Biology subjects

Farooq, M.

Publications and source records attributed to Farooq, M..

5 recordsLinked to original sources

bioRxiv2026

A multitask single-cell analysis framework with knowledge graph as a prior

Single-cell RNA sequencing has reshaped cancer biology by revealing the cellular and molecular diversity of the tumor microenvironment, yet most analysis pipelines still treat clustering, annotation, pathway scoring, metabolic inference, and cell-cell communication as separate tasks. Here, we present scKNIFE (Knowledge graph-based NMF for Inference of Functional Entities), a unified graph-regularized non-negative matrix factorization framework that jointly reconstructs single-cell expression profiles and infers activities of diverse biological entities, including pathways, cell-type markers, metabolic reactions and tasks, ligand-receptor interactions, and cell-state programs. By embedding a heterogeneous knowledge graph directly into the factorization objective, scKNIFE propagates information between biologically related entities through Laplacian regularization while sparsity constraints preserve interpretability. The integrated prior graph spans 47,274 nodes and 506,620 edges, assembled from complementary resources including Gene Ontology, Reactome, Hallmark gene sets, Human-GEM, CellChat, PanglaoDB, Cytopus, and Tabula Muris-derived annotations. Across multiple cancer-focused single-cell datasets, scKNIFE yields competitive to leading performance for clustering and cell-type annotation, while also recovering biologically coherent metabolic programs that agree with dedicated metabolism-focused methods and capture therapy-response-associated states. In addition, the framework supports downstream inference of cell-type-specific biological activities from a single latent representation. Together, these results establish scKNIFE as a modular and extensible framework for end-to-end biological interpretation of single-cell cancer transcriptomics

cancer biology↗

bioRxiv2026

Deterministic retrieval recovers biomedical associations lost by language models

Biomedical research increasingly relies on expert-curated databases to connect diseases, genes, variants, phenotypes, pathways and therapeutics. Incomplete or irreproducible retrieval can distort interpretation, mechanistic inference, variant assessment or therapeutic prioritization despite an unchanged evidence base. Agentic natural-language-to-SQL (NL2SQL) systems and Model Context Protocol-enabled agents can autonomously retrieve evidence from biomedical databases, including Open Targets and the Highly Confident Drug-Target Database, in response to natural-language questions. However, they may truncate large result sets, miss records when terminology differs from database vocabulary and return different outputs across runs. We systematically quantify these failures and introduce BioChirp, a goal-directed autonomous system for reliable retrieval from structured biomedical databases, built on interpretation-execution separation. A coordinated language-model layer interprets intent and resolves database-field assignments, after which entity resolution maps user terms to database terminology. A deterministic Steiner-tree planner and executor then retrieve matching records without further language-model involvement. Across curated biomedical databases spanning more than one million associations, BioChirp retrieved thousands of records for exhaustive queries, whereas baseline agents returned at most a few hundred or failed. Across 70 queries, five runs and three BioChirp database backends, BioChirp achieved a median cross-run Jaccard similarity of 1.0. Across 910 database-question evaluations derived from expert-written BioASQ questions and spanning ten databases, BioChirp retrieved at least one database record in 85.4% of cases and produced a fully correct answer in 51.1% of cases, compared with 44.6% and 17.4% for agentic NL2SQL. BioChirp is publicly available at https://biochirp.iiitd.edu.in.

bioinformatics↗

bioRxiv2023

Comparative Analysis, Diversification and Functional Validation of Plant Nucleotide-Binding Site Domain Genes

