Analysis of Mutations in Precision Oncology using The Automated, Accurate, and User-Friendly Web Tool PredictONCO

RT Khan, P Pokorna, J Stourac, S Borko, A Dobias… - bioRxiv, 2024 - biorxiv.org
Next-generation sequencing technology has created many new opportunities for clinical
diagnostics, but it faces the challenge of functional annotation of identified mutations …

A computational workflow for analysis of missense mutations in precision oncology

RT Khan, P Pokorna, J Stourac, S Borko… - Journal of …, 2024 - Springer
Every year, more than 19 million cancer cases are diagnosed, and this number continues to
increase annually. Since standard treatment options have varying success rates for different …

BayesPI-BAR2: a new python package for predicting functional non-coding mutations in cancer patient cohorts

K Batmanov, J Delabie, J Wang - Frontiers in genetics, 2019 - frontiersin.org
Most of somatic mutations in cancer occur outside of gene coding regions. These mutations
may disrupt the gene regulation by affecting protein-DNA interaction. A study of these …

Applying expression profile similarity for discovery of patient-specific functional mutations

G Meng - High-throughput, 2018 - mdpi.com
The progress of cancer genome sequencing projects yields unprecedented information of
mutations for numerous patients. However, the complexity of mutation profiles of cancer …

PredictONCO: a web tool supporting decision-making in precision oncology by extending the bioinformatics predictions with advanced computing and machine …

J Stourac, S Borko, RT Khan, P Pokorna… - Briefings in …, 2024 - academic.oup.com
PredictONCO 1.0 is a unique web server that analyzes effects of mutations on proteins
frequently altered in various cancer types. The server can assess the impact of mutations on …

Combining evolution and protein language models for an interpretable cancer driver mutation prediction with D2Deep

K Tzavella, A Diaz, C Olsen, WF Vranken - bioRxiv, 2023 - biorxiv.org
Background: The mutations driving cancer are being increasingly exposed through tumor-
specific genomic data. However, differentiating between cancer-causing driver mutations …

Computational structure prediction methods enable the systematic identification of oncogenic mutations

X Fu, C Reglero, V Swamy, JW Loh, H Khiabanian… - bioRxiv, 2022 - biorxiv.org
Oncogenic mutations are associated with the activation of key pathways necessary for the
initiation, progression and treatment-evasion of tumors. While large genomic studies provide …

CancerVar: An artificial intelligence–empowered platform for clinical interpretation of somatic mutations in cancer

Q Li, Z Ren, K Cao, MM Li, K Wang, Y Zhou - Science advances, 2022 - science.org
Several knowledgebases are manually curated to support clinical interpretations of
thousands of hotspot somatic mutations in cancer. However, discrepancies or even …

Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks

M Nourbakhsh, K Degn, A Saksager… - Briefings in …, 2024 - academic.oup.com
The vast amount of available sequencing data allows the scientific community to explore
different genetic alterations that may drive cancer or favor cancer progression. Software …

Comprehensive assessment of computational algorithms in predicting cancer driver mutations

H Chen, J Li, Y Wang, PKS Ng, YH Tsang, KR Shaw… - Genome biology, 2020 - Springer
Background The initiation and subsequent evolution of cancer are largely driven by a
relatively small number of somatic mutations with critical functional impacts, so-called driver …