AIMC Topic: Models, Biological

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Evolutionary Innovations and Where to Find Them: Routes to Open-Ended Evolution in Natural and Artificial Systems.

Artificial life Jan 1, 2019
This article presents a high-level conceptual framework to help orient the discussion and implementation of open-endedness in evolutionary systems. Drawing upon earlier work by Banzhaf et al. (2016), three different kinds of open-endedness are identi...
Models, Biological Biology Biological Evolution Systems Theory Artificial Intelligence Computer Simulation
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Recognition of Lung Adenocarcinoma-specific Gene Pairs Based on Genetic Algorithm and Establishment of a Deep Learning Prediction Model.

Combinatorial chemistry & high throughput screening Jan 1, 2019
AIM AND OBJECTIVE: Lung cancer is a disease with a dismal prognosis and is the major cause of cancer deaths in many countries. Nonetheless, rapid technological developments in genome science guarantees more effective prevention and treatment strategi...
Algorithms Gene Expression Profiling Deep Learning Lung Neoplasms Humans Cluster Analysis Models, Biological Adenocarcinoma of Lung
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Constructing a Risk Prediction Model for Lung Cancer Recurrence by Using Gene Function Clustering and Machine Learning.

Combinatorial chemistry & high throughput screening Jan 1, 2019
OBJECTIVE: A significant proportion of patients with early non-small cell lung cancer (NSCLC) can be cured by surgery. The distant metastasis of tumors is the most common cause of treatment failure. Precisely predicting the likelihood that a patient ...
Machine Learning Female Risk Factors Male Humans Carcinoma, Non-Small-Cell Lung Middle Aged Models, Biological Lung Neoplasms Multigene Family Gene Expression Regulation, Neoplastic Recurrence
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Make robot motions natural.

Nature Jan 1, 2019
Locomotion Humans Acclimatization Algorithms Motion Computer Simulation Motion Pictures Movement Biomimetics Animals Caenorhabditis elegans Hypogravity Ants Gestures Posture Robotics Models, Biological
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Morphology-based prediction of cancer cell migration using an artificial neural network and a random decision forest.

Integrative biology : quantitative biosciences from nano to macro Dec 19, 2018
Metastasis is the cause of death in most patients of breast cancer and other solid malignancies. Identification of cancer cells with highly migratory capability to metastasize relies on markers for epithelial-to-mesenchymal transition (EMT), a proces...
Neoplasm Metastasis Lab-On-A-Chip Devices Neoplasms Deep Learning Cell Line, Tumor Single-Cell Analysis Mice Cell Movement Epithelial-Mesenchymal Transition Models, Biological Female Reproducibility of Results Breast Neoplasms Animals Neural Networks, Computer Algorithms Decision Support Techniques Humans
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Expert-level sleep scoring with deep neural networks.

Journal of the American Medical Informatics Association : JAMIA Dec 1, 2018
OBJECTIVES: Scoring laboratory polysomnography (PSG) data remains a manual task of visually annotating 3 primary categories: sleep stages, sleep disordered breathing, and limb movements. Attempts to automate this process have been hampered by the com...
Sleep Apnea Syndromes Models, Biological Sleep Datasets as Topic Classification Humans Polysomnography Neural Networks, Computer Sleep Stages Machine Learning Electroencephalography
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Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype.

Journal of the American Medical Informatics Association : JAMIA Oct 1, 2018
We introduce data assimilation as a computational method that uses machine learning to combine data with human knowledge in the form of mechanistic models in order to forecast future states, to impute missing data from the past by smoothing, and to i...
Machine Learning Phenotype Blood Glucose Insulin Diabetes Mellitus, Type 2 Regression Analysis Bayes Theorem Models, Biological Humans Data Mining Blood Glucose Self-Monitoring Normal Distribution
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[From symptom to syndrome using modern software support].

Der Internist Aug 1, 2018
Diagnosing rare diseases can be challenging for clinicians. This article gives an overview on novel approaches, which enable automated phenotype-driven analyses of differential diagnoses for rare diseases as well as genomic variation data of affected...
Genomics Computational Biology Humans Gene Expression Models, Biological Syndrome Software Biological Ontologies Algorithms Gene Ontology Phenotype Rare Diseases
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Learned protein embeddings for machine learning.

Bioinformatics (Oxford, England) Aug 1, 2018
MOTIVATION: Machine-learning models trained on protein sequences and their measured functions can infer biological properties of unseen sequences without requiring an understanding of the underlying physical or biological mechanisms. Such models enab...
Amino Acid Sequence Eukaryota Sequence Analysis, Protein Computational Biology Proteins Models, Biological Humans Machine Learning Software Bacteria
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Enhancing fluorescent protein photostability through robot-assisted photobleaching.

Integrative biology : quantitative biosciences from nano to macro Jul 16, 2018
Improving fluorescent proteins through the use of directed evolution requires robust techniques for screening large libraries of genetic variants. Here we describe an effective and relatively low-cost system for screening libraries of fluorescent pro...
HeLa Cells Bacterial Proteins Luminescent Proteins Plasmids Humans Crystallography, X-Ray Photobleaching Mutation Photochemistry Escherichia coli Models, Biological Robotics Light
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