Tag: Machine Learning

miRbiom – A Machine Learning Approach to Profile miRNAs

miRbiom

:: DESCRIPTION

miRbiom: Machine-Learning on Bayesian Causal Nets of RBP-miRNA interactions successfully predicts miRNA profiles

::DEVELOPER

SCBB-LAB (Studio of Computational Biology and Bioinformatics)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

miRbiom

:: MORE INFORMATION

Citation

Pradhan UK, Sharma NK, Kumar P, Kumar A, Gupta S, Shankar R.
miRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles.
PLoS One. 2021 Oct 12;16(10):e0258550. doi: 10.1371/journal.pone.0258550. PMID: 34637468; PMCID: PMC8509996.

epitope3D – Machine Learning method for conformational B-cell Epitope prediction

epitope3D

:: DESCRIPTION

epitope3D is a novel scalable machine learning method capable of accurately identifying conformational epitopes trained and evaluated on the largest curated epitope data set to date.

::DEVELOPER

Biosig Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

da Silva BM, Myung Y, Ascher DB, Pires DEV.
epitope3D: a machine learning method for conformational B-cell epitope prediction.
Brief Bioinform. 2021 Oct 21:bbab423. doi: 10.1093/bib/bbab423. Epub ahead of print. PMID: 34676398.

flowLearn – Machine-learning algorithm for Gating Flow Cytometry data

flowLearn

:: DESCRIPTION

flowLearn is a semi-supervised approach for the quality-checked identification of cell populations.

::DEVELOPER

Computational Methods for Paleogenomics and Comparative Genomics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

flowLearn

:: MORE INFORMATION

Citation

Lux M, Brinkman RR, Chauve C, Laing A, Lorenc A, Abeler-Dörner L, Hammer B.
flowLearn: fast and precise identification and quality checking of cell populations in flow cytometry.
Bioinformatics. 2018 Jul 1;34(13):2245-2253. doi: 10.1093/bioinformatics/bty082. PMID: 29462241; PMCID: PMC6022609.

MitoScape v1.0 – Machine-learning workflow for Aligning mtDNA from NGS data

MitoScape v1.0

:: DESCRIPTION

MitoScape is a novel, big-data, software for extracting mitochondrial DNA sequences from NGS.

::DEVELOPER

Larry N. Singh

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

MitoScape

:: MORE INFORMATION

Citation

Singh LN, Ennis B, Loneragan B, Tsao NL, Lopez Sanchez MIG, Li J, Acheampong P, Tran O, Trounce IA, Zhu Y, Potluri P; Regeneron Genetics Center, Emanuel BS, Rader DJ, Arany Z, Damrauer SM, Resnick AC, Anderson SA, Wallace DC.
MitoScape: A big-data, machine-learning platform for obtaining mitochondrial DNA from next-generation sequencing data.
PLoS Comput Biol. 2021 Nov 11;17(11):e1009594. doi: 10.1371/journal.pcbi.1009594. Epub ahead of print. PMID: 34762648.

preciseTAD 1.4.0 – Machine Learning framework for precise TAD Boundary Prediction

preciseTAD 1.4.0

:: DESCRIPTION

preciseTAD provides functions to predict the location of boundaries of topologically associated domains (TADs) and chromatin loops at base-level resolution.

::DEVELOPER

Mikhail Dozmorov <mikhail.dozmorov at gmail.com>

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux
  • R
  • BioConductor

:: DOWNLOAD

preciseTAD

:: MORE INFORMATION

Citation:

Stilianoudakis SC, Marshall MA, Dozmorov MG.
preciseTAD: A transfer learning framework for 3D domain boundary prediction at base-pair resolution.
Bioinformatics. 2021 Nov 6:btab743. doi: 10.1093/bioinformatics/btab743. Epub ahead of print. PMID: 34741515.

miRLocator – Machine Learning-based mature miRNAs within pre-miRNA Sequences

miRLocator

:: DESCRIPTION

miRLocator applies machine learning algorithms to accurately predict the localization of most likely miRNAs within their pre-miRNAs.

::DEVELOPER

Ma Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • Python

:: DOWNLOAD

miRLocator

:: MORE INFORMATION

Citation

Zhang T, Ju L, Zhai J, Song Y, Song J, Ma C.
miRLocator: A Python Implementation and Web Server for Predicting miRNAs from Pre-miRNA Sequences.
Methods Mol Biol. 2019;1932:89-97. doi: 10.1007/978-1-4939-9042-9_6. PMID: 30701493.

Cui H, Zhai J, Ma C.
miRLocator: Machine Learning-Based Prediction of Mature MicroRNAs within Plant Pre-miRNA Sequences.
PLoS One. 2015 Nov 11;10(11):e0142753. doi: 10.1371/journal.pone.0142753. PMID: 26558614; PMCID: PMC4641693.

ACES – Machine Learning Toolbox for Clustering analysis and Visualization

ACES

:: DESCRIPTION

ACES is a machine learning toolbox for clustering analysis and visualization of both biological data and other types data. Given the biological data or their distance/probability matrix, ACES can automatically extract the features of each identity and cluster them by various widely used clustering algorithms.

::DEVELOPER

Grabherr Group

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux / Windows
  • Java

:: DOWNLOAD

ACES

:: MORE INFORMATION

Citation

Gao J, Sundström G, Moghadam BT, Zamani N, Grabherr MG.
ACES: a machine learning toolbox for clustering analysis and visualization.
BMC Genomics. 2018 Dec 27;19(1):964. doi: 10.1186/s12864-018-5300-y. PMID: 30587115; PMCID: PMC6307290.

PrePPItar 0.0.1 – Machine Learning Framework to Predict PPI Target for Drug

PrePPItar 0.0.1

:: DESCRIPTION

PrePPItar is a computational method to Predict PPIs as drug targets by uncovering the potential associations between drugs and PPIs.

::DEVELOPER

Optimization and Computational Systems Biology Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • MatLab

:: DOWNLOAD

 PrePPItar

:: MORE INFORMATION

Citation:

Computational probing protein-protein interactions targeting small molecules.
Wang YC, Chen SL, Deng NY, Wang Y.
Bioinformatics. 2015 Sep 28. pii: btv528.

MLbias 1.0 – Correct Machine Learning Bias

MLbias 1.0

:: DESCRIPTION

MLbias is an R package to correct for machine learning bias when many classifiers are compared and the best is selected

::DEVELOPER

George C. Tseng 

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 MLbias

:: MORE INFORMATION

Citation:

Bias correction for selecting the minimal-error classifier from many machine learning models.
Ding Y, Tang S, Liao SG, Jia J, Oesterreich S, Lin Y, Tseng GC.
Bioinformatics. 2014 Aug 1. pii: btu520.

GANN 2.0 – Machine Learning tool for the Detection of Conserved Features in DNA

GANN 2.0

:: DESCRIPTION

GANN (Genetic Algorithm Neural Networks) is a machine learning method designed with the complexities of transcriptional regulation in mind.The key principle is that regulatory regions are composed of features such as consensus strings, characterized binding sites, and DNA structural properties. GANN identifies these features in a set of sequences, and then identifies combinations of features that can differentiate between the positive set (sequences with known or putative regulatory function) and the negative set (sequences with no regulatory function). Once these features have been identified, they can be used to classify new sequences of unknown function.

::DEVELOPER

Beiko lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows
  • Perl

:: DOWNLOAD

 GANN

:: MORE INFORMATION

Citation

Beiko, R.G. and Charlebois, R.L. (2005).
GANN: genetic algorithm neural networks for the detection of conserved combinations of features in DNA.
BMC Bioinformatics 6: 36.