Genetics

Machine-learning algorithms predict breast cancer patient survival from UK Biobank whole-exome sequencing data.

We tested whether machine-learning algorithm could find biomarkers predicting overall survival in breast cancer patients using blood-based whole-exome sequencing data. Whole-exome sequencing data derived from 1181 female breast cancer patients within the UK Biobank was collected. We found feature genes (n = 50) regarding total mutation burden using the long short-term memory model. Then, we deve...

Scaling up DNA digital data storage by efficiently predicting DNA hybridisation using deep learning.

Deoxyribonucleic acid (DNA) has shown great promise in enabling computational applications, most notably in the fields of DNA digital data storage and DNA computing. Information is encoded as DNA strands, which will naturally bind in solution, thus enabling search and pattern-matching capabilities. Being able to control and predict the process of DNA hybridisation is crucial for the ambitious futu...

Effectiveness of Create ML in microscopy image classifications: a simple and inexpensive deep learning pipeline for non-data scientists.

Observing chromosomes is a time-consuming and labor-intensive process, and chromosomes have been analyzed manually for many years. In the last decade,...

Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases.

BACKGROUND: Clinical interpretation of genetic variants in the context of the patient's phenotype is becoming the largest component of cost and time e...

Using a multi-head, convolutional neural network with data augmentation to improve electropherogram classification performance.

DNA profiles are generated in forensic biology laboratories around the world. It is possible that these profiles are assessed by two independent peopl...

The BrAID study protocol: integration of machine learning and transcriptomics for brugada syndrome recognition.

BACKGROUND: Type 1 Brugada syndrome (BrS) is a hereditary arrhythmogenic disease showing peculiar electrocardiographic (ECG) patterns, characterized b...

miRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles.

Formation of mature miRNAs and their expression is a highly controlled process. It is very much dependent upon the post-transcriptional regulatory eve...

Genetic variant effect prediction by supervised nonnegative matrix tri-factorization.

Discriminating between deleterious and neutral mutations among numerous non-synonymous single nucleotide variants (nsSNVs) that may be observed throug...

Association of TLR 9 gene polymorphisms with remission in patients with rheumatoid arthritis receiving TNF-α inhibitors and development of machine learning models.

Toll-like receptor (TLR)-4 and TLR9 are known to play important roles in the immune system, and several studies have shown their association with the ...

Improved prediction of smoking status via isoform-aware RNA-seq deep learning models.

Most predictive models based on gene expression data do not leverage information related to gene splicing, despite the fact that splicing is a fundame...

Automatic Film Label Acquisition Method Based on Improved Neural Networks Optimized by Mutation Ant Colony Algorithm.

Nowadays, with the constant change of public aesthetic standards, a large number of new types and themes of film programs have emerged. For this reaso...

Artificial image objects for classification of breast cancer biomarkers with transcriptome sequencing data and convolutional neural network algorithms.

BACKGROUND: Transcriptome sequencing has been broadly available in clinical studies. However, it remains a challenge to utilize these data effectively...

Stem-cell based, machine learning approach for optimizing natural killer cell-based personalized immunotherapy for high-grade ovarian cancer.

Advanced high-grade serous ovarian cancer continues to be a therapeutic challenge for those affected using the current therapeutic interventions. Ther...

A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases.

Rare diseases (RDs) concern a broad range of disorders and can result from various origins. For a long time, the scientific community was unaware of R...

Audience Evaluation and Analysis of Symphony Performance Effects Based on the Genetic Neural Network Algorithm for the Multilayer Perceptron (GA-MLP-NN).

Traditional symphony performances need to obtain a large amount of data in terms of effect evaluation to ensure the authenticity and stability of the ...

Predicting TF-DNA Binding Motifs from ChIP-seq Datasets Using the Bag-Based Classifier Combined With a Multi-Fold Learning Scheme.

The rapid development of high-throughput sequencing technology provides unique opportunities for studying of transcription factor binding sites, but a...

Multi-Scale Capsule Network for Predicting DNA-Protein Binding Sites.

Discovering DNA-protein binding sites, also known as motif discovery, is the foundation for further analysis of transcription factors (TFs). Deep lear...

Framework for classification of cancer gene expression data using Bayesian hyper-parameter optimization.

Computational classification of cancers is an important research problem. Gene expression data has 1000s of features, very few samples, and a class im...

Integrating Statistical and Machine-Learning Approach for Meta-Analysis of Bisphenol A-Exposure Datasets Reveals Effects on Mouse Gene Expression within Pathways of Apoptosis and Cell Survival.

Bisphenols are important environmental pollutants that are extensively studied due to different detrimental effects, while the molecular mechanisms be...

WinBinVec: Cancer-Associated Protein-Protein Interaction Extraction and Identification of 20 Various Cancer Types and Metastasis Using Different Deep Learning Models.

Biophysical protein-protein interactions perform dominant roles in the initiation and progression of many cancer-related pathways. A protein-protein i...