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High-Throughput Nucleotide Sequencing

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Machine learning on microbiome research in gastrointestinal cancer.

Journal of gastroenterology and hepatology
Gastrointestinal cancer maintains the highest incidence and mortality rate among all cancers globally. In addition to genetic causes, it has been reported that individuals' diet and composition of the gastrointestinal microbiome have profound impacts...

A novel machine learning approach (svmSomatic) to distinguish somatic and germline mutations using next-generation sequencing data.

Zoological research
Somatic mutations are a large category of genetic variations, which play an essential role in tumorigenesis. Detection of somatic single nucleotide variants (SNVs) could facilitate downstream analysis of tumorigenesis. Many computational methods have...

Machine Learning Approaches on High Throughput NGS Data to Unveil Mechanisms of Function in Biology and Disease.

Cancer genomics & proteomics
In this review, the fundamental basis of machine learning (ML) and data mining (DM) are summarized together with the techniques for distilling knowledge from state-of-the-art omics experiments. This includes an introduction to the basic mathematical ...

Artificial Intelligence in Pathology: A Simple and Practical Guide.

Advances in anatomic pathology
Artificial intelligence (AI) is having an increasing impact on the field of pathology, as computation techniques allow computers to perform tasks previously performed by people. Here, we offer a simple and practical guide to AI methods used in pathol...

Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms.

Molecular imaging and biology
PURPOSE: Considerable progress has been made in the assessment and management of non-small cell lung cancer (NSCLC) patients based on mutation status in the epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene (KRAS). At the...

A refined cell-of-origin classifier with targeted NGS and artificial intelligence shows robust predictive value in DLBCL.

Blood advances
Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous entity of B-cell lymphoma. Cell-of-origin (COO) classification of DLBCL is required in routine practice by the World Health Organization classification for biological and therapeutic insights. ...

Imputing missing RNA-sequencing data from DNA methylation by using a transfer learning-based neural network.

GigaScience
BACKGROUND: Gene expression plays a key intermediate role in linking molecular features at the DNA level and phenotype. However, owing to various limitations in experiments, the RNA-seq data are missing in many samples while there exist high-quality ...

Uncovering tissue-specific binding features from differential deep learning.

Nucleic acids research
Transcription factors (TFs) can bind DNA in a cooperative manner, enabling a mutual increase in occupancy. Through this type of interaction, alternative binding sites can be preferentially bound in different tissues to regulate tissue-specific expres...