Identification of neoantigens for individualized therapeutic cancer vaccines
Somatic mutations in cancer cells can generate tumour-specific neoepitopes, which are
recognized by autologous T cells in the host. As neoepitopes are not subject to central …
recognized by autologous T cells in the host. As neoepitopes are not subject to central …
Variant calling and benchmarking in an era of complete human genome sequences
Genetic variant calling from DNA sequencing has enabled understanding of germline
variation in hundreds of thousands of humans. Sequencing technologies and variant-calling …
variation in hundreds of thousands of humans. Sequencing technologies and variant-calling …
Artificial intelligence and machine learning in precision and genomic medicine
S Quazi - Medical Oncology, 2022 - Springer
The advancement of precision medicine in medical care has led behind the conventional
symptom-driven treatment process by allowing early risk prediction of disease through …
symptom-driven treatment process by allowing early risk prediction of disease through …
Molecular convolutional neural networks with DNA regulatory circuits
Complex biomolecular circuits enabled cells with intelligent behaviour to survive before
neural brains evolved. Since DNA computing was first demonstrated in the mid-1990s …
neural brains evolved. Since DNA computing was first demonstrated in the mid-1990s …
Curated variation benchmarks for challenging medically relevant autosomal genes
J Wagner, ND Olson, L Harris, J McDaniel… - Nature …, 2022 - nature.com
The repetitive nature and complexity of some medically relevant genes poses a challenge
for their accurate analysis in a clinical setting. The Genome in a Bottle Consortium has …
for their accurate analysis in a clinical setting. The Genome in a Bottle Consortium has …
Training confounder-free deep learning models for medical applications
The presence of confounding effects (or biases) is one of the most critical challenges in
using deep learning to advance discovery in medical imaging studies. Confounders affect …
using deep learning to advance discovery in medical imaging studies. Confounders affect …
Computational analysis of cancer genome sequencing data
Distilling biologically meaningful information from cancer genome sequencing data requires
comprehensive identification of somatic alterations using rigorous computational methods …
comprehensive identification of somatic alterations using rigorous computational methods …
Computational network biology: data, models, and applications
Biological entities are involved in intricate and complex interactions, in which uncovering the
biological information from the network concepts are of great significance. Benefiting from …
biological information from the network concepts are of great significance. Benefiting from …
Automated MRI-based deep learning model for detection of Alzheimer's disease process
W Feng, NV Halm-Lutterodt, H Tang… - … Journal of Neural …, 2020 - World Scientific
In the context of neuro-pathological disorders, neuroimaging has been widely accepted as a
clinical tool for diagnosing patients with Alzheimer's disease (AD) and mild cognitive …
clinical tool for diagnosing patients with Alzheimer's disease (AD) and mild cognitive …
Emerging applications of machine learning in genomic medicine and healthcare
The integration of artificial intelligence technologies has propelled the progress of clinical
and genomic medicine in recent years. The significant increase in computing power has …
and genomic medicine in recent years. The significant increase in computing power has …