Nucleotide-binding site (NBS) domain genes are one of a superfamily of resistance genes involved in plant responses to pathogens. The current study identified presumably identified 12,820 NBS-containing genes across 34 species covering from mosses to monocots and dicots. These identified genes classified into 168 classes with several novel domain architectures patterns encompassing significant diversity among plant species. Several classical (NBS, NBS-LRR, TIR-NBS, TIR-NBS-LRR etc.) and species-specific structural patterns (TIR-NBS-TIR-Cupin_1-Cupin_1, TIR-NBS-Prenyltransf, Sugar_tr-NBS etc.) were discovered. We observed 603 orthogroups (OGs) with some core (most common orthogroups; OG0, OG1, OG2 etc.) and unique (highly specific to species; OG80, OG82 etc) OGs with tandem duplications. The expression profiling presented the putative upregulation of OG2, OG6, and OG15 in different tissues under various biotic and abiotic stresses in susceptible and tolerant plants to CLCuD. The genetic variation between susceptible (Coker 312) and tolerant (Mac7) G. hirsutum accessions identified several unique variants in NBS genes of Mac7 (6,583 varaints) and Coker312 (5,173 variants). The protein-ligand and proteins-protein interaction showed a strong interaction of some putative NBS proteins with ADP/ATP and different core proteins of cotton leaf curl disease virus. The silencing of GaNBS (OG2) in resistant cotton through virus-induced gene silencing (VIGS) demonstrated its putative role in virus tittering. The presented study will be further helpful to understand the plant adaptation mechanism.

plant biology↗

bioRxiv2019

RRP7A links primary microcephaly to radial glial cells and dysfunction of ribosomal biogenesis, neurogenesis and ciliary resorption

Introductory paragraphPrimary microcephaly (MCPH) is characterized by reduced brain size and intellectual disability1. The exact pathophysiological mechanism underlying MCPH remains to be elucidated, but dysfunction of neuronal progenitors in the developing neocortex plays a major role1. Using homozygosity mapping and whole exome sequencing, we identified a homozygous missense mutation (p.W155C) in Ribosomal RNA Processing 7 Homolog A, RRP7A, which segregated with MCPH in a consanguineous family with 10 affected individuals. RRP7A is expressed in neural stem cells/radial glial cells of the developing human forebrain, and targeted mutation of Rrp7a leads to defects in both neurogenesis and proliferation in a mouse stem cell model. RRP7A localizes to centrosomes, cilia and nucleoli, and patient-derived fibroblasts display defects in processing of ribosomal RNA, resorption of primary cilia and cell cycle progression. Finally, analysis of zebrafish embryos with loss-of-function mutation in rrp7a confirmed that RRP7A depletion causes reduced brain size, impaired neurogenesis and cell proliferation as well as defective ribosomal RNA processing. These findings provide novel insight into human brain development and MCPH.

neuroscience↗

bioRxiv2018

Detection of residual disease after neoadjuvant therapy in breast cancer using personalized circulating tumor DNA analysis

Accurate detection of minimal residual disease (MRD) can guide individualized management of early stage cancer patients, but current diagnostic approaches lack adequate sensitivity. Circulating tumor DNA (ctDNA) analysis has shown promise for recurrence monitoring but MRD detection immediately after neoadjuvant therapy or surgical resection has remained challenging. We have developed TARgeted DIgital Sequencing (TARDIS) to simultaneously analyze multiple patient-specific cancer mutations in plasma and improve sensitivity for minute quantities of residual tumor DNA. In 77 reference samples at 0.03%-1% mutant allele fraction (AF), we observed 93.5% sensitivity. Using TARDIS, we analyzed ctDNA in 34 samples from 13 patients with stage II/III breast cancer treated with neoadjuvant therapy. Prior to treatment, we detected ctDNA in 12/12 patients at 0.002%-1.04% AF (0.040% median). After completion of neoadjuvant therapy, we detected ctDNA in 7/8 patients with residual disease observed at surgery and in 1/5 patients with pathological complete response (odds ratio, 18.5, Fishers exact p=0.032). These results demonstrate high accuracy for a personalized blood test to detect residual disease after neoadjuvant therapy. With additional clinical validation, TARDIS could identify patients with molecular complete response after neoadjuvant therapy who may be candidates for nonoperative management.\n\nOne Sentence SummaryA personalized ctDNA test achieves high accuracy for residual disease.

genomics↗