AIMC Topic: Classification

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16S classifier: a tool for fast and accurate taxonomic classification of 16S rRNA hypervariable regions in metagenomic datasets.

PloS one
The diversity of microbial species in a metagenomic study is commonly assessed using 16S rRNA gene sequencing. With the rapid developments in genome sequencing technologies, the focus has shifted towards the sequencing of hypervariable regions of 16S...

Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning.

Neural networks : the official journal of the International Neural Network Society
Constructing an informative and discriminative graph plays an important role in various pattern recognition tasks such as clustering and classification. Among the existing graph-based learning models, low-rank representation (LRR) is a very competiti...

ADReCS: an ontology database for aiding standardization and hierarchical classification of adverse drug reaction terms.

Nucleic acids research
Adverse drug reactions (ADRs) are noxious and unexpected effects during normal drug therapy. They have caused significant clinical burden and been responsible for a large portion of new drug development failure. Molecular understanding and in silico ...

Expected energy-based restricted Boltzmann machine for classification.

Neural networks : the official journal of the International Neural Network Society
In classification tasks, restricted Boltzmann machines (RBMs) have predominantly been used in the first stage, either as feature extractors or to provide initialization of neural networks. In this study, we propose a discriminative learning approach ...

On Equivalence of FIS and ELM for Interpretable Rule-Based Knowledge Representation.

IEEE transactions on neural networks and learning systems
This paper presents a fuzzy extreme learning machine (F-ELM) that embeds fuzzy membership functions and rules into the hidden layer of extreme learning machine (ELM). Similar to the concept of ELM that employed the random initialization technique, th...

phyddle: Software for Exploring Phylogenetic Models with Deep Learning.

Systematic biology
Phylogenies contain a wealth of information about the evolutionary history and process that gave rise to the diversity of life. This information can be extracted by fitting phylogenetic models to trees. However, many realistic phylogenetic models lac...

Integrating Deep Learning Derived Morphological Traits and Molecular Data for Total-Evidence Phylogenetics: Lessons from Digitized Collections.

Systematic biology
Deep learning has previously shown success in automatically generating morphological traits that carry a phylogenetic signal. In this paper, we explore combining molecular data with deep learning derived morphological traits from images of pinned ins...

Knowledge-guided generative artificial intelligence for automated taxonomy learning from drug labels.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: To automatically construct a drug indication taxonomy from drug labels using generative Artificial Intelligence (AI) represented by the Large Language Model (LLM) GPT-4 and real-world evidence (RWE).

A taxonomy for advancing systematic error analysis in multi-site electronic health record-based clinical concept extraction.

Journal of the American Medical Informatics Association : JAMIA
BACKGROUND: Error analysis plays a crucial role in clinical concept extraction, a fundamental subtask within clinical natural language processing (NLP). The process typically involves a manual review of error types, such as contextual and linguistic ...

Image-based recognition of parasitoid wasps using advanced neural networks.

Invertebrate systematics
Hymenoptera has some of the highest diversity and number of individuals among insects. Many of these species potentially play key roles as food sources, pest controllers and pollinators. However, little is known about the diversity and biology and ~8...