A5057761555- Computational Drug Discovery Methods
- Protein Structure and Dynamics
- Analytical Chemistry and Chromatography
- Online Learning and Analytics
- Radiomics and Machine Learning in Medical Imaging
- Metabolomics and Mass Spectrometry Studies
- Machine Learning in Materials Science
- Statistical Methods in Clinical Trials
- Pharmacogenetics and Drug Metabolism
- Bioinformatics and Genomic Networks
- Cancer Genomics and Diagnostics
- AI in cancer detection
- Genetics, Bioinformatics, and Biomedical Research
Argonne National Laboratory
2022-2025
Anhui Normal University
2022
Abstract The rapid evolution of machine learning has led to a proliferation sophisticated models for predicting therapeutic responses in cancer. While many these show promise research, standards clinical evaluation and adoption are lacking. Here, we propose seven hallmarks by which predictive oncology can be assessed compared. These Data Relevance Actionability, Expressive Architecture, Standardized Benchmarking, Generalizability, Interpretability, Accessibility Reproducibility, Fairness....
Abstract Drug response prediction (DRP) methods tackle the complex task of associating effectiveness small molecules with specific genetic makeup patient. Anti-cancer DRP is a particularly challenging requiring costly experiments as underlying pathogenic mechanisms are broad and associated multiple genomic pathways. The scientific community has exerted significant efforts to generate public drug screening datasets, giving path various machine learning models that attempt reason over data...
Cancer is a heterogeneous disease in that tumors of the same histology type can respond differently to treatment. Anti-cancer drug response prediction paramount importance for both development and patient treatment design. Although various computational methods data have been used develop models, it remains challenging problem due complexities cancer mechanisms cancer-drug interactions. To better characterize interaction between drugs, we investigate feasibility integrating computationally...
Drug response prediction (DRP) methods tackle the complex task of associating effectiveness small molecules with specific genetic makeup patient. Anti-cancer DRP is a particularly challenging requiring costly exper-iments as underlying pathogenic mechanisms are broad and associated multiple genomic pathways. The scientific community has exerted significant efforts to generate public drug screening datasets, giving path various machine learning (ML) models that attempt reason over data space...
Human cancers present a significant public health challenge and require the discovery of novel drugs through translational research. Transcriptomics profiling data that describes molecular activities in tumors cancer cell lines are widely utilized for predicting anti-cancer drug responses. However, existing AI models face challenges due to noise transcriptomics lack biological interpretability. To overcome these limitations, we introduce VETE (Variational Explanatory Encoder), neural network...
Cancer drug response prediction (DRP) models present a promising approach towards precision oncology, tailoring treatments to individual patient profiles. While deep learning (DL) methods have shown great potential in this area, that can be successfully translated into clinical practice and shed light on the molecular mechanisms underlying treatment will likely emerge from collaborative research efforts. This highlights need for reusable adaptable improved tested by wider scientific...
This work studies a particular setting for regression problems – tasks with complex combinatorial data space where samples can be divided into distinct groups. Anti-cancer drug response prediction is perfect example of this setting, in which each sample includes cancer biological features and chemical information. Many existing works pan-drug pan-cancer modeling treat different combinations drugs cancers as individual samples. A potential problem these that model may heavily influenced...
Patient-derived xenografts (PDXs) are an appealing platform for preclinical drug studies because the in vivo environment of PDXs helps preserve tumor heterogeneity and usually better mimics response patients with cancer compared to CCLs. We investigate multimodal neural network (MM-Net) data augmentation prediction PDXs. The MM-Net learns predict using descriptors, gene expressions (GE), histology whole-slide images (WSIs) where multi-modality refers features. explore whether integration...
Students' concentration status is not only an evaluation method for students' acceptance of courses but also important reference factor the quality teachers' teaching. This paper proposes a deep learning-based student analysis system. We use methods and models learning to analyze students from two aspects fatigue state distraction behavior. The main contents are as follows: input each frame image obtained in video stream into third-party face recognition module Dlib retrained YOLOv5 series...