Genetics

Latest AI and machine learning research in genetics for healthcare professionals.

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Role of Electrostatic Interactions on Supramolecular Organization in Calf-Thymus DNA Solutions under Flow.

Previous investigations were conducted on two concentrations of DNA solution: 4 mg/mL, for which it ...

Modeling daily suspended sediment load using improved support vector machine model and genetic algorithm.

Prediction of sediment volume and sediment load is always one of the important issues for decision-m...

Novel Neural Network Approach to Predict Drug-Target Interactions Based on Drug Side Effects and Genome-Wide Association Studies.

AIMS: We propose a novel machine learning approach to expand the knowledge about drug-target interac...

Automated detection of cancer cells in effusion specimens by DNA karyometry.

BACKGROUND: The average sensitivity of conventional cytology for the identification of cancer cells ...

Ensemble of machine learning algorithms using the stacked generalization approach to estimate the warfarin dose.

Warfarin dosing remains challenging due to narrow therapeutic index and highly individual variabilit...

Drug Repurposing Prediction for Immune-Mediated Cutaneous Diseases using a Word-Embedding-Based Machine Learning Approach.

Immune-mediated diseases affect more than 20% of the population, and many autoimmune diseases affect...

Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance.

Mycobacterium tuberculosis is a serious human pathogen threat exhibiting complex evolution of antimi...

Structure and Protein Interaction-Based Gene Ontology Annotations Reveal Likely Functions of Uncharacterized Proteins on Human Chromosome 17.

Understanding the function of human proteins is essential to decipher the molecular mechanisms of hu...

CamurWeb: a classification software and a large knowledge base for gene expression data of cancer.

BACKGROUND: The high growth of Next Generation Sequencing data currently demands new knowledge extra...

Deep-RBPPred: Predicting RNA binding proteins in the proteome scale based on deep learning.

RNA binding protein (RBP) plays an important role in cellular processes. Identifying RBPs by computa...

Recurrent Neural Network for Predicting Transcription Factor Binding Sites.

It is well known that DNA sequence contains a certain amount of transcription factors (TF) binding s...

Prediction of RNA-protein interactions by combining deep convolutional neural network with feature selection ensemble method.

RNA-protein interaction (RPI) plays an important role in the basic cellular processes of organisms. ...

Effective DNA binding protein prediction by using key features via Chou's general PseAAC.

DNA-binding proteins (DBPs) are responsible for several cellular functions, starting from our immuni...

Selection of recycling centre locations by using the interval type-2 fuzzy sets and two-objective genetic algorithm.

In the sense of contemporary industrial challenges, it may be said that many issues in the field of ...

Dense neural networks for predicting chromatin conformation.

BACKGROUND: DNA inside eukaryotic cells wraps around histones to form the 11nm chromatin fiber that ...

New bacitracin-resistant nisin-producing strain of and its physiological characterization.

Nisin A belonging to the class I bacteriocins and produced by subsp. is widely used in many countr...

Diagnosis of T-cell-mediated kidney rejection in formalin-fixed, paraffin-embedded tissues using RNA-Seq-based machine learning algorithms.

Molecular diagnosis is being increasingly used in transplant pathology to render more objective and ...

Combining High Speed ELM Learning with a Deep Convolutional Neural Network Feature Encoding for Predicting Protein-RNA Interactions.

Emerging evidence has shown that RNA plays a crucial role in many cellular processes, and their biol...

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