AIMC Topic: Algorithms

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An ensemble approach to protein fold classification by integration of template-based assignment and support vector machine classifier.

Bioinformatics (Oxford, England)
MOTIVATION: Protein fold classification is a critical step in protein structure prediction. There are two possible ways to classify protein folds. One is through template-based fold assignment and the other is ab-initio prediction using machine learn...

CellSort: a support vector machine tool for optimizing fluorescence-activated cell sorting and reducing experimental effort.

Bioinformatics (Oxford, England)
MOTIVATION: High throughput screening by fluorescence activated cell sorting (FACS) is a common task in protein engineering and directed evolution. It can also be a rate-limiting step if high false positive or negative rates necessitate multiple roun...

Hum-mPLoc 3.0: prediction enhancement of human protein subcellular localization through modeling the hidden correlations of gene ontology and functional domain features.

Bioinformatics (Oxford, England)
MOTIVATION: Protein subcellular localization prediction has been an important research topic in computational biology over the last decade. Various automatic methods have been proposed to predict locations for large scale protein datasets, where stat...

The Diagnostic Imagination in Radiology: Part 2.

Radiology management
Developing algorithms for the improve- ment of diagnostic care leverages tech- nologies and techniques developed across industries that are exponentially being improved, developed, and tested. Machine learning means extracting patterns not only from ...

Advances in Spectroscopic Characters of Space Objects.

Guang pu xue yu guang pu fen xi = Guang pu
Spectroscopic characters of space objects are basic optical attributes which could stand for the material types of space objects. By comparing actual spectral characteristics with that obtained in the lab, the types of space materials can be identifi...

Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning.

Journal of biomedical optics
We present an approach for automatic diagnosis of tissue biopsies. Our methodology consists of a quantitative phase imaging tissue scanner and machine learning algorithms to process these data. We illustrate the performance by automatic Gleason gradi...

Artificial intelligence based model for optimization of COD removal efficiency of an up-flow anaerobic sludge blanket reactor in the saline wastewater treatment.

Water science and technology : a journal of the International Association on Water Pollution Research
The complex non-linear behavior presented in the biological treatment of wastewater requires an accurate model to predict the system performance. This study evaluates the effectiveness of an artificial intelligence (AI) model, based on the combinatio...

Improving protein disorder prediction by deep bidirectional long short-term memory recurrent neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Capturing long-range interactions between structural but not sequence neighbors of proteins is a long-standing challenging problem in bioinformatics. Recently, long short-term memory (LSTM) networks have significantly improved the accurac...

The promise of cyborg intelligence.

Learning & behavior
Yu et al. (2016) demonstrated that algorithms designed to find efficient routes in standard mazes can be integrated with the natural processes controlling rat navigation and spatial choices, and they pointed out the promise of such "cyborg intelligen...

Application of supervised machine learning algorithms for the classification of regulatory RNA riboswitches.

Briefings in functional genomics
Riboswitches, the small structured RNA elements, were discovered about a decade ago. It has been the subject of intense interest to identify riboswitches, understand their mechanisms of action and use them in genetic engineering. The accumulation of ...