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Machine learning based toxicity prediction: from chemical structural description to transcriptome analysis
Toxicity prediction is very important to public health. Among its many applications, toxicity
prediction is essential to reduce the cost and labor of a drug's preclinical and clinical trials …
prediction is essential to reduce the cost and labor of a drug's preclinical and clinical trials …
[HTML][HTML] Deep learning for genomics: from early neural nets to modern large language models
The data explosion driven by advancements in genomic research, such as high-throughput
sequencing techniques, is constantly challenging conventional methods used in genomics …
sequencing techniques, is constantly challenging conventional methods used in genomics …
A review on prediction and prognosis of the prostate cancer and gleason grading of prostatic carcinoma using deep transfer learning based approaches
Prostate cancer is a dangerous type of cancer that kills a lot of men because it is hard to
diagnose. Images taken of people with carcinoma have complex and important parts that are …
diagnose. Images taken of people with carcinoma have complex and important parts that are …
Breast cancer prediction from microRNA profiling using random subspace ensemble of LDA classifiers via Bayesian optimization
Breast cancer rates are rising. It also remains the second principal reason for cancer-related
mortality in females, and the mortality rate is also drastically rising. In recent years …
mortality in females, and the mortality rate is also drastically rising. In recent years …
Out-of-distribution generalization from labelled and unlabelled gene expression data for drug response prediction
Data discrepancy between preclinical and clinical datasets poses a major challenge for
accurate drug response prediction based on gene expression data. Different methods of …
accurate drug response prediction based on gene expression data. Different methods of …
AITL: adversarial inductive transfer learning with input and output space adaptation for pharmacogenomics
Motivation The goal of pharmacogenomics is to predict drug response in patients using their
single-or multi-omics data. A major challenge is that clinical data (ie patients) with drug …
single-or multi-omics data. A major challenge is that clinical data (ie patients) with drug …
Biological interpretation of deep neural network for phenotype prediction based on gene expression
Background The use of predictive gene signatures to assist clinical decision is becoming
more and more important. Deep learning has a huge potential in the prediction of phenotype …
more and more important. Deep learning has a huge potential in the prediction of phenotype …
Feature selection with ensemble learning for prostate cancer diagnosis from microarray gene expression
Cancer diagnosis using machine learning algorithms is one of the main topics of research in
computer-based medical science. Prostate cancer is considered one of the reasons that are …
computer-based medical science. Prostate cancer is considered one of the reasons that are …
[PDF][PDF] DeepHealth: Deep Learning for Health Informatics reviews, challenges, and opportunities on medical imaging, electronic health records, genomics, sensing …
CCS Concepts:• Computing methodologies→ Machine learning approaches; Machine
learning;• Social and professional topics→ Computing/technology policy; Medical …
learning;• Social and professional topics→ Computing/technology policy; Medical …
MetastaSite: Predicting metastasis to different sites using deep learning with gene expression data
Deep learning has massive potential in predicting phenotype from different omics profiles.
However, deep neural networks are viewed as black boxes, providing predictions without …
However, deep neural networks are viewed as black boxes, providing predictions without …