Danilo Sipoli Sanches

biomapp::chip: Large-Scale Motif Analysis

The paper introduces a tool for optimizing biological motif discovery in large datasets, showing improved performance and accuracy over existing methods in ChIP-seq analysis.

Simulation of 69 microbial communities indicates sequencing depth and false positives are major drivers of bias in Prokaryotic metagenome-assembled genome recovery

The paper simulates microbial communities to show sequencing depth and false positives significantly affect metagenome-assembled genome recovery accuracy.

Discovering motifs and genomic patterns with SMT: a high-performance data structure for counting kmers

The paper introduces SMT, a data structure for efficient kmer counting and motif discovery in genomic data, showing its effectiveness in handling noisy datasets and large-scale analysis.

Feature Extraction Approaches for Biological Sequences: A Comparative Study of Mathematical Models

This paper compares mathematical models for feature extraction from biological sequences, showing their effectiveness in classifying Long Non-Coding RNA and other sequence types.

MathFeature: Feature Extraction Package for Biological Sequences Based on Mathematical Descriptors

MathFeature provides a package for extracting mathematical descriptors from biological sequences to improve machine learning performance in bioinformatics.