BNFO 615: Data analysis for bioinformatics
Fall 2014

Instructor: Usman Roshan
Office: GITC 3802
Ph: 973-596-2872
Office hours: Tue 4-5:30, Wed 2-5
Email: usman@cs.njit.edu

Course Overview: Students will study and present bioinformatics papers and work on a bioinformatics project that makes extensive use of R or Python. Towards the end of the course students will make a class presentation of their project.

Course plan:

Topic	Date	Notes
Slides on R courtesy of Zhi Wei		Bio background Intro to R Probability Data exploration More R
Slides on Python		Introduction to Python
Papers		Whole genome alignment assessment Alignathon: A competitive assessment of whole genome alignment methods SVM sequence alignment Structural SVM for Protein Sequence Alignment Species tree from whole genome Whole genome prokaryotic phylogeny Whole-Genome-Based Phylogeny and Divergence of the Genus Brucella Construction of a Phylogenetic Tree of Photosynthetic Prokaryotes Based on Average Similarities of Whole Genome Sequences Phylogeny of Bacterial and Archaeal Genomes Using Conserved Genes: Supertrees and Supermatrices Deep learning/Representation learning Representation Learning: A Review and New Perspectives Learning Feature Representations with K-means Analysis of single-layer networks in unsupervised feature learning Building high-level features using large-scale unsupervised learning Using deep learning to enhance cancer diagnosis and classification Deep learning of the tissue-regulated splicing code Gene expression The l1-l2 regularization framework unmasks the hypoxia signature hidden in the transcriptome of a set of heterogeneous neuroblastoma cell lines Detecting Stable Clusters Using Principal Component Analysis Gene selection for microarray data analysis using principal component analysis Techniques for clustering gene expression data Other Empirical performance of drug sensitivity prediction algorithms
Projects		Deep learning for case and control prediction, Anna Evaluation of whole genome alignment, Nandini Species tree from whole genome data, Ranganathan Classification and ranking of pairwise sequence alignments, Turki and Nikita Clustering and visualizing expression data with L1 vs L2 PCA, Nidhi Boosting Gradient boosting
Project resources		Python sci-kit library Deep learning in Python Train and test pairwise alignments
Project proposals		Nikeeta Ana Ranganathan Nandini Nidhi Turki
Python		Programming assignment Python programming assignment NW part1 NW part2