Cellular calcium fluorescence imaging utilized to study cellular behaviors typically results in large datasets and a profound need for standardized and accurate analysis methods. Here, we describe open-source software (4SM) to overcome these limitati...
Raman microspectroscopy is a powerful tool for obtaining biomolecular information from single microbial cells in a nondestructive manner. Here, we detail steps to discriminate prokaryotic species using single-cell Raman spectra acquisitions followed ...
Functional doppelgängers (FDs) are independently derived sample pairs that confound machine learning model (ML) performance when assorted across training and validation sets. Here, we detail the use of doppelgangerIdentifier (DI), providing software ...
We describe a protocol to perform fast and non-arbitrary quality control of single-cell RNA sequencing (scRNA-seq) raw data using scKB and COPILOT. scKB is a wrapper script of kallisto and bustools for accelerated alignment and transcript count matri...
Computational protocols for cell type deconvolution from bulk RNA-seq data have been used to understand cellular heterogeneity in disease-related samples, but their performance can be impacted by batch effect among datasets. Here, we present a DAISM-...
Identifying and designing high-performance multi-element ceramics based on trial-and-error approaches are ineffective and expensive. Here, we present a machine-learning-accelerated method for prediction of mechanical properties of multi-element ceram...
We present a protocol which implements deep learning-based identification of the lung adenocarcinoma category with high accuracy and generalizability, and labeling of the high-risk area on Computed Tomography (CT) images. The protocol details the exe...
Quantitative studies of cellular morphodynamics rely on accurate cell segmentation in live cell images. However, fluorescence and phase contrast imaging hinder accurate edge localization. To address this challenge, we developed MARS-Net, a deep learn...
Cancer survival prediction is typically done with uninterpretable machine learning techniques, e.g., gradient tree boosting. Therefore, additional steps are needed to infer biological plausibility of the predictions. Here, we describe a protocol that...
Even though there were many tool developments of fusion gene prediction from NGS data, too many false positives are still an issue. Wise use of the genomic features around the fusion gene breakpoints will be helpful to identify reliable fusion genes ...