Genetics

Application of High Throughput Technologies in the Development of Acute Myeloid Leukemia Therapy: Challenges and Progress.

Acute myeloid leukemia (AML) is a complex hematological malignancy characterized by extensive heterogeneity in genetics, response to therapy and long-term outcomes, making it a prototype example of development for personalized medicine. Given the accessibility to hematologic malignancy patient samples and recent advances in high-throughput technologies, large amounts of biological data that are cl...

Designing sensitive viral diagnostics with machine learning.

Design of nucleic acid-based viral diagnostics typically follows heuristic rules and, to contend with viral variation, focuses on a genome's conserved regions. A design process could, instead, directly optimize diagnostic effectiveness using a learned model of sensitivity for targets and their variants. Toward that goal, we screen 19,209 diagnostic-target pairs, concentrated on CRISPR-based diagno...

A brief review of machine learning methods for RNA methylation sites prediction.

Thanks to the tremendous advancement of deep sequencing and large-scale profiling, epitranscriptomics has become a rapidly growing field. As one of th...

Two-dimensional CNN-based distinction of human emotions from EEG channels selected by multi-objective evolutionary algorithm.

In this study we explore how different levels of emotional intensity (Arousal) and pleasantness (Valence) are reflected in electroencephalographic (EE...

FusionAI, a DNA-sequence-based deep learning protocol reduces the false positives of human fusion gene prediction.

Even though there were many tool developments of fusion gene prediction from NGS data, too many false positives are still an issue. Wise use of the ge...

DrABC: deep learning accurately predicts germline pathogenic mutation status in breast cancer patients based on phenotype data.

BACKGROUND: Identifying breast cancer patients with DNA repair pathway-related germline pathogenic variants (GPVs) is important for effectively employ...

Deep learning using bulk RNA-seq data expands cell landscape identification in tumor microenvironment.

The tumor microenvironment (TME) profoundly influences tumor progression and affects immunotherapy responses and resistance. Understanding its heterog...

Proposing a novel deep network for detecting COVID-19 based on chest images.

The rapid outbreak of coronavirus threatens humans' life all around the world. Due to the insufficient diagnostic infrastructures, developing an accur...

Augmentation of Transcriptomic Data for Improved Classification of Patients with Respiratory Diseases of Viral Origin.

To better understand the molecular basis of respiratory diseases of viral origin, high-throughput gene-expression data are frequently taken by means o...

Inferring RNA-binding protein target preferences using adversarial domain adaptation.

Precise identification of target sites of RNA-binding proteins (RBP) is important to understand their biochemical and cellular functions. A large amou...

GGA-MLP: A Greedy Genetic Algorithm to Optimize Weights and Biases in Multilayer Perceptron.

The task of designing an Artificial Neural Network (ANN) can be thought of as an optimization problem that involves many parameters whose optimal valu...

Borophene as an emerging 2D flatland for biomedical applications: current challenges and future prospects.

Recently, two-dimensional (2D)-borophene has emerged as a remarkable translational nanomaterial substituting its predecessors in the field of biomedic...

Prediction of Neonatal Respiratory Distress Biomarker Concentration by Application of Machine Learning to Mid-Infrared Spectra.

The authors of this study developed the use of attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) combined with machine l...

Effects of Irbesartan and Amlodipine Besylate Tablets on the Intestinal Microflora of Rats With Hypertensive Renal Damage.

The present study aimed to investigate the effects of irbesartan and amlodipine besylate tablets on the intestinal microflora of rats with hypertensi...

Identification of a novel four-gene diagnostic signature for patients with sepsis by integrating weighted gene co-expression network analysis and support vector machine algorithm.

Sepsis is a life-threatening condition in which the immune response is directed towards the host tissues, causing organ failure. Since sepsis does not...

Machine Learning Predictability of Clinical Next Generation Sequencing for Hematologic Malignancies to Guide High-Value Precision Medicine.

Advancing diagnostic testing capabilities such as clinical next generation sequencing methods offer the potential to diagnose, risk stratify, and guid...

Machine learning models for accurate prioritization of variants of uncertain significance.

The growing use of next-generation sequencing technologies on genetic diagnosis has produced an exponential increase in the number of variants of unce...

From shallow to deep: some lessons learned from application of machine learning for recognition of functional genomic elements in human genome.

Identification of genomic signals as indicators for functional genomic elements is one of the areas that received early and widespread application of ...

High-throughput screening, next generation sequencing and machine learning: advanced methods in enzyme engineering.

Enzyme engineering is an important biotechnological process capable of generating tailored biocatalysts for applications in industrial chemical conver...

Predicting Mutational Status of Driver and Suppressor Genes Directly from Histopathology With Deep Learning: A Systematic Study Across 23 Solid Tumor Types.

In the last four years, advances in Deep Learning technology have enabled the inference of selected mutational alterations directly from routine histo...