Genetics

Unsupervised generative and graph representation learning for modelling cell differentiation.

Using machine learning techniques to build representations from biomedical data can help us understand the latent biological mechanism of action and lead to important discoveries. Recent developments in single-cell RNA-sequencing protocols have allowed measuring gene expression for individual cells in a population, thus opening up the possibility of finding answers to biomedical questions about ce...

Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population.

Since December 2019 the novel coronavirus SARS-CoV-2 has been identified as the cause of the pandemic COVID-19. Early symptoms overlap with other common conditions such as common cold and Influenza, making early screening and diagnosis are crucial goals for health practitioners. The aim of the study was to use machine learning (ML), an artificial neural network (ANN) and a simple statistical test ...

A Support Vector Machine Model Predicting the Risk of Duodenal Cancer in Patients with Familial Adenomatous Polyposis at the Transcript Levels.

OBJECTIVE: Familial adenomatous polyposis (FAP) is one major type of inherited duodenal cancer. The estimate of duodenal cancer risk in patients with ...

Translating big data to better treatment in bipolar disorder - a manifesto for coordinated action.

Bipolar disorder (BD) is a major healthcare and socio-economic challenge. Despite its substantial burden on society, the research activity in BD is mu...

Differentiating molecular etiologies of Angelman syndrome through facial phenotyping using deep learning.

Angelman syndrome (AS) is caused by several genetic mechanisms that impair the expression of maternally-inherited UBE3A through deletions, paternal un...

Machine Learning on DNA-Encoded Libraries: A New Paradigm for Hit Finding.

DNA-encoded small molecule libraries (DELs) have enabled discovery of novel inhibitors for many distinct protein targets of therapeutic value. We demo...

Decoding whole-genome mutational signatures in 37 human pan-cancers by denoising sparse autoencoder neural network.

Millions of somatic mutations have recently been discovered in cancer genomes. These mutations in cancer genomes occur due to internal and external mu...

A network-based computational framework to predict and differentiate functions for gene isoforms using exon-level expression data.

MOTIVATION: Alternative splicing makes significant contributions to functional diversity of transcripts and proteins. Many alternatively spliced gene ...

Application of Whole-Genome Sequences and Machine Learning in Source Attribution of Salmonella Typhimurium.

Prevention of the emergence and spread of foodborne diseases is an important prerequisite for the improvement of public health. Source attribution mod...

Predicting geographic location from genetic variation with deep neural networks.

Most organisms are more closely related to nearby than distant members of their species, creating spatial autocorrelations in genetic data. This allow...

Acute myeloid leukemia and artificial intelligence, algorithms and new scores.

Artificial intelligence, and more narrowly machine-learning, is beginning to expand humanity's capacity to analyze increasingly large and complex data...

Matrix factorization with neural network for predicting circRNA-RBP interactions.

BACKGROUND: Circular RNA (circRNA) has been extensively identified in cells and tissues, and plays crucial roles in human diseases and biological proc...

Homogeneous DNA-only keypad locks enable one-pot assay of multi-inputs.

Homogeneous DNA-only keypad locks were built with multi-stranded scalable junction substrates and a series of double-stranded eliminators to different...

Effects of CYP2C19*17 Genetic Polymorphisms on the Steady-State Concentration of Diazepam in Patients With Alcohol Withdrawal Syndrome.

Diazepam is one of the most widely prescribed tranquilizers for the therapy of alcohol withdrawal syndrome (AWS), which includes the symptoms of anxi...

Using Reactome to build an autophagy mechanism knowledgebase.

The 21st century has revealed much about the fundamental cellular process of autophagy. Autophagy controls the catabolism and recycling of various cel...

Prediction of N6-methyladenosine sites using convolution neural network model based on distributed feature representations.

N-methyladenosine (mA) is a well-studied and most common interior messenger RNA (mRNA) modification that plays an important function in cell developme...

Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov.

A novel coronavirus, called 2019-nCoV, was recently found in Wuhan, Hubei Province of China, and now is spreading across China and other parts of the ...

Powerful, transferable representations for molecules through intelligent task selection in deep multitask networks.

Chemical representations derived from deep learning are emerging as a powerful tool in areas such as drug discovery and materials innovation. Currentl...

Machine learning uncovers cell identity regulator by histone code.

Conversion between cell types, e.g., by induced expression of master transcription factors, holds great promise for cellular therapy. Our ability to m...

CRISPRpred(SEQ): a sequence-based method for sgRNA on target activity prediction using traditional machine learning.

BACKGROUND: The latest works on CRISPR genome editing tools mainly employs deep learning techniques. However, deep learning models lack explainability...