{'fraction_non_alphanumeric': 0.01927860696517413, 'fraction_numerical': 0.009950248756218905, 'mean_word_length': 5.359683794466403, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Dots Witer: Prediction of Potential Cancer Driver Genes Using Hybrid Approach

Identifying cancer driver genes remains of great significance since it assists in increasing the survival rate by defining cohesive treatments at early stages. There are several approaches available for driver gene prediction, but methodical ways to combine and optimize the existing algorithms for more massive datasets are few. This approach formulates an effective method to identify potential cancer driver genes by identifying the drawbacks of existing identification methods of cancer driver genes. The newly proposed hybrid pipeline (Dots Witer) summarizes the somatic mutations and genes involved in tumorigenesis. The pan-cancer dataset consists of 2397 small somatic variants related to Breast Invasive Carcinoma (BRCA), and a 1017 data sample of Lung Adenocarcinoma (LUAD) has been selected as a dataset for testing the proposed approach. The Dots Witer pipeline can be applied to genes that are targeted by Single Nucleotide Variants (SNVs) and small insertions and/or deletions (indels). The Dots Witer pipeline has been implemented by integrating DOTS-Finder and WITER to identify the driver genes efficiently and accurately. The proposed pipeline identified 65 cancer progression genes, and among those, 32 were tested and confirmed by Cancer Gene Census (CGC) related to BRCA. Moreover, the Dots Witer identified 43 cancer progression genes and 29 were verified and confirmed by CGC related to LUAD. The Dot Witer pipeline gives a higher fraction of predicted driver gene set compared to other existing pipelines.