Genetics

Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred secondary structure and evolutionary-derived mutational coupling.

MOTIVATION: Recently, AlphaFold2 achieved high experimental accuracy for the majority of proteins in Critical Assessment of Structure Prediction (CASP 14). This raises the hope that one day, we may achieve the same feat for RNA structure prediction for those structured RNAs, which is as fundamentally and practically important similar to protein structure prediction. One major factor in the recent ...

Homologues not needed: Structure prediction from a protein language model.

Accurate protein structure predictors use clusters of homologues, which disregard sequence specific effects. In this issue of Structure, Weißenow and colleagues report a deep learning-based tool, EMBER2, that efficiently predicts the distances in a protein structure from its amino acid sequence only. This approach should enable the analysis of mutation effects.

Intratumoral Resolution of Driver Gene Mutation Heterogeneity in Renal Cancer Using Deep Learning.

UNLABELLED: Intratumoral heterogeneity arising from tumor evolution poses significant challenges biologically and clinically. Dissecting this complexi...

Investigating Morphologic Correlates of Driver Gene Mutation Heterogeneity via Deep Learning.

Despite the crucial role of phenotypic and genetic intratumoral heterogeneity in understanding and predicting clinical outcomes for patients with canc...

KLFDAPC: a supervised machine learning approach for spatial genetic structure analysis.

Geographic patterns of human genetic variation provide important insights into human evolution and disease. A commonly used tool to detect and describ...

Adaptive sequencing using nanopores and deep learning of mitochondrial DNA.

Nanopore sequencing is an emerging technology that reads DNA by utilizing a unique method of detecting nucleic acid sequences and identifies the vario...

DeepLUCIA: predicting tissue-specific chromatin loops using Deep Learning-based Universal Chromatin Interaction Annotator.

MOTIVATION: The importance of chromatin loops in gene regulation is broadly accepted. There are mainly two approaches to predict chromatin loops: tran...

EPIMUTESTR: a nearest neighbor machine learning approach to predict cancer driver genes from the evolutionary action of coding variants.

Discovering rare cancer driver genes is difficult because their mutational frequency is too low for statistical detection by computational methods. EP...

Deep learning identifies A-to-I RNA edits using nanopore sequencing data.

Diagnosis of coronavirus disease 2019 and the potential role of deep learning: insights from the experience of Cairo University Hospitals.

OBJECTIVES: Early detection of coronavirus disease 2019 (COVID-19) is crucial for patients and public health to ensure pandemic control. We aimed to c...

Wasserstein GAN based Chest X-Ray Dataset Augmentation for Deep Learning Models: COVID-19 Detection Use-Case.

The novel coronavirus infection (COVID-19) is still continuing to be a concern for the entire globe. Since early detection of COVID-19 is of particula...

A novel ergodic cellular automaton gene network model towards efficient hardware-based genome simulator.

In this paper, a novel ergodic cellular automaton model of Hes1 mRNA and Hes1 protein network is presented. Detailed analyses reveal that the presente...

Generative Moment Matching Networks for Genotype Simulation.

The generation of synthetic genomic sequences using neural networks has potential to ameliorate privacy and data sharing concerns and to mitigate pote...

Natural Language Processing Approaches for Retrieval of Clinically Relevant Genomic Information in Cancer.

The accelerating impact of genomic data in clinical decision-making has generated a paradigm shift from treatment based on the anatomic origin of the ...

AutoDC: an automatic machine learning framework for disease classification.

MOTIVATION: The emergence of next-generation sequencing techniques opens up tremendous opportunities for researchers to uncover the basic mechanisms o...

Intelligent dynamic data perturbation OCDM encryption scheme based on cellular neural network and biological genetic encoding.

In this paper, an intelligent dynamic perturbation orthogonal chirp division multiplexing (OCDM) encryption scheme based on cellular neural network an...

Rapid and accurate identification of ribosomal RNA sequences via deep learning.

Advances in transcriptomic and translatomic techniques enable in-depth studies of RNA activity profiles and RNA-based regulatory mechanisms. Ribosomal...

Artificial Intelligence-Assisted Serial Analysis of Clinical Cancer Genomics Data Identifies Changing Treatment Recommendations and Therapeutic Targets.

PURPOSE: Given the pace of predictive biomarker and targeted therapy development, it is unknown whether repeat annotation of the same next-generation ...

Genome-wide cis-decoding for expression design in tomato using cistrome data and explainable deep learning.

In the evolutionary history of plants, variation in cis-regulatory elements (CREs) resulting in diversification of gene expression has played a centra...

DNA-binding protein prediction based on deep transfer learning.

The study of DNA binding proteins (DBPs) is of great importance in the biomedical field and plays a key role in this field. At present, many researche...