Ali Rahnavard

seqLens: optimizing language models for genomic predictions

This study develops seqLens, a genomic language model family, and evaluates its performance on phenotypic predictions using diverse pretraining datasets and tokenization strategies.

Meta-analytic microbiome target discovery for immune checkpoint inhibitor response in advanced melanoma

This study identifies microbial species and metabolic pathways associated with immune checkpoint inhibitor response in melanoma through a meta-analysis of seven microbiome cohorts.

resLens: genomic language models to enhance antibiotic resistance gene detection

resLens improves antibiotic resistance gene detection by using genomic language models trained on curated datasets, outperforming reference-based methods in varied scenarios.

waveome: a toolkit for longitudinal omics analysis using Gaussian processes

The paper introduces waveome, a Python toolkit using Gaussian processes for longitudinal omics analysis, identifying biomarkers in Crohn's disease with sparse, irregular data.

Guided tokenization and domain knowledge enhance genomic language models' performance

This paper introduces guided tokenization and domain adaptation to improve genomic language models' performance in classification and annotation tasks.

Curating 16S rRNA databases enhances taxonomic accuracy and computational efficiency in microbial profiling

The paper curates 16S rRNA databases to improve taxonomic accuracy and computational efficiency in microbial profiling by cleaning sequences and reducing redundancy.

cellSight: Characterizing dynamics of cells using single-cell RNA-sequencing

cellSight automates single-cell RNA-sequencing analysis to improve efficiency, reduce errors, and enhance reproducibility in cellular studies.

Deep skin fibroblast-mediated macrophage recruitment supports acute wound healing

The paper identifies deep skin fibroblasts as a source of pro-inflammatory factors that drive macrophage recruitment and support wound healing in mice.

Discovering genotype-phenotype relationships with machine learning and the Visual Physiology Opsin Database (VPOD)

The paper introduces a database of opsin genotypes and λmax phenotypes and demonstrates regression-based ML models can predict λmax and identify functionally critical sites.

Differential expression of single-cell RNA-seq data using Tweedie models

The paper introduces Tweedie models for differential expression analysis in scRNA-seq, showing improved performance over existing methods across platforms.

High-sensitivity pattern discovery in large, paired multi-omic datasets

This paper introduces HAllA, a method for discovering associations in multi-omic datasets, showing it outperforms existing approaches and identifies new biological relationships.

netome: a computational framework for metabolite profiling and omics network analysis

netome provides tools for analyzing LC-MS metabolomics data, including quality control, peak alignment, and network-based statistical testing.