Anjali Dhall

SCLC TumorMiner is a genomics platform that provides molecular classification, biomarker identification, and drug-response analysis for small cell lung cancer using integrated datasets from multiple institutions.

This study evaluates TR-107, a mitochondrial ClpP agonist, showing antitumor effects in ACC cell lines and organoids, with synergistic activity when combined with IGF-1R inhibitors.

This study identifies key cellular programs and pathways driving metastatic organotropism in small cell lung cancer using preclinical models and multiomic profiling.

This paper identifies DLK1 as a novel immunotherapeutic target in adrenocortical carcinoma, showing it regulates tumor cell plasticity and chemoresistance, and demonstrates the efficacy of a DLK1-targeting antibody-drug conjugate.

This paper identifies SEMA7A as a prognostic biomarker in adrenocortical carcinoma using transcriptome data from TCGA and NCI samples, showing its association with poor prognosis and specific signaling pathways.

This study shows that LMNA deficiency in SCLC increases R-loop accumulation, leading to genomic instability and poor patient survival.

This study shows that Top3b loss accelerates lymphoma development and causes immune dysregulation in mice through R-loop accumulation and immune signaling activation.

The paper develops a hybrid method to predict IFN-gamma inducing peptides in human and mouse, achieving higher AUROC scores than existing methods.

This paper introduces HLA-DR4Pred2, an improved method for predicting HLA-DRB1*04:01 binders using a larger dataset and hybrid approaches, achieving higher AUC scores than existing methods.

This study develops a hybrid method combining alignment-based and alignment-free approaches to predict transcription factors with high accuracy, achieving an AUC of 0.99 on an independent dataset.

The paper maps genome-wide topoisomerase binding sites to identify TOP3A as a sensor of transcription-replication conflicts.

This paper develops a deep learning model using single-cell transcriptomics to classify HNSCC and distinguish HPV+ from HPV- patients, achieving high AUROC scores on a validation dataset.

This paper introduces AntiBP3, a hybrid method for predicting antibacterial peptides effective against gram-positive, gram-negative, and gram-variable bacteria, achieving high AUC scores on independent datasets.

This study develops a deep learning model to predict Alzheimer's disease using single-cell transcriptomics data, achieving an AUC of 0.75-0.84 on validation datasets.

This paper develops computational tools to predict celiac disease-associated epitopes and motifs in proteins, achieving high accuracy using machine learning and motif-based approaches.

The paper introduces Pprint2, a method using convolutional neural networks and evolutionary profiles to predict RNA-interacting residues in proteins, achieving an AUC of 0.82.

This paper develops in-silico models to predict infectious Influenza A virus strains from genome and protein sequences, achieving high AUC scores and providing a web tool for pandemic preparedness.

The paper introduces a software package that computes over 50,000 features for protein/peptide function prediction, including residue-level annotations.

DBpred predicts DNA-interacting residues in proteins using deep learning, achieving improved AUROC scores compared to existing methods.

This paper develops models to predict, scan, and design TNF-α inducing epitopes for human and mouse, achieving AUROC values up to 0.83 for human and 0.77 for mouse.

This paper develops methods to predict and design T1DM-associated peptides, achieving high accuracy with a hybrid approach and a webserver for peptide prediction.

This paper develops an ensemble model to predict phage-host interactions for phage therapy, achieving higher accuracy than existing methods across multiple taxonomic levels.

The paper develops HLAncPred, an AI-based method to predict non-classical HLA (HLA-G and HLA-E) binders, achieving high AUC scores and providing a web server for this purpose.

This paper introduces IL-13Pred, a method to predict IL-13 inducing peptides in proteins, with high accuracy for managing COVID-19 severity.

The paper benchmarks mutation calling techniques for liver cancer prognosis using VCF and MAF files, finding MuTect2's VCF files best predict high-risk patients with high AUROC and HR.

Nfeature computes a large number of features for DNA and RNA sequences, providing a standalone package and web platform for researchers.

The paper organizes a DREAM challenge to benchmark predictive models of single-cell signaling using mass cytometry data from breast cancer cell lines.

This paper identifies 15 gene signatures for predicting lymph node metastasis in TNBC using TCGA data, but finds poor cross-platform validation and develops a webserver for prediction.

This paper develops a machine learning model to predict ceftazidime-resistant bacterial strains from beta-lactamase amino acid sequences, achieving AUROC of 0.80 on training data.

SAPdb is a database of experimentally validated peptide nanostructures formed by self-assembly, providing information on chemical modifications, nanostructure types, and experimental conditions.