Latest AI and machine learning research in genetics for healthcare professionals.
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects nearly 3% of children and has a strong genetic component. While hundreds of ASD risk genes have been identified through sequencing studies, the genetic heterogeneity of ASD makes identifying additional risk genes using these methods challenging. To predict candidate ASD risk genes, we developed a simple machine learning m...
Most cancers are genetically and phenotypically heterogeneous. This includes subpopulations of cells with different levels of sensitivity to chemotherapy, which may lead to treatment failure as the more resistant cells can survive drug treatment and continue to proliferate. While the genetic basis of resistance to many drugs is relatively well characterised, non-genetic factors are much less under...
Epigenetic DNA methylations are linked to the activation of oncogenes and inactivation of tumor suppressor genes. A reliable and label-free method to ...
Today, drug discovery and development is one of the fields where Artificial Intelligence (AI) is used extensively. Therefore, this study aims to syste...
Neurofibromatosis type 1 (NF1) is an autosomal dominant disorder caused by mutations in the gene, typically diagnosed during early childhood and char...
Dravet syndrome is a developmental and epileptic encephalopathy, characterized by the early onset of drug-resistant seizures and various comorbiditi...
Predicting genetic perturbations enables the identification of potentially crucial genes prior to wet-lab experiments, significantly improving overa...
Gene finding is the task of identifying the locations of coding sequences within the vast amount of genetic code contained in the genome. With an ev...
The introduction of multimodal models is a huge step forward in Artificial Intelligence. A single model is trained to understand multiple modalities...
MOTIVATION: Single-cell RNA sequencing (scRNA-seq) analysis relies heavily on effective clustering to facilitate numerous downstream applications. Alt...
OBJECTIVES: Reliable methods for predicting pathological complete response (pCR) in non-small cell lung cancer (NSCLC) patients undergoing neoadjuvant...
SUMMARY: Currently available and frequently used tools for annotating antibiotic resistance genes (ARGs) in genomes and metagenomes provide results us...
Population pharmacokinetic (PopPK) modelling is a fundamental tool for understanding drug behaviour across diverse patient populations and enabling ...
Genome modeling conventionally treats gene sequence as a language, reflecting its structured motifs and long-range dependencies analogous to linguis...
GCsnap2 Cluster is a scalable, high performance tool for genomic context analysis, developed to overcome the limitations of its predecessor, GCsnap1...
DNA microarray technology enables the simultaneous measurement of expression levels of thousands of genes, thereby facilitating the understanding of...
Mixture interpretation is a central challenge in forensic science, where evidence often contains contributions from multiple sources. In the context...
Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), remains a major global health threat, compounded by the rise of extensively drug-resist...
To address the challenge of scarce computational resources in genomic modeling, we introduce GERM, a genomic foundation model with strong compressio...
The study of chromatin interactions has advanced considerably with technologies such as high-throughput chromosome conformation capture (Hi-C) sequenc...