Imran S. Haque

This paper builds and benchmarks perturbative maps of transcriptional and morphological data, demonstrating their utility for discovering new biology and highlighting the importance of standardized frameworks.

The paper identifies a proximity bias in CRISPR-Cas9 knockouts, showing phenotypic similarity between unrelated genes on the same chromosome arm, and proposes a correction method.

RxRx3 provides a large-scale high-content screening dataset combining CRISPR knockouts and small molecule treatments to enable machine learning research and biological discovery.

This study analyzes genetic ancestry in 93,419 individuals to show that ethnicity-based carrier-screening guidelines are incomplete and miss carriers in at-risk populations.

In an image-based screen of 1,670 compounds in SARS-CoV-2-infected human renal epithelial cells, remdesivir was the only antiviral with strong efficacy; chloroquine and hydroxychloroquine showed no benefit.

This paper validates an expanded carrier screen with high sensitivity and specificity, showing significant improvements in detecting fetal disease risk through CNV calling and challenging conditions.

This paper reanalyzes ctDNA data to show that early cancer detection via ctDNA mutation detection faces technical and economic challenges, suggesting the need for integrated multi-analyte approaches.

The paper examines the frequencies of recessive lethal mutations in human populations and finds they are higher than expected, suggesting ascertainment bias rather than mutation-selection balance.

The paper evaluates methods for designing expanded carrier screening panels and finds that full-exon sequencing outperforms targeted genotyping in detecting affected fetuses.

This study compares whole-genome sequencing and SNP methods for noninvasive prenatal screening at low fetal fractions, finding WGS detects more aneuploidies and avoids unnecessary invasive procedures.

The paper develops and validates a 36-gene sequencing assay for hereditary cancer risk assessment, showing 100% sensitivity and high specificity across mutation types.

The paper estimates SLOS carrier frequencies and in utero mortality rates by analyzing carrier screening data and comparing predicted to observed birth incidences.

SSCM-Pathogenic is a genome-wide, allele-specific score that predicts variant pathogenicity using a semi-supervised clustering algorithm, showing effectiveness in both coding and noncoding regions.

The paper introduces two optimized matrix multiply routines for calculating protein RMSD, achieving up to 3X speed improvement over existing libraries.