AIMC Topic: Machine Learning

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Artificial Intelligence Applied to Battery Research: Hype or Reality?

Chemical reviews
This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily understandable, review of general interest to the battery...

MIDGET:Detecting differential gene expression on microarray data.

Computer methods and programs in biomedicine
Backgound and Objective: Detecting differentially expressed genes is an important step in genome wide analysis and expression profiling. There are a wide array of algorithms used in today's research based on statistical approaches. Even though the cu...

TRIOMPHE: Transcriptome-Based Inference and Generation of Molecules with Desired Phenotypes by Machine Learning.

Journal of chemical information and modeling
One of the most challenging tasks in the drug-discovery process is the efficient identification of small molecules with desired phenotypes. In this study, we propose a novel computational method for omics-based drug design, which we call TRIOMPHE (t...

A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis.

Sensors (Basel, Switzerland)
Regular inspection of railway track health is crucial for maintaining safe and reliable train operations. Factors, such as cracks, ballast issues, rail discontinuity, loose nuts and bolts, burnt wheels, superelevation, and misalignment developed on t...

Machine learning approach for automatic recognition of tomato-pollinating bees based on their buzzing-sounds.

PLoS computational biology
Bee-mediated pollination greatly increases the size and weight of tomato fruits. Therefore, distinguishing between the local set of bees-those that are efficient pollinators-is essential to improve the economic returns for farmers. To achieve this, i...

Machine Learning Applications in Solid Organ Transplantation and Related Complications.

Frontiers in immunology
The complexity of transplant medicine pushes the boundaries of innate, human reasoning. From networks of immune modulators to dynamic pharmacokinetics to variable postoperative graft survival to equitable allocation of scarce organs, machine learning...

ESIDE: A computationally intelligent method to identify earthworm species (E. fetida) from digital images: Application in taxonomy.

PloS one
Earthworms (Crassiclitellata) being ecosystem engineers significantly affect the physical, chemical, and biological properties of the soil by recycling organic material, increasing nutrient availability, and improving soil structure. The efficiency o...

Polycystic ovary syndrome: clinical and laboratory variables related to new phenotypes using machine-learning models.

Journal of endocrinological investigation
PURPOSE: Polycystic Ovary Syndrome (PCOS) is the most frequent endocrinopathy in women of reproductive age. Machine learning (ML) is the area of artificial intelligence with a focus on predictive computing algorithms. We aimed to define the most rele...

Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network.

American journal of human genetics
Rare diseases affect millions of people worldwide, and discovering their genetic causes is challenging. More than half of the individuals analyzed by the Undiagnosed Diseases Network (UDN) remain undiagnosed. The central hypothesis of this work is th...

Application of Artificial Intelligence in Acute Coronary Syndrome: A Brief Literature Review.

Advances in therapy
Artificial intelligence (AI) is defined as a set of algorithms and intelligence to try to imitate human intelligence. Machine learning is one of them, and deep learning is one of those machine learning techniques. The application of AI in healthcare ...