Miguel A. Hernández Betancourt

Miguel A. Hernández Betancourt

Bio:

Hello, my name is Miguel Angel Hernández Betancourt, I'm an undergradate student undergoing a double major in Biology and Computer Science.

Weekly Updates

Semester: August-December, 2019

Week 1 :

  • Finally i enrolled into the research course.
  • I'm less confused about about the inner workings of research.
  • Went to the first lab meeting.
  • I will start reading papers.

Week 2 :

  • Of three assigned papers I only read one. Will read the other two on the weekend
  • Still don't know what project to join or do.
  • Also currently searching for a paper.
  • Weekly update over.

Week 3 :

  • Went to the lab meeting.
  • Couldn't do anything else do to personal matters.
  • Will try to catch on for next week.

Week 4 :

  • There was no lab meeting.
  • I finished reading week 2's assigned papers.

Week 5 :

  • Went to lab meeting.
  • Haven't done anything lab related since I have a Lineal Algebra exam tuesday 17. Help me.
  • Still need to find a paper. Will work on that after tuesday.

Week 6 :

  • I read the paper "UNSUPERVISED FEATURE CONSTRUCTION AND KNOWLEDGE EXTRACTION FROM GENOME-WIDE ASSAYS OF BREAST CANCER WITH DENOISING AUTOENCODERS".
  • Will read similar papers.

Week 7 :

  • I gave a presentation about Denoisining Autoencoders.
  • Humberto assigned me to replicate the experiment.
  • Next week I've got two examens.

Week 8 :

  • Didn't do much do to having two examens this week.
  • Found some articles about autoencoders.

Week 9 :

  • I started watching youtube videos about neural networks and how they work.
  • Found a video about Denoising autoencoders.
  • There was no lab meeting this week.

Week 10 :

  • Went to lab meeting, Brikinie presented seq-seq pan.
  • Will start looking into the autoencoder project.
  • Got tons of work to do.

Week 11 - 14:

  • Searched more information on neural networks and autoencoders and how they work and function.
  • Tried to download the programs needed for Theano. But had some trouble downloading the intstaller anaconda. It took quite more time that I anticipated but managed to download the programs.
  • Had quite a lot of work and exams.

Week 15 :

  • No lab meeting do to thanksgiving weekend

Semester: January-May, 2020

Week 1: (January 27 - February 1)

  • Humberto greet us with the wonderful news we were going to SiDIM :'( .
  • Notified us that we had until February 15 to summit abstracts.

Week 2: (February 2 - 8)

  • Started writing the abstract with Roberto

Week 3: (February 9 - 15)

  • There was no lab meeting today.
  • Finished writing the abstract. Humberto approved.
  • Started setting up my PC with theano and related programs for the autoencoders.

Week 4: (February 16 - 22)

  • There was no lab meeting this week do to a two day machine learning in bioinformatics workshop.
  • I attended the workshop, althought I missed the first one and a half hour of the workshop do to a conflicting squedule with immunology class. In the workshop they disccused and work with:
  • Random Forests
  • Support Vector Machines
  • Naive Beyes
  • Cross validation
  • Make synthetic datasets

Week 5: (February 23 - 29)

  • Couldn't work much on the project do to Inmunology exam and next week's supirior algebra exam.

Week 6: (March 1 - 7)

  • Found a website with code for a denoising autoencoder using theano library. But there is no time to add it to the poster.
  • After SiDIM:
  • Instead of focusing on trying to use the theano library I will open up to the Keras and Tenserflow library since they are more widely use and there are more tutorials and code using this libraries ( I know since I have have found more of these on my search for theano tutorials and code for denoising autoencoders).
  • I will start documenting better the articles I have read and used to understand and get into the field of neural networks and autoencoders.

Semester: January-May, 2026

Overview:

Filler

Week 1: January 14 - 16

  • Start of new semester

Week 2: Junuary 19 - 23

  • Reorganized the lab.
  • Was given the papers:
  • Single-cell biological network inference using heterogeneous graph transformers.
  • Scanpy: large-scale single-cell gene expresion data analysis.

Week 3: January 26 - 30

  • Read the papers from week 2.
  • Found the papers:
  • Epigenetics: The Science of Change (https://pmc.ncbi.nlm.nih.gov/articles/PMC1392256/)
  • The Changing Concept of Epigenetics. (https://nyaspubs.onlinelibrary.wiley.com/doi/full/10.1111/j.1749-6632.2002.tb04913.x?casa_token=O-3Qko62Wr8AAAAA%3AIBarO4wOs6VJ1-EtKveCwL4TNNM4FQJxh1ZN0i52F84DY5rt_2PAXfmvIgTgtfX5ihDI2vw85pe4Fi8)
  • Single-cell sequencing to multi-omics: technologies and applications (https://link.springer.com/article/10.1186/s40364-024-00643-4)
  • Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer (https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2020.588221/full)
  • Revolutionizing multi-omics analysis with artificial intelligence and data processing (https://onlinelibrary.wiley.com/doi/full/10.1002/qub2.70002)

Week 4: February 2 - 6

  • Read the Epigenetics papers, which helped me understand the scope and focus of the field.
  • Read Accelerating single-cell genomic analysis with gpu's paper.

Week 5: February 9 - 13

  • Created a Notebooklm entry with all the papers that I read so far.
  • Found and read the paper "Single-cell sequencing to multi-omics: technologies and applications." (https://link.springer.com/article/10.1186/s40364-024-00643-4). Really good paper that introduces multi-omics, what it encompases, the type of data that is used and analysed, the

Week 6: February 16 - 20

  • Got Assigned the paper DeepMAPS
  • Started working on technical report for DeepMAPS
  • Went to HPCF

Week 7: 23 - 27

  • Finished the technical report.
  • Technical report presentation

Week 8: 2 - 6

  • Did not gave the presentation, did not assist to the unversity this week

Week 9: 9 - 13

  • Worked to get up to date with university homework
  • Will look into online courses for machine learning, AI, etc. to get myself more familiarized with a more hands on aproach for this project. I have an O'Reilly book for machine learning using Scikit-Learn, Keras and TensorFlow. Gonna start there.
  • Will look into graph transformers vs standard statical models vs convolutional neural networks.
  • Will get more hands on with DeepMAPS