AIMC Topic: Machine Learning

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Classification of FLT3 inhibitors and SAR analysis by machine learning methods.

Molecular diversity
FMS-like tyrosine kinase 3 (FLT3) is a type III receptor tyrosine kinase, which is an important target for anti-cancer therapy. In this work, we conducted a structure-activity relationship (SAR) study on 3867 FLT3 inhibitors we collected. MACCS finge...

Artificial intelligence enabled carbon capture: A review.

The Science of the total environment
Carbon capturing is imperative to fight climate change as much carbon emissions are liberated into the atmosphere, leading to adversely negative environmental impacts. Today's world addresses all the issues with the aid of digital technologies like d...

Framingham risk score conventional risk factors are potent to predict all-cause mortality using machine learning algorithms: a population-based prospective cohort study over 40 years in China.

Journal of investigative medicine : the official publication of the American Federation for Clinical Research
Predicting all-cause mortality using available or conveniently modifiable risk factors is potentially crucial in reducing deaths precisely and efficiently. Framingham risk score (FRS) is widely used in predicting cardiovascular diseases, and its conv...

The implementation of a real time early warning system using machine learning in an Australian hospital to improve patient outcomes.

Resuscitation
BACKGROUND: Early Warning Scores (EWS) monitor inpatient deterioration predominantly using vital signs. We evaluated inpatient outcomes after implementing an Artificial Intelligence (AI) based intervention in our local EWS.

Human Collective Intelligence Inspired Multi-View Representation Learning - Enabling View Communication by Simulating Human Communication Mechanism.

IEEE transactions on pattern analysis and machine intelligence
In real-world applications, we often encounter multi-view learning tasks where we need to learn from multiple sources of data or use multiple sources of data to make decisions. Multi-view representation learning, which can learn a unified representat...

EgoCom: A Multi-Person Multi-Modal Egocentric Communications Dataset.

IEEE transactions on pattern analysis and machine intelligence
Multi-modal datasets in artificial intelligence (AI) often capture a third-person perspective, but our embodied human intelligence evolved with sensory input from the egocentric, first-person perspective. Towards embodied AI, we introduce the Egocent...

Basis for Accurate Protein p Prediction with Machine Learning.

Journal of chemical information and modeling
pH regulates protein structures and the associated functions in many biological processes via protonation and deprotonation of ionizable side chains where the titration equilibria are determined by p's. To accelerate pH-dependent molecular mechanism ...

Ontology-driven and weakly supervised rare disease identification from clinical notes.

BMC medical informatics and decision making
BACKGROUND: Computational text phenotyping is the practice of identifying patients with certain disorders and traits from clinical notes. Rare diseases are challenging to be identified due to few cases available for machine learning and the need for ...

Atomically thin optomemristive feedback neurons.

Nature nanotechnology
Cognitive functions such as learning in mammalian brains have been attributed to the presence of neuronal circuits with feed-forward and feedback topologies. Such networks have interactions within and between neurons that provide excitory and inhibit...