AIMC Topic: Supervised Machine Learning

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Modeling CRISPR gene drives for suppression of invasive rodents using a supervised machine learning framework.

PLoS computational biology
Invasive rodent populations pose a threat to biodiversity across the globe. When confronted with these invaders, native species that evolved independently are often defenseless. CRISPR gene drive systems could provide a solution to this problem by sp...

Fault Diagnosis of Rotating Machinery Based on Improved Self-Supervised Learning Method and Very Few Labeled Samples.

Sensors (Basel, Switzerland)
Convolution neural network (CNN)-based fault diagnosis methods have been widely adopted to obtain representative features and used to classify fault modes due to their prominent feature extraction capability. However, a large number of labeled sample...

When unsupervised training benefits category learning.

Cognition
Humans continuously categorise inputs, but only rarely receive explicit feedback as to whether or not they are correct. This implies that they may be integrating unsupervised information together with their sparse supervised data - a form of semi-sup...

A Semi-Supervised Transfer Learning with Dynamic Associate Domain Adaptation for Human Activity Recognition Using WiFi Signals.

Sensors (Basel, Switzerland)
Human activity recognition without equipment plays a vital role in smart home applications, freeing humans from the shackles of wearable devices. In this paper, by using the channel state information (CSI) of the WiFi signal, semi-supervised transfer...

Comparison of the Meta-Active Machine Learning Model Applied to Biological Data-Driven Experiments with Other Models.

Journal of healthcare engineering
Currently, many methods that could estimate the effects of conditions on a given biological target require either strong modelling assumptions or separate screens. Traditionally, many conditions and targets, without doing all possible experiments, co...

Predicting deleterious missense genetic variants via integrative supervised nonnegative matrix tri-factorization.

Scientific reports
Among an assortment of genetic variations, Missense are major ones which a small subset of them may led to the upset of the protein function and ultimately end in human diseases. Various machine learning methods were declared to differentiate deleter...

Weakly Supervised Sensitive Heatmap framework to classify and localize diabetic retinopathy lesions.

Scientific reports
Vision loss happens due to diabetic retinopathy (DR) in severe stages. Thus, an automatic detection method applied to diagnose DR in an earlier phase may help medical doctors to make better decisions. DR is considered one of the main risks, leading t...

Mitotic Index Determination on Live Cells From Label-Free Acquired Quantitative Phase Images Using a Supervised Autoencoder.

IEEE/ACM transactions on computational biology and bioinformatics
This interdisciplinary work focuses on the interest of a new auto-encoder for supervised classification of live cell populations growing in a thermostated imaging station and acquired by a Quantitative Phase Imaging (QPI) camera. This type of camera ...

Categorical Matrix Completion With Active Learning for High-Throughput Screening.

IEEE/ACM transactions on computational biology and bioinformatics
The recent advances in wet-lab automation enable high-throughput experiments to be conducted seamlessly. In particular, the exhaustive enumeration of all possible conditions is always involved in high-throughput screening. Nonetheless, such a screeni...

Average Top-k Aggregate Loss for Supervised Learning.

IEEE transactions on pattern analysis and machine intelligence
In this work, we introduce the average top- k ( AT) loss, which is the average over the k largest individual losses over a training data, as a new aggregate loss for supervised learning. We show that the AT loss is a natural generalization of the two...