AIMC Topic: Algorithms

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MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm.

Briefings in bioinformatics
Deep learning is an important branch of artificial intelligence that has been successfully applied into medicine and two-dimensional ligand design. The three-dimensional (3D) ligand generation in the 3D pocket of protein target is an interesting and ...

Integrative biomarker detection on high-dimensional gene expression data sets: a survey on prior knowledge approaches.

Briefings in bioinformatics
Gene expression data provide the expression levels of tens of thousands of genes from several hundred samples. These data are analyzed to detect biomarkers that can be of prognostic or diagnostic use. Traditionally, biomarker detection for gene expre...

Different molecular enumeration influences in deep learning: an example using aqueous solubility.

Briefings in bioinformatics
Aqueous solubility is the key property driving many chemical and biological phenomena and impacts experimental and computational attempts to assess those phenomena. Accurate prediction of solubility is essential and challenging, even with modern comp...

Systematic evaluation of machine learning methods for identifying human-pathogen protein-protein interactions.

Briefings in bioinformatics
In recent years, high-throughput experimental techniques have significantly enhanced the accuracy and coverage of protein-protein interaction identification, including human-pathogen protein-protein interactions (HP-PPIs). Despite this progress, expe...

iPiDi-PUL: identifying Piwi-interacting RNA-disease associations based on positive unlabeled learning.

Briefings in bioinformatics
Accumulated researches have revealed that Piwi-interacting RNAs (piRNAs) are regulating the development of germ and stem cells, and they are closely associated with the progression of many diseases. As the number of the detected piRNAs is increasing ...

Manipulation for self-Identification, and self-Identification for better manipulation.

Science robotics
The process of modeling a series of hand-object parameters is crucial for precise and controllable robotic in-hand manipulation because it enables the mapping from the hand's actuation input to the object's motion to be obtained. Without assuming tha...

Closed-loop feedback control of microfluidic cell manipulation deep-learning integrated sensor networks.

Lab on a chip
Microfluidic technologies have long enabled the manipulation of flow-driven cells en masse under a variety of force fields with the goal of characterizing them or discriminating the pathogenic ones. On the other hand, a microfluidic platform is typic...

Knockoff boosted tree for model-free variable selection.

Bioinformatics (Oxford, England)
MOTIVATION: The recently proposed knockoff filter is a general framework for controlling the false discovery rate (FDR) when performing variable selection. This powerful new approach generates a 'knockoff' of each variable tested for exact FDR contro...

The potential for reduced radiation dose from deep learning-based CT image reconstruction: A comparison with filtered back projection and hybrid iterative reconstruction using a phantom.

Medicine
The purpose of this phantom study is to compare radiation dose and image quality of abdominal computed tomography (CT) scanned with different tube voltages and tube currents, reconstructed with filtered back projection (FBP), hybrid iterative reconst...

Predicting in-hospital mortality in ICU patients with sepsis using gradient boosting decision tree.

Medicine
Sepsis is a leading cause of mortality in the intensive care unit. Early prediction of sepsis can reduce the overall mortality rate and cost of sepsis treatment. Some studies have predicted mortality and development of sepsis using machine learning m...