1,016 research outputs found
Ovarian Cancer Data Analysis using Deep Learning: A Systematic Review from the Perspectives of Key Features of Data Analysis and AI Assurance
Background and objectives: By extracting this information, Machine or Deep
Learning (ML/DL)-based autonomous data analysis tools can assist clinicians and
cancer researchers in discovering patterns and relationships from complex data
sets. Many DL-based analyses on ovarian cancer (OC) data have recently been
published. These analyses are highly diverse in various aspects of cancer
(e.g., subdomain(s) and cancer type they address) and data analysis features.
However, a comprehensive understanding of these analyses in terms of these
features and AI assurance (AIA) is currently lacking. This systematic review
aims to fill this gap by examining the existing literature and identifying
important aspects of OC data analysis using DL, explicitly focusing on the key
features and AI assurance perspectives. Methods: The PRISMA framework was used
to conduct comprehensive searches in three journal databases. Only studies
published between 2015 and 2023 in peer-reviewed journals were included in the
analysis. Results: In the review, a total of 96 DL-driven analyses were
examined. The findings reveal several important insights regarding DL-driven
ovarian cancer data analysis: - Most studies 71% (68 out of 96) focused on
detection and diagnosis, while no study addressed the prediction and prevention
of OC. - The analyses were predominantly based on samples from a non-diverse
population (75% (72/96 studies)), limited to a geographic location or country.
- Only a small proportion of studies (only 33% (32/96)) performed integrated
analyses, most of which used homogeneous data (clinical or omics). - Notably, a
mere 8.3% (8/96) of the studies validated their models using external and
diverse data sets, highlighting the need for enhanced model validation, and -
The inclusion of AIA in cancer data analysis is in a very early stage; only
2.1% (2/96) explicitly addressed AIA through explainability
High-Dimensional Joint Estimation of Multiple Directed Gaussian Graphical Models
We consider the problem of jointly estimating multiple related directed
acyclic graph (DAG) models based on high-dimensional data from each graph. This
problem is motivated by the task of learning gene regulatory networks based on
gene expression data from different tissues, developmental stages or disease
states. We prove that under certain regularity conditions, the proposed
-penalized maximum likelihood estimator converges in Frobenius norm to
the adjacency matrices consistent with the data-generating distributions and
has the correct sparsity. In particular, we show that this joint estimation
procedure leads to a faster convergence rate than estimating each DAG model
separately. As a corollary, we also obtain high-dimensional consistency results
for causal inference from a mix of observational and interventional data. For
practical purposes, we propose \emph{jointGES} consisting of Greedy Equivalence
Search (GES) to estimate the union of all DAG models followed by variable
selection using lasso to obtain the different DAGs, and we analyze its
consistency guarantees. The proposed method is illustrated through an analysis
of simulated data as well as epithelial ovarian cancer gene expression data
A Study Of Computational Problems In Computational Biology And Social Networks: Cancer Informatics And Cascade Modelling
It is undoubtedly that everything in this world is related and nothing independently exists. Entities interact together to form groups, resulting in many complex networks. Examples involve functional regulation models of proteins in biology, communities of people within social network. Since complex networks are ubiquitous in daily life, network learning had been gaining momentum in a variety of discipline like computer science, economics and biology. This call for new technique in exploring the structure as well as the interactions of network since it provides insight in understanding how the network works and deepening our knowledge of the subject in hand. For example, uncovering proteins modules helps us understand what causes lead to certain disease and how protein co-regulate each others. Therefore, my dissertation takes on problems in computational biology and social network: cancer informatics and cascade model-ling. In cancer informatics, identifying specific genes that cause cancer (driver genes) is crucial in cancer research. The more drivers identified, the more options to treat the cancer with a drug to act on that gene. However, identifying driver gene is not easy. Cancer cells are undergoing rapid mutation and are compromised in regards to the body\u27s normally DNA repair mechanisms. I employed Markov chain, Bayesian network and graphical model to identify cancer drivers. I utilize heterogeneous sources of information to discover cancer drivers and unlocking the mechanism behind cancer. Above all, I encode various pieces of biological information to form a multi-graph and trigger various Markov chains in it and rank the genes in the aftermath. We also leverage probabilistic mixed graphical model to learn the complex and uncertain relationships among various bio-medical data. On the other hand, diffusion of information over the network had drawn up great interest in research community. For example, epidemiologists observe that a person becomes ill but they can neither determine who infected the patient nor the infection rate of each individual. Therefore, it is critical to decipher the mechanism underlying the process since it validates efforts for preventing from virus infections. We come up with a new modeling to model cascade data in three different scenario
A Review of Artificial Intelligence in Breast Imaging
With the increasing dominance of artificial intelligence (AI) techniques, the important prospects for their application have extended to various medical fields, including domains such as in vitro diagnosis, intelligent rehabilitation, medical imaging, and prognosis. Breast cancer is a common malignancy that critically affects womenās physical and mental health. Early breast cancer screeningāthrough mammography, ultrasound, or magnetic resonance imaging (MRI)ācan substantially improve the prognosis for breast cancer patients. AI applications have shown excellent performance in various image recognition tasks, and their use in breast cancer screening has been explored in numerous studies. This paper introduces relevant AI techniques and their applications in the field of medical imaging of the breast (mammography and ultrasound), specifically in terms of identifying, segmenting, and classifying lesions; assessing breast cancer risk; and improving image quality. Focusing on medical imaging for breast cancer, this paper also reviews related challenges and prospects for AI
Current challenges in glioblastoma : intratumour heterogeneity, residual disease and models to predict disease recurrence
Glioblastoma (GB) is the most common malignant primary brain tumour, and despite the availability of chemotherapy and radiotherapy to combat the disease, overall survival remains low with a high incidence of tumour recurrence. Technological advances are continually improving our understanding of the disease and in particular our knowledge of clonal evolution, intratumour heterogeneity and possible reservoirs of residual disease. These may inform how we approach clinical treatment and recurrence in GB. Mathematical modelling (including neural networks), and strategies such as multiple-sampling during tumour resection and genetic analysis of circulating cancer cells, may be of great future benefit to help predict the nature of residual disease and resistance to standard and molecular therapies in GB
Applications of Machine Learning in Cancer Prediction and Prognosis
Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to ālearnā from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on āolderā technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15ā25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression
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