A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data
Background There is an increasing interest in the use of Deep Learning (DL) based
methods as a supporting analytical framework in oncology. However, most direct …
methods as a supporting analytical framework in oncology. However, most direct …
Integrated multi-omics analysis of ovarian cancer using variational autoencoders
Cancer is a complex disease that deregulates cellular functions at various molecular levels
(eg, DNA, RNA, and proteins). Integrated multi-omics analysis of data from these levels is …
(eg, DNA, RNA, and proteins). Integrated multi-omics analysis of data from these levels is …
Multiple-instance learning of somatic mutations for the classification of tumour type and the prediction of microsatellite status
Large-scale genomic data are well suited to analysis by deep learning algorithms. However,
for many genomic datasets, labels are at the level of the sample rather than for individual …
for many genomic datasets, labels are at the level of the sample rather than for individual …
Multi-approach bioinformatics analysis of curated omics data provides a gene expression panorama for multiple cancer types
Studies describing the expression patterns and biomarkers for the tumoral process increase
in number every year. The availability of new datasets, although essential, also creates a …
in number every year. The availability of new datasets, although essential, also creates a …
Mutation-Attention (MuAt): deep representation learning of somatic mutations for tumour typing and subtyping
Background Cancer genome sequencing enables accurate classification of tumours and
tumour subtypes. However, prediction performance is still limited using exome-only …
tumour subtypes. However, prediction performance is still limited using exome-only …
Representation learning for the clustering of multi-omics data
G Viaud, P Mayilvahanan… - IEEE/ACM Transactions …, 2021 - ieeexplore.ieee.org
The integration of several sources of data for the identification of subtypes of diseases has
gained attention over the past few years. The heterogeneity and the high dimensions of the …
gained attention over the past few years. The heterogeneity and the high dimensions of the …
Stability of scRNA-Seq Analysis Workflows is Susceptible to Preprocessing and is Mitigated by Regularized or Supervised Approaches
Background: Statistical methods developed to address various questions in single-cell
datasets show increased variability to different parameter regimes. In order to delineate …
datasets show increased variability to different parameter regimes. In order to delineate …
Scalable privacy-preserving cancer type prediction with homomorphic encryption
Machine Learning (ML) alleviates the challenges of high-dimensional data analysis and
improves decision making in critical applications like healthcare. Effective cancer type from …
improves decision making in critical applications like healthcare. Effective cancer type from …
Characteristics of pan-cancer patients with ultrahigh tumor mutation burden
H Yuan, J Ji, M Shi, Y Shi, J Liu, J Wu, C Yang… - Frontiers in …, 2021 - frontiersin.org
Background Tumor mutation burden has been proven to be a good predictor for the efficacy
of immunotherapy, especially in patients with hypermutation. However, most research …
of immunotherapy, especially in patients with hypermutation. However, most research …
An image compression approach for efficient pneumonia recognition
S Nefoussi, A Amamra… - The Imaging Science …, 2024 - Taylor & Francis
Increasingly, analytics such as classification and detection suffer from a significant amount of
generated visual data. Nonetheless, recent approaches have not given substantial thought …
generated visual data. Nonetheless, recent approaches have not given substantial thought …