AIMC Topic: Computational Biology

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Uncertainty-Gated Stochastic Sequential Model for EHR Mortality Prediction.

IEEE transactions on neural networks and learning systems
Electronic health records (EHRs) are characterized as nonstationary, heterogeneous, noisy, and sparse data; therefore, it is challenging to learn the regularities or patterns inherent within them. In particular, sparseness caused mostly by many missi...

GenTB: A user-friendly genome-based predictor for tuberculosis resistance powered by machine learning.

Genome medicine
BACKGROUND: Multidrug-resistant Mycobacterium tuberculosis (Mtb) is a significant global public health threat. Genotypic resistance prediction from Mtb DNA sequences offers an alternative to laboratory-based drug-susceptibility testing. User-friendly...

Interpretation of Electrocardiogram Heartbeat by CNN and GRU.

Computational and mathematical methods in medicine
The diagnosis of electrocardiogram (ECG) is extremely onerous and inefficient, so it is necessary to use a computer-aided diagnosis of ECG signals. However, it is still a challenging problem to design high-accuracy ECG algorithms suitable for the med...

Uncovering cancer vulnerabilities by machine learning prediction of synthetic lethality.

Molecular cancer
BACKGROUND: Synthetic lethality describes a genetic interaction between two perturbations, leading to cell death, whereas neither event alone has a significant effect on cell viability. This concept can be exploited to specifically target tumor cells...

Gene Ontology representation for transcription factor functions.

Biochimica et biophysica acta. Gene regulatory mechanisms
Transcription plays a central role in defining the identity and functionalities of cells, as well as in their responses to changes in the cellular environment. The Gene Ontology (GO) provides a rigorously defined set of concepts that describe the fun...

In silico screening of ssDNA aptamer against Escherichia coli O157:H7: A machine learning and the Pseudo K-tuple nucleotide composition based approach.

Computational biology and chemistry
This study was planned to in silico screening of ssDNA aptamer against Escherichia coli O157:H7 by combination of machine learning and the PseKNC approach. For this, firstly a total numbers of 47 validated ssDNA aptamers as well as 498 random DNA seq...

KEGG mapping tools for uncovering hidden features in biological data.

Protein science : a publication of the Protein Society
In contrast to artificial intelligence and machine learning approaches, KEGG (https://www.kegg.jp) has relied on human intelligence to develop "models" of biological systems, especially in the form of KEGG pathway maps that are manually created by ca...

Using machine learning and big data to explore the drug resistance landscape in HIV.

PLoS computational biology
Drug resistance mutations (DRMs) appear in HIV under treatment pressure. DRMs are commonly transmitted to naive patients. The standard approach to reveal new DRMs is to test for significant frequency differences of mutations between treated and naive...

Review of machine learning methods for RNA secondary structure prediction.

PLoS computational biology
Secondary structure plays an important role in determining the function of noncoding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary struc...

Rosetta:MSF:NN: Boosting performance of multi-state computational protein design with a neural network.

PloS one
Rational protein design aims at the targeted modification of existing proteins. To reach this goal, software suites like Rosetta propose sequences to introduce the desired properties. Challenging design problems necessitate the representation of a pr...