AIMC Topic: Machine Learning

Clear Filters Showing 8961 to 8970 of 34417 articles

Effects of machine learning errors on human decision-making: manipulations of model accuracy, error types, and error importance.

Cognitive research: principles and implications
This study addressed the cognitive impacts of providing correct and incorrect machine learning (ML) outputs in support of an object detection task. The study consisted of five experiments that manipulated the accuracy and importance of mock ML output...

What could be the role of genetic tests and machine learning of AXIN2 variant dominance in non-syndromic hypodontia? A case-control study in orthodontically treated patients.

Progress in orthodontics
BACKGROUND: Hypodontia is the most prevalent dental anomaly in humans, and is primarily attributed to genetic factors. Although genome-wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNP) associated with hypodontia, g...

MAGICAL: A multi-class classifier to predict synthetic lethal and viable interactions using protein-protein interaction network.

PLoS computational biology
Synthetic lethality (SL) and synthetic viability (SV) are commonly studied genetic interactions in the targeted therapy approach in cancer. In SL, inhibiting either of the genes does not affect the cancer cell survival, but inhibiting both leads to a...

Benchmarking the negatives: Effect of negative data generation on the classification of miRNA-mRNA interactions.

PLoS computational biology
MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally. In animals, this regulation is achieved via base-pairing with partially complementary sequences on mainly 3' UTR region of messenger RNAs (mRNAs). Comp...

Molecular connectomics: Placing cells into morphological tissue context.

PLoS biology
Here we propose "molecular connectomics" to link molecular and morphological cell features in three dimensions across scales, using machine learning and artificial intelligence to reveal emergent properties of complex biological systems.

A machine learning enhanced EMS mutagenesis probability map for efficient identification of causal mutations in Caenorhabditis elegans.

PLoS genetics
Chemical mutagenesis-driven forward genetic screens are pivotal in unveiling gene functions, yet identifying causal mutations behind phenotypes remains laborious, hindering their high-throughput application. Here, we reveal a non-uniform mutation rat...

Yield prediction for crops by gradient-based algorithms.

PloS one
A timely and consistent assessment of crop yield will assist the farmers in improving their income, minimizing losses, and deriving strategic plans in agricultural commodities to adopt import-export policies. Crop yield predictions are one of the var...

Machine learning assessment of vildagliptin and linagliptin effectiveness in type 2 diabetes: Predictors of glycemic control.

PloS one
OBJECTIVE: Differential effects of linagliptin and vildagliptin may help us personalize treatment for Type 2 Diabetes Mellitus (T2DM). The current study compares the effect of these drugs on glycated hemoglobin (HbA1c) in an artificial neural network...

Machine learning-driven assessment of biochemical qualities in tomato and mandarin using RGB and hyperspectral sensors as nondestructive technologies.

PloS one
Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non-destructive techniques such as image processing and spectral refl...

Prediction of future customer needs using machine learning across multiple product categories.

PloS one
In recent years, computational approaches for extracting customer needs from user generated content have been proposed. However, there is a lack of studies that focus on extracting unmet needs for future popular products. Therefore, this study presen...