACKGROUND: The accurate screening of tumor genomic landscapes for somatic mutations using high-throughput sequencing involves a crucial step in precise clinical diagnosis and targeted therapy. However, the complex inherent features of cancer tissue,...

Selecting precise reference normal tissue samples for cancer research using a deep learning approach.

BMC medical genomics
BACKGROUND: Normal tissue samples are often employed as a control for understanding disease mechanisms, however, collecting matched normal tissues from patients is difficult in many instances. In cancer research, for example, the open cancer resource...

A robust fuzzy rule based integrative feature selection strategy for gene expression data in TCGA.

BMC medical genomics
BACKGROUND: Lots of researches have been conducted in the selection of gene signatures that could distinguish the cancer patients from the normal. However, it is still an open question on how to extract the robust gene features.

Predicting drug response of tumors from integrated genomic profiles by deep neural networks.

BMC medical genomics
BACKGROUND: The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent study screened for the response of a thousand human cancer ...

A novel gene selection algorithm for cancer classification using microarray datasets.

BMC medical genomics
BACKGROUND: Microarray datasets are an important medical diagnostic tool as they represent the states of a cell at the molecular level. Available microarray datasets for classifying cancer types generally have a fairly small sample size compared to t...

Discovering functional impacts of miRNAs in cancers using a causal deep learning model.

BMC medical genomics
BACKGROUND: Micro-RNAs (miRNAs) play a significant role in regulating gene expression under physiological and pathological conditions such as cancers. However, it remains a challenging problem to discover the target messenger RNAs (mRNAs) of a miRNA ...

CRlncRC: a machine learning-based method for cancer-related long noncoding RNA identification using integrated features.

BMC medical genomics
BACKGROUND: Long noncoding RNAs (lncRNAs) are widely involved in the initiation and development of cancer. Although some computational methods have been proposed to identify cancer-related lncRNAs, there is still a demanding to improve the prediction...

Gene Ontology-based function prediction of long non-coding RNAs using bi-random walk.

BMC medical genomics
BACKGROUND: With the development of sequencing technology, more and more long non-coding RNAs (lncRNAs) have been identified. Some lncRNAs have been confirmed that they play an important role in the process of development through the dosage compensat...

Bi-stream CNN Down Syndrome screening model based on genotyping array.

BMC medical genomics
BACKGROUND: Human Down syndrome (DS) is usually caused by genomic micro-duplications and dosage imbalances of human chromosome 21. It is associated with many genomic and phenotype abnormalities. Even though human DS occurs about 1 per 1,000 births wo...