🧬 It’s not all about Humans 🥔

Newsletter #2 — LifeSciences & Machine Learning news


Discover how you can predict the best quality potato for your next plate of fries! 🍟

Webinar ran successfully on June 24th. See the video recording below, and review the outcome models on JADBio. Watch the Webinar recording below:

We show how a team of researchers applied JADBio’s Automated Machine Learning (AutoML) platform to predict potatoes’ susceptibility to bruising and also its potential for coloration during chip/crisp processing. The aim was to differentiate between potatoes that would be less prone to bruising from those that would more easily bruise during mechanical handling. Another goal was to successfully predict the potatoes’ potential susceptibility to acrylamide formation during chip/crisp processing due to the Maillard reaction.



Multi-omics characterization of
left-right colorectal cancer

Researchers used JADBio to classify left and right Colorectal Cancer, based on the fold change in values between tumor and adjacent normal, with promising results. The researchers, John Marshall, Takayuki Yoshino et al., published their paper, on Multi-omics Characterization of Left-Right Colorectal Cancer, in the Journal of Clinical Oncology and presented their work at ASCO 2021 annual Meeting.

Future Data Analyst Will Be Customising AutoML Tools

Datatechvibe writes about how AutoML needs educated users. In an exclusive interview with Dr. Ioannis Tsamardinos, CEO and Co-founder at JADBio, they discuss how AutoML is changing the face of ML-based solutions. How AutoML can provide significant value to life sciences and why Healthcare companies often struggle with unlocking value from their own data.
Read the interview>


What is Survival Analysis?

The field of statistical analysis that applies specific methodologies to explore the time it takes for an event to happen is the bare-bones definition of Survival Analysis. The amount of time it takes before a predetermined event takes place is also known as time-to-event analysis. By “survival” in this context, we refer to what remains free of a particular outcome over time.

JADBio performs feature selection with hundreds of thousands of markers and image features, which filters out not only the irrelevant ones but also the redundant markers. Read the specific example on the problem of predicting the survival time after surgery of low-grade glioma patients from miRNA profiles measured in tumor biopsies. Read the full article >


Predicting PD-1/PD-L1 inhibitors treatment on metastatic non-small cell lung cancer

The researchers used JADBio for binary classification modelling for the prediction of the probability of a single individual to achieve DS (PR or SD vs. PD) with ICIs as second-line treatment. The feature classification of the parameters used as input in JADBio is demonstrated in S1 Table. The tool applied the following modelling algorithms: support vector machines (SVM) with full polynomial and Gaussian kernels [25], random forests [26], ridge logistic regression [27], and decision trees [28]. The performance metric we chose over the several ones available at JADBio, is the AUC. In most cases, the result of an analysis will be a complex model, incomprehensible to the human user. To aid in that regard, JADBio additionally outputs the best interpretable model. In their work, they report the performance estimation of the best-performing model. Read the Case Study>

Head over to all our Case Studies 🤓>


JADBio enhances security by adding 2-factor authentication. Now, our users can increase the protection of their accounts by enabling two-factor authentication using the free Google authenticator app.



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JADBio allows biologists, bioinformaticians, clinicians & non-expert analysts to perform sophisticated data analyses with the click of a button jadbio.com