AIMC Topic: Machine Learning

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Deep-Net: A Lightweight CNN-Based Speech Emotion Recognition System Using Deep Frequency Features.

Sensors (Basel, Switzerland)
Artificial intelligence (AI) and machine learning (ML) are employed to make systems smarter. Today, the speech emotion recognition (SER) system evaluates the emotional state of the speaker by investigating his/her speech signal. Emotion recognition i...

Intelligent Imaging in Nuclear Medicine: the Principles of Artificial Intelligence, Machine Learning and Deep Learning.

Seminars in nuclear medicine
The emergence of artificial intelligence (AI) in nuclear medicine has occurred over the last 50 years but more recent developments in machine learning (ML) and deep learning (DL) have driven new capabilities of AI in nuclear medicine. In nuclear medi...

iDRBP_MMC: Identifying DNA-Binding Proteins and RNA-Binding Proteins Based on Multi-Label Learning Model and Motif-Based Convolutional Neural Network.

Journal of molecular biology
DNA-binding protein (DBP) and RNA-binding protein (RBP) are playing crucial roles in gene expression. Accurate identification of them is of great significance, and accurately computational predictors are highly required. In previous studies, DBP reco...

Machine learning method using position-specific mutation based classification outperforms one hot coding for disease severity prediction in haemophilia 'A'.

Genomics
Haemophilia is an X-linked genetic disorder in which A and B types are the most common that occur due to absence or lack of protein factors VIII and IX, respectively. Severity of the disease depends on mutation. Available Machine Learning (ML) method...

A Deep-Learning View of Chemical Space Designed to Facilitate Drug Discovery.

Journal of chemical information and modeling
Drug discovery projects entail cycles of design, synthesis, and testing that yield a series of chemically related small molecules whose properties, such as binding affinity to a given target protein, are progressively tailored to a particular drug di...

Improved Activity Recognition Combining Inertial Motion Sensors and Electroencephalogram Signals.

International journal of neural systems
Human activity recognition and neural activity analysis are the basis for human computational neureoethology research dealing with the simultaneous analysis of behavioral ethogram descriptions and neural activity measurements. Wireless electroencepha...

Predicting the frequencies of drug side effects.

Nature communications
A central issue in drug risk-benefit assessment is identifying frequencies of side effects in humans. Currently, frequencies are experimentally determined in randomised controlled clinical trials. We present a machine learning framework for computati...

Classification of Parkinson's disease and essential tremor based on balance and gait characteristics from wearable motion sensors via machine learning techniques: a data-driven approach.

Journal of neuroengineering and rehabilitation
BACKGROUND: Parkinson's disease (PD) and essential tremor (ET) are movement disorders that can have similar clinical characteristics including tremor and gait difficulty. These disorders can be misdiagnosed leading to delay in appropriate treatment. ...

Accurate prediction of DNA N-methylcytosine sites via boost-learning various types of sequence features.

BMC genomics
BACKGROUND: DNA N4-methylcytosine (4mC) is a critical epigenetic modification and has various roles in the restriction-modification system. Due to the high cost of experimental laboratory detection, computational methods using sequence characteristic...