AIMC Topic: Genomics

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Group Lasso Regularized Deep Learning for Cancer Prognosis from Multi-Omics and Clinical Features.

Genes
Accurate prognosis of patients with cancer is important for the stratification of patients, the optimization of treatment strategies, and the design of clinical trials. Both clinical features and molecular data can be used for this purpose, for insta...

netDx: interpretable patient classification using integrated patient similarity networks.

Molecular systems biology
Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis, and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse da...

TADKB: Family classification and a knowledge base of topologically associating domains.

BMC genomics
BACKGROUND: Topologically associating domains (TADs) are considered the structural and functional units of the genome. However, there is a lack of an integrated resource for TADs in the literature where researchers can obtain family classifications a...

Incorporating microbial community data with machine learning techniques to predict feed substrates in microbial fuel cells.

Biosensors & bioelectronics
The complicated interactions that occur in mixed-species biotechnologies, including biosensors, hinder chemical detection specificity. This lack of specificity limits applications in which biosensors may be deployed, such as those where an unknown fe...

Interpretable genotype-to-phenotype classifiers with performance guarantees.

Scientific reports
Understanding the relationship between the genome of a cell and its phenotype is a central problem in precision medicine. Nonetheless, genotype-to-phenotype prediction comes with great challenges for machine learning algorithms that limit their use i...

Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data.

Genes
It is very significant to explore the intrinsic differences in breast cancer subtypes. These intrinsic differences are closely related to clinical diagnosis and designation of treatment plans. With the accumulation of biological and medicine datasets...

Deep learning can see the unseeable: predicting molecular markers from MRI of brain gliomas.

Clinical radiology
This paper describes state-of-the-art methods for molecular biomarker prediction utilising magnetic resonance imaging. This review paper covers both classical machine learning approaches and deep learning approaches to identifying the predictive feat...

A multi-task convolutional deep neural network for variant calling in single molecule sequencing.

Nature communications
The accurate identification of DNA sequence variants is an important, but challenging task in genomics. It is particularly difficult for single molecule sequencing, which has a per-nucleotide error rate of ~5-15%. Meeting this demand, we developed Cl...

Introducing high school students to the Gene Ontology classification system.

F1000Research
We present a tutorial that introduces high school students to the Gene Ontology classification system which is widely used in genomics and systems biology studies to characterize large sets of genes based on functional and structural information. Thi...

Unsupervised Learning Approach for Comparing Multiple Transposon Insertion Sequencing Studies.

mSphere
Transposon insertion sequencing (TIS) is a widely used technique for conducting genome-scale forward genetic screens in bacteria. However, few methods enable comparison of TIS data across multiple replicates of a screen or across independent screens,...