AIMC Topic: Machine Learning

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In silico design of novel aptamers utilizing a hybrid method of machine learning and genetic algorithm.

Molecular diversity
Aptamers can be regarded as efficient substitutes for monoclonal antibodies in many diagnostic and therapeutic applications. Due to the tedious and prohibitive nature of SELEX (systematic evolution of ligands by exponential enrichment), the in silico...

A new recursive least squares-based learning algorithm for spiking neurons.

Neural networks : the official journal of the International Neural Network Society
Spiking neural networks (SNNs) are regarded as effective models for processing spatio-temporal information. However, their inherent complexity of temporal coding makes it an arduous task to put forward an effective supervised learning algorithm, whic...

BloodCaps: A capsule network based model for the multiclassification of human peripheral blood cells.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The classification of human peripheral blood cells yields significance in the detection of inflammation, infections and blood cell disorders such as leukemia. Limitations in traditional algorithms for blood cell classificati...

Advances in de Novo Drug Design: From Conventional to Machine Learning Methods.

International journal of molecular sciences
. De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properti...

Imaging-Based Outcome Prediction of Acute Intracerebral Hemorrhage.

Translational stroke research
We hypothesized that imaging-only-based machine learning algorithms can analyze non-enhanced CT scans of patients with acute intracerebral hemorrhage (ICH). This retrospective multicenter cohort study analyzed 520 non-enhanced CT scans and clinical d...

Artificial intelligence: a critical review of current applications in pancreatic imaging.

Japanese journal of radiology
The applications of artificial intelligence (AI), including machine learning and deep learning, in the field of pancreatic disease imaging are rapidly expanding. AI can be used for the detection of pancreatic ductal adenocarcinoma and other pancreati...

Computational approach for identification, characterization, three-dimensional structure modelling and machine learning-based thermostability prediction of xylanases from the genome of Aspergillus fumigatus.

Computational biology and chemistry
Identification of thermostable and alkaline xylanases from different fungal and bacterial species have gained an interest for the researchers because of its biotechnological relevance in many industries, such as pulp, paper, and bioethanol. In this s...

Differential diagnosis of ameloblastoma and odontogenic keratocyst by machine learning of panoramic radiographs.

International journal of computer assisted radiology and surgery
PURPOSE: The differentiation of the ameloblastoma and odontogenic keratocyst directly affects the formulation of surgical plans, while the results of differential diagnosis by imaging alone are not satisfactory. This paper aimed to propose an algorit...

Toward data-efficient learning: A benchmark for COVID-19 CT lung and infection segmentation.

Medical physics
PURPOSE: Accurate segmentation of lung and infection in COVID-19 computed tomography (CT) scans plays an important role in the quantitative management of patients. Most of the existing studies are based on large and private annotated datasets that ar...

Raman spectroscopy combined with machine learning for rapid detection of food-borne pathogens at the single-cell level.

Talanta
Rapid detection of food-borne pathogens in early food contamination is a permanent topic to ensure food safety and prevent public health problems. Raman spectroscopy, a label-free, highly sensitive and dependable technology has attracted more and mor...