The following sections are included:Bioinformatics is a Mature DisciplineThe Golden Era of Bioinformatics Has BegunNo-Boundary Thinking in BioinformaticsReferences.
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Sex classification of 3D skull images using deep neural networks.
Sci Rep
June 2024
Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, CA, USA.
Article Synopsis
- Researchers examined the characteristics of faces that define them as feminine or masculine, focusing on deep learning models for sex classification based on 3D skull images.
- The study utilized a unique dataset of 98 skull samples from Cedars Sinai Medical Center, assessing the performance of different neural network architectures: Resnet3D, PointNet++, and MeshNet, with PointNet++ achieving the highest accuracy.
- Additionally, the researchers developed a new method using morphological gradients to better visualize the features influencing model decisions, offering an improved alternative to traditional saliency maps.
No-boundary thinking for artificial intelligence in bioinformatics and education.
Front Bioinform
January 2024
Biology Department, University of Dallas, Irving, TX, United States.
No-boundary thinking enables the scientific community to reflect in a thoughtful manner and discover new opportunities, create innovative solutions, and break through barriers that might have otherwise constrained their progress. This concept encourages thinking without being confined by traditional rules, limitations, or established norms, and a mindset that is not limited by previous work, leading to fresh perspectives and innovative outcomes. So, where do we see the field of artificial intelligence (AI) in bioinformatics going in the next 30 years? That was the theme of a "No-Boundary Thinking" Session as part of the Mid-South Computational Bioinformatics Society's (MCBIOS) 19th annual meeting in Irving, Texas.
View Article and Find Full Text PDFStudy COVID-19 Severity of Patients Admitted to Emergency Room (ER) with Chest X-ray Images.
medRxiv
December 2022
Center for No-Boundary Thinking (CNBT), Arkansas State University, Jonesboro, Arkansas.
We have conducted a study of the COVID-19 severity with the chest x-ray images, a private dataset collected from our collaborator St Bernards Medical Center. The dataset is comprised of chest x-ray images from 1,550 patients who were admitted to emergency room (ER) and were all tested positive for COVID-19. Our study is focused on the following two questions: (1) To predict patients hospital staying duration, based on the chest x-ray image which was taken when the patient was admitted to the ER.
View Article and Find Full Text PDFPredicting invasion risk of grasses in novel environments requires improved genomic understanding of adaptive potential.
Am J Bot
December 2022
Department of Biological Sciences, Arkansas State University, State University, AR, USA.
Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning.
Front Plant Sci
March 2022
Department of Computer Science, Arkansas State University, Jonesboro, AR, United States.
Unmanned aerial vehicles (UAVs) equipped with multispectral sensors offer high spatial and temporal resolution imagery for monitoring crop stress at early stages of development. Analysis of UAV-derived data with advanced machine learning models could improve real-time management in agricultural systems, but guidance for this integration is currently limited. Here we compare two deep learning-based strategies for early warning detection of crop stress, using multitemporal imagery throughout the growing season to predict field-scale yield in irrigated rice in eastern Arkansas.
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