8,822 research outputs found
An evaluation of DNA-damage response and cell-cycle pathways for breast cancer classification
Accurate subtyping or classification of breast cancer is important for
ensuring proper treatment of patients and also for understanding the molecular
mechanisms driving this disease. While there have been several gene signatures
proposed in the literature to classify breast tumours, these signatures show
very low overlaps, different classification performance, and not much relevance
to the underlying biology of these tumours. Here we evaluate DNA-damage
response (DDR) and cell cycle pathways, which are critical pathways implicated
in a considerable proportion of breast tumours, for their usefulness and
ability in breast tumour subtyping. We think that subtyping breast tumours
based on these two pathways could lead to vital insights into molecular
mechanisms driving these tumours. Here, we performed a systematic evaluation of
DDR and cell-cycle pathways for subtyping of breast tumours into the five known
intrinsic subtypes. Homologous Recombination (HR) pathway showed the best
performance in subtyping breast tumours, indicating that HR genes are strongly
involved in all breast tumours. Comparisons of pathway based signatures and two
standard gene signatures supported the use of known pathways for breast tumour
subtyping. Further, the evaluation of these standard gene signatures showed
that breast tumour subtyping, prognosis and survival estimation are all closely
related. Finally, we constructed an all-inclusive super-signature by combining
(union of) all genes and performing a stringent feature selection, and found it
to be reasonably accurate and robust in classification as well as prognostic
value. Adopting DDR and cell cycle pathways for breast tumour subtyping
achieved robust and accurate breast tumour subtyping, and constructing a
super-signature which contains feature selected mix of genes from these
molecular pathways as well as clinical aspects is valuable in clinical
practice.Comment: 28 pages, 7 figures, 6 table
Integrative methods for analyzing big data in precision medicine
We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of “Big Data” in biology and medicine. These data offer many opportunities to advance precision medicine. We outline key challenges in precision medicine and present recent advances in data integration-based methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, biomarkers discovery, and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face
Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review
Cancer remains one of the most challenging diseases to treat in the medical
field. Machine learning has enabled in-depth analysis of rich multi-omics
profiles and medical imaging for cancer diagnosis and prognosis. Despite these
advancements, machine learning models face challenges stemming from limited
labeled sample sizes, the intricate interplay of high-dimensionality data
types, the inherent heterogeneity observed among patients and within tumors,
and concerns about interpretability and consistency with existing biomedical
knowledge. One approach to surmount these challenges is to integrate biomedical
knowledge into data-driven models, which has proven potential to improve the
accuracy, robustness, and interpretability of model results. Here, we review
the state-of-the-art machine learning studies that adopted the fusion of
biomedical knowledge and data, termed knowledge-informed machine learning, for
cancer diagnosis and prognosis. Emphasizing the properties inherent in four
primary data types including clinical, imaging, molecular, and treatment data,
we highlight modeling considerations relevant to these contexts. We provide an
overview of diverse forms of knowledge representation and current strategies of
knowledge integration into machine learning pipelines with concrete examples.
We conclude the review article by discussing future directions to advance
cancer research through knowledge-informed machine learning.Comment: 41 pages, 4 figures, 2 table
Deep learning cardiac motion analysis for human survival prediction
Motion analysis is used in computer vision to understand the behaviour of
moving objects in sequences of images. Optimising the interpretation of dynamic
biological systems requires accurate and precise motion tracking as well as
efficient representations of high-dimensional motion trajectories so that these
can be used for prediction tasks. Here we use image sequences of the heart,
acquired using cardiac magnetic resonance imaging, to create time-resolved
three-dimensional segmentations using a fully convolutional network trained on
anatomical shape priors. This dense motion model formed the input to a
supervised denoising autoencoder (4Dsurvival), which is a hybrid network
consisting of an autoencoder that learns a task-specific latent code
representation trained on observed outcome data, yielding a latent
representation optimised for survival prediction. To handle right-censored
survival outcomes, our network used a Cox partial likelihood loss function. In
a study of 302 patients the predictive accuracy (quantified by Harrell's
C-index) was significantly higher (p < .0001) for our model C=0.73 (95 CI:
0.68 - 0.78) than the human benchmark of C=0.59 (95 CI: 0.53 - 0.65). This
work demonstrates how a complex computer vision task using high-dimensional
medical image data can efficiently predict human survival
Integrative methods for analysing big data in precision medicine
We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of “Big Data” in biology and medicine. These data offer many opportunities to advance precision medicine. We outline key challenges in precision medicine and present recent advances in data integration-based methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, biomarkers discovery, and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face
Semi-supervised ViT knowledge distillation network with style transfer normalization for colorectal liver metastases survival prediction
Colorectal liver metastases (CLM) significantly impact colon cancer patients,
influencing survival based on systemic chemotherapy response. Traditional
methods like tumor grading scores (e.g., tumor regression grade - TRG) for
prognosis suffer from subjectivity, time constraints, and expertise demands.
Current machine learning approaches often focus on radiological data, yet the
relevance of histological images for survival predictions, capturing intricate
tumor microenvironment characteristics, is gaining recognition. To address
these limitations, we propose an end-to-end approach for automated prognosis
prediction using histology slides stained with H&E and HPS. We first employ a
Generative Adversarial Network (GAN) for slide normalization to reduce staining
variations and improve the overall quality of the images that are used as input
to our prediction pipeline. We propose a semi-supervised model to perform
tissue classification from sparse annotations, producing feature maps. We use
an attention-based approach that weighs the importance of different slide
regions in producing the final classification results. We exploit the extracted
features for the metastatic nodules and surrounding tissue to train a prognosis
model. In parallel, we train a vision Transformer (ViT) in a knowledge
distillation framework to replicate and enhance the performance of the
prognosis prediction. In our evaluation on a clinical dataset of 258 patients,
our approach demonstrates superior performance with c-indexes of 0.804 (0.014)
for OS and 0.733 (0.014) for TTR. Achieving 86.9% to 90.3% accuracy in
predicting TRG dichotomization and 78.5% to 82.1% accuracy for the 3-class TRG
classification task, our approach outperforms comparative methods. Our proposed
pipeline can provide automated prognosis for pathologists and oncologists, and
can greatly promote precision medicine progress in managing CLM patients.Comment: 16 pages, 7 figures and 7 tables. Submitted to Medical Journal
Analysis (MedIA) journa
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