AIMC Topic: Algorithms

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Interpretability of Clinical Decision Support Systems Based on Artificial Intelligence from Technological and Medical Perspective: A Systematic Review.

Journal of healthcare engineering
BACKGROUND: Artificial intelligence (AI) has developed rapidly, and its application extends to clinical decision support system (CDSS) for improving healthcare quality. However, the interpretability of AI-driven CDSS poses significant challenges to w...

Lung Diseases Detection Using Various Deep Learning Algorithms.

Journal of healthcare engineering
The primary objective of this proposed framework work is to detect and classify various lung diseases such as pneumonia, tuberculosis, and lung cancer from standard X-ray images and Computerized Tomography (CT) scan images with the help of volume dat...

A Method for Predicting DNA Motif Length Based On Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics
A DNA motif is a sequence pattern shared by the DNA sequence segments that bind to a specific protein. Discovering motifs in a given DNA sequence dataset plays a vital role in studying gene expression regulation. As an important attribute of the DNA ...

A Learning Robust and Discriminative Shape Descriptor for Plant Species Identification.

IEEE/ACM transactions on computational biology and bioinformatics
Plant identification based on leaf images is a widely concerned application field in artificial intelligence and botany. The key problem is extracting robust discriminative features from leaf images and assigning a measure of similarity. This study p...

SENIES: DNA Shape Enhanced Two-Layer Deep Learning Predictor for the Identification of Enhancers and Their Strength.

IEEE/ACM transactions on computational biology and bioinformatics
Identifying enhancers is a critical task in bioinformatics due to their primary role in regulating gene expression. For this reason, various computational algorithms devoted to enhancer identification have been put forward over the years. More featur...

DeepSide: A Deep Learning Approach for Drug Side Effect Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Drug failures due to unforeseen adverse effects at clinical trials pose health risks for the participants and lead to substantial financial losses. Side effect prediction algorithms have the potential to guide the drug design process. LINCS L1000 dat...

Alignment-Free Sequence Comparison: A Systematic Survey From a Machine Learning Perspective.

IEEE/ACM transactions on computational biology and bioinformatics
The encounter of large amounts of biological sequence data generated during the last decades and the algorithmic and hardware improvements have offered the possibility to apply machine learning techniques in bioinformatics. While the machine learning...

iDRBP-EL: Identifying DNA- and RNA- Binding Proteins Based on Hierarchical Ensemble Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Identification of DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs) from the primary sequences is essential for further exploring protein-nucleic acid interactions. Previous studies have shown that machine-learning-based methods can efficie...

Laplacian Regularized Sparse Representation Based Classifier for Identifying DNA N4-Methylcytosine Sites via L-Matrix Norm.

IEEE/ACM transactions on computational biology and bioinformatics
N4-methylcytosine (4mC) is one of important epigenetic modifications in DNA sequences. Detecting 4mC sites is time-consuming. The computational method based on machine learning has provided effective help for identifying 4mC. To further improve the p...

Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations.

Neural networks : the official journal of the International Neural Network Society
This paper investigates the approximation properties of deep neural networks with piecewise-polynomial activation functions. We derive the required depth, width, and sparsity of a deep neural network to approximate any Hölder smooth function up to a ...