Providing data-driven solutions for labs and businesses
Expertise
What We Do
Skills and Practice Areas
Programming
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R
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Python
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C++
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Java
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Matlab
Other Tech
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Bioconductor
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Tensorflow
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Theano
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Keras
Bioinformatics Related
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ChIP-seq
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RNA-seq
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ssRNA-seq
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Whole Genome-seq
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CyTOF
With years of data analytics experience in Yale University laboratories, SeqHub understands your research and publication standards. We guarantee clear, publication-worthy analysis delivered in a timely fashion. Our philosophy is to work with you to understand the purpose and field-specific terminology of your research to deliver the most personalized service available.
Math and Statistics
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Deep learning
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Signal processsing
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Sprectral methods
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Statistical modeling
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Machine learning
Research Samples
Our team of experts are active academic researchers and contributors to the data science field; the following is a sampling of our publications:
“Ritornello: High fidelity control-free chip-seq peak calling”
http://biorxiv.org/content/early/2015/12/11/034090
“Removal of Batch Effects using Distribution-Matching residual Networks”
https://arxiv.org/abs/1610.04181
“Arpeggio: Harmonic compression of ChIP-seq data reveals protein-chromatin interaction signatures”
http://nar.oxfordjournals.org/content/41/16/e161.long
“FastPCA” - Random matrix projection approach for very fast principle components analysis (SVDS in matlab) – publication pending
http://dl.acm.org/citation.cfm?id=3004053
“Gating Mass Cytometry by deep learning”
http://biorxiv.org/content/early/2016/05/20/054411
In Situ Quantitative Measurement of HER2mRNA Predicts Benefit from Trastuzumab-Containing Chemotherapy in a Cohort of Metastatic Breast Cancer Patients
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0099131
Germline competency of parthenogenetic embryonic stem cells from immature oocytes of adult mouse ovary